Halittu · hankula da hankula masu yiwuwa, an jera su bisa shaida
Idan duniya kwaikwayo ce, hankula na iya zama wasu daga abubuwa mafi wahalar lissafa a cikinta. Wannan jagora yana jera lamuran da aka sani, daga ƙwayoyin halitta ɗaiɗai zuwa mutane. Yana kuma ƙunshe da tsarin wucin gadi da aka gina zuwa yanzu, da hankula da masana kimiyya da falsafa suka ba da shawara amma babu wanda ya lura da su.
Kowace lamba tana da tushe da alamar amincewa. an auna ƙidaya ce kai tsaye, kiyasi an lissafa ta daga wasu bayanai, kuma ba a ƙirga ba yana nufin babu wanda ya buga ƙidaya. Ko ɗaya daga cikin waɗannan tsarin yana da ƙwarewa tambaya ce daban. Shaidar hakan tana ƙarƙashin ma’aunan iya ji da alamomin sani.
Hankula ɗaiɗai
- Orca †Biliyan 43.1
- Long-finned pilot whale †Biliyan 37.2
- Ɗan Adam▲1Biliyan 16.3
- Harbour porpoise †Biliyan 14.9
- Dolfin bottlenose †Biliyan 12.7
- Cuvier’s beaked whaleBiliyan 9.1
- GorillaBiliyan 8.5
- OrangutanBiliyan 8.5
- ChimpanzeeBiliyan 7.4
- Dolfin na gama gariBiliyan 6.7
- Harp sealBiliyan 6.1
- Giwar Afirka▼1Biliyan 5.6
- Fin whaleBiliyan 5.2
- Blue whaleBiliyan 5.1
- Minke whaleBiliyan 3.2
10610710810910101011neurons
Nuna lambobin
| # | Dabba | Cortex ko pallium | Duka kwakwalwa |
|---|---|---|---|
| 1 | Orca † | Biliyan 43.1 | |
| 2 | Long-finned pilot whale † | Biliyan 37.2 | |
| 3 | Ɗan Adam | Biliyan 16.3 | Biliyan 86.1 |
| 4 | Harbour porpoise † | Biliyan 14.9 | |
| 5 | Dolfin bottlenose † | Biliyan 12.7 | |
| 6 | Cuvier’s beaked whale | Biliyan 9.1 | |
| 7 | Gorilla | Biliyan 8.5 | |
| 8 | Orangutan | Biliyan 8.5 | |
| 9 | Chimpanzee | Biliyan 7.4 | |
| 10 | Dolfin na gama gari | Biliyan 6.7 | |
| 11 | Harp seal | Biliyan 6.1 | |
| 12 | Giwar Afirka | Biliyan 5.6 | Biliyan 257 |
| 13 | Fin whale | Biliyan 5.2 | |
| 14 | Blue whale | Biliyan 5.1 | |
| 15 | Minke whale | Biliyan 3.2 | Biliyan 57.4 |
| 16 | Olive baboon | Biliyan 2.88 | Biliyan 10.95 |
| 17 | Beluga | Biliyan 2.7 | |
| 18 | Pygmy sperm whale | Biliyan 2.02 | |
| 19 | Macaw shuɗi da rawaya | Biliyan 1.92 | Biliyan 3.14 |
| 20 | Rhesus macaque | Biliyan 1.71 | Biliyan 6.39 |
| 21 | Raƙumin dawa | Biliyan 1.67 | Biliyan 11.21 |
| 22 | Squirrel monkey | Biliyan 1.34 | Biliyan 3.25 |
| 23 | Mujiyar gaggafa ta Eurasia | Biliyan 1.33 | Biliyan 1.9 |
| 24 | Kea | Biliyan 1.28 | Biliyan 2.15 |
| 25 | Mujiya ta dusar ƙanƙara | Biliyan 1.27 | Biliyan 1.81 |
| 26 | Hankaka na gama gari | Biliyan 1.2 | Biliyan 2.17 |
| 27 | Tawny owl | Biliyan 1.15 | Biliyan 1.58 |
| 28 | Biri capuchin | Biliyan 1.14 | Biliyan 3.69 |
| 29 | Sulphur-crested cockatoo | Biliyan 1.14 | Biliyan 2.12 |
| 30 | Aku launin toka na Afirka | Miliyan 850 | Biliyan 1.57 |
| 31 | Rook | Miliyan 820 | Biliyan 1.51 |
| 32 | Greater kudu | Miliyan 793.07 | Biliyan 6.09 |
| 33 | Mujiya mai dogayen kunnuwa | Miliyan 673 | Miliyan 991.31 |
| 34 | Goffin’s cockatoo | Miliyan 599 | Biliyan 1.16 |
| 35 | Zaki | Miliyan 545.24 | Biliyan 4.67 |
| 36 | White-tailed eagle | Miliyan 542.93 | Biliyan 1.65 |
| 37 | Eurasian jay | Miliyan 529 | Biliyan 1.09 |
| 38 | Kare na gida | Miliyan 526 | |
| 39 | Striped hyena | Miliyan 495.28 | Biliyan 3.88 |
| 40 | Western jackdaw | Miliyan 492 | Miliyan 967.99 |
| 41 | Jimina ta gama gari | Miliyan 479.41 | Biliyan 1.63 |
| 42 | Eurasian magpie | Miliyan 443 | Miliyan 897.27 |
| 43 | Saker falcon | Miliyan 442.95 | Biliyan 1.2 |
| 44 | Emu | Miliyan 439 | Biliyan 1.34 |
| 45 | Raccoon | Miliyan 437.94 | Biliyan 2.15 |
| 46 | Mujiya ta rumbu | Miliyan 437 | Miliyan 689.54 |
| 47 | Ƙaramar mujiya | Miliyan 400.82 | Miliyan 585.92 |
| 48 | Ungulu baƙa | Miliyan 398.9 | Biliyan 1.01 |
| 49 | Eurasian pygmy owl | Miliyan 364 | Miliyan 533.26 |
| 50 | Buzzard na gama gari | Miliyan 351.7 | Biliyan 1 |
| 51 | Greater rhea | Miliyan 330.34 | Biliyan 1.03 |
| 52 | Mute swan | Miliyan 323.66 | Biliyan 1 |
| 53 | Alade na gida | Miliyan 293 | Biliyan 2.22 |
| 54 | Greylag goose | Miliyan 258.65 | Miliyan 738.23 |
| 55 | Beyar launin ruwan ƙasa | Miliyan 250.97 | Biliyan 9.59 |
| 56 | Kyanwa ta gida | Miliyan 250 | Biliyan 1.22 |
| 57 | Marmoset na gama gari | Miliyan 245 | Miliyan 638 |
| 58 | Kestrel na gama gari | Miliyan 237.9 | Miliyan 624.58 |
| 59 | Eurasian teal | Miliyan 167.29 | Miliyan 375.41 |
| 60 | Budgerigar | Miliyan 149 | Miliyan 321.82 |
| 61 | Red-breasted goose | Miliyan 148.62 | Miliyan 482.71 |
| 62 | Wood duck | Miliyan 138.21 | Miliyan 305.82 |
| 63 | Pygmy falcon | Miliyan 131.9 | Miliyan 350.37 |
| 64 | Marbled duck | Miliyan 130.14 | Miliyan 386.92 |
| 65 | Indian peafowl | Miliyan 129.62 | Miliyan 570.93 |
| 66 | Yellow-knobbed curassow | Miliyan 124.62 | Miliyan 652.99 |
| 67 | Victoria crowned pigeon | Miliyan 118.45 | Miliyan 578.7 |
| 68 | Mallard | Miliyan 112.26 | Miliyan 367.47 |
| 69 | Turkey na daji | Miliyan 105.65 | Miliyan 492.87 |
| 70 | Eurasian sparrowhawk | Miliyan 87.83 | Miliyan 401.75 |
| 71 | Reeves’s pheasant | Miliyan 80.69 | Miliyan 387.17 |
| 72 | Kurciya ta dutse | Miliyan 72 | Miliyan 310 |
| 73 | Zomon Turai | Miliyan 71.5 | Miliyan 513 |
| 74 | Red junglefowl | Miliyan 60.7 | Miliyan 220.84 |
| 75 | Tinamou na Chile | Miliyan 59.13 | Miliyan 241.37 |
| 76 | Zebra finch | Miliyan 55.2 | Miliyan 135.98 |
| 77 | Tinamou mai kyau mai kambi | Miliyan 51.38 | Miliyan 217.77 |
| 78 | Kurciyar itace ta gama gari | Miliyan 51.33 | Miliyan 258.68 |
| 79 | Quebracho crested tinamou | Miliyan 48.29 | Miliyan 235.67 |
| 80 | Kurciyar wuya ta Afirka | Miliyan 40 | Miliyan 139.27 |
| 81 | Northern bobwhite | Miliyan 39.11 | Miliyan 148.4 |
| 82 | Grey partridge | Miliyan 36.88 | Miliyan 170.29 |
| 83 | Bera | Miliyan 31 | Miliyan 200 |
| 84 | Quail na gama gari | Miliyan 22.57 | Miliyan 117.76 |
| 85 | King quail | Miliyan 20.52 | Miliyan 80.48 |
| 86 | Kurciya mai lu’u-lu’u | Miliyan 18.21 | Miliyan 87.88 |
| 87 | Bera | Miliyan 13.7 | Miliyan 70.9 |
| 88 | Naked mole-rat | Miliyan 5.31 | Miliyan 33.62 |
- Neocortex na ɗan Adam1.6 × 10¹⁴
- GPT-5.5 (OpenAI, 2026) *~4.7 × 10¹²
- Claude Fable 5 (Anthropic, 2026) *~3.5 × 10¹²
- Grok 3 (xAI, 2025)~3 × 10¹²
- Grok 4 (xAI, 2025)~3 × 10¹²
- Gemini 2.5 Pro (Google, 2025) *~3 × 10¹²
- Kimi K3 (Moonshot, 2026)~2.8 × 10¹²
- Qwen 3.8 Max (Alibaba, 2026)2.4 × 10¹²
- Qwen3.8-2.4T-A95B (Alibaba, 2026)2.4 × 10¹²
- Grok 4.7 (xAI, 2026) *~2.1 × 10¹²
- Llama 4 Behemoth (Meta AI, 2025)~2 × 10¹²
- GPT-4 (OpenAI, 2023)~1.8 × 10¹²
- DeepSeek-V4-Pro (DeepSeek, 2026)~1.6 × 10¹²
- LongCat-2.0 (Meituan Inc, 2026)~1.6 × 10¹²
- Switch (Google, 2021)1.6 × 10¹²
- GLaM (Google, 2021)1.2 × 10¹²
- PanGu-Σ (Huawei Noah's Ark Lab, 2023)1.1 × 10¹²
- Kimi K2.5 (Moonshot, 2026)~1 × 10¹²
- Composer 2 (Cursor, 2026)1 × 10¹²
- Kimi K2.6 (Moonshot, 2026)1 × 10¹²
- Composer 2.5 (Cursor, 2026)~1 × 10¹²
- MiMo-V2-Pro (Xiaomi Corp, 2026)1 × 10¹²
- MiMo-V2.5-Pro (Xiaomi Corp, 2026)~1 × 10¹²
- M6-T (Alibaba, 2021)~1 × 10¹²
- Kimi K2 (Moonshot, 2025)1 × 10¹²
- Qwen3-Max (Alibaba, 2025)~1 × 10¹²
- Ling-1T (Ant Group, 2025)1 × 10¹²
- Kimi K2 Thinking (Moonshot, 2025)~1 × 10¹²
- Inkling (Thinking Machines, 2026)~9.8 × 10¹¹
- Tencent Hy4 preview (Tencent, 2026)~7.7 × 10¹¹
- GLM-5.1 (Z.ai (Zhipu AI), 2026)7.5 × 10¹¹
- K-EXAONE 2.0 (LG AI Research, 2026)7.5 × 10¹¹
- DeepSeek V4.1 Flash (DeepSeek, 2026)~7.5 × 10¹¹
- GLM-5 (Z.ai (Zhipu AI), 2026)~7.4 × 10¹¹
- GLM-5.3 (Z.ai (Zhipu AI), 2026)7.4 × 10¹¹
- A.X K2 (SK Telecom, 2026)6.9 × 10¹¹
- DeepSeekMath-V2 (DeepSeek, 2025)6.8 × 10¹¹
- Mistral Large 3 (Mistral AI, 2025)6.8 × 10¹¹
- DeepSeek-V3 (DeepSeek, 2024)6.7 × 10¹¹
- DeepSeek-R1 (DeepSeek, 2025)6.7 × 10¹¹
104106108101010121014parameters ko synapses
Nuna lambobin
| # | Tsari | Duk parameters | Ana amfani da shi ga kowane token | Shekara | Nau’i |
|---|---|---|---|---|---|
| 1 | Neocortex na ɗan Adam | 1.6 × 10¹⁴ | 1.6 × 10¹⁴ | synapses | |
| 2 | GPT-5.5 (OpenAI, 2026) * | ~4.7 × 10¹² | 2026 | kiyasi | |
| 3 | Claude Fable 5 (Anthropic, 2026) * | ~3.5 × 10¹² | 2026 | kiyasi | |
| 4 | Grok 3 (xAI, 2025) | ~3 × 10¹² | 2025 | kiyasi | |
| 5 | Grok 4 (xAI, 2025) | ~3 × 10¹² | 2025 | kiyasi | |
| 6 | Gemini 2.5 Pro (Google, 2025) * | ~3 × 10¹² | 2025 | kiyasi | |
| 7 | Kimi K3 (Moonshot, 2026) | ~2.8 × 10¹² | 2026 | kiyasi | |
| 8 | Qwen 3.8 Max (Alibaba, 2026) | 2.4 × 10¹² | 2.4 × 10¹² | 2026 | ba a bayyana tsari ba |
| 9 | Qwen3.8-2.4T-A95B (Alibaba, 2026) | 2.4 × 10¹² | 9.5 × 10¹⁰ | 2026 | gauraye na ƙwararru |
| 10 | Grok 4.7 (xAI, 2026) * | ~2.1 × 10¹² | 2026 | kiyasi | |
| 11 | Llama 4 Behemoth (Meta AI, 2025) | ~2 × 10¹² | 2025 | kiyasi | |
| 12 | GPT-4 (OpenAI, 2023) | ~1.8 × 10¹² | 2023 | kiyasi | |
| 13 | DeepSeek-V4-Pro (DeepSeek, 2026) | ~1.6 × 10¹² | 2026 | kiyasi | |
| 14 | LongCat-2.0 (Meituan Inc, 2026) | ~1.6 × 10¹² | 2026 | kiyasi | |
| 15 | Switch (Google, 2021) | 1.6 × 10¹² | 1.6 × 10¹² | 2021 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 16 | GLaM (Google, 2021) | 1.2 × 10¹² | 1.2 × 10¹² | 2021 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 17 | PanGu-Σ (Huawei Noah's Ark Lab, 2023) | 1.1 × 10¹² | 1.1 × 10¹² | 2023 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 18 | Kimi K2.5 (Moonshot, 2026) | ~1 × 10¹² | 2026 | kiyasi | |
| 19 | Composer 2 (Cursor, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 20 | Kimi K2.6 (Moonshot, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 21 | Composer 2.5 (Cursor, 2026) | ~1 × 10¹² | 2026 | kiyasi | |
| 22 | MiMo-V2-Pro (Xiaomi Corp, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | ba a bayyana tsari ba |
| 23 | MiMo-V2.5-Pro (Xiaomi Corp, 2026) | ~1 × 10¹² | 2026 | kiyasi | |
| 24 | M6-T (Alibaba, 2021) | ~1 × 10¹² | 2021 | kiyasi | |
| 25 | Kimi K2 (Moonshot, 2025) | 1 × 10¹² | 1 × 10¹² | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 26 | Qwen3-Max (Alibaba, 2025) | ~1 × 10¹² | 2025 | kiyasi | |
| 27 | Ling-1T (Ant Group, 2025) | 1 × 10¹² | 5 × 10¹⁰ | 2025 | gauraye na ƙwararru |
| 28 | Kimi K2 Thinking (Moonshot, 2025) | ~1 × 10¹² | 2025 | kiyasi | |
| 29 | Inkling (Thinking Machines, 2026) | ~9.8 × 10¹¹ | 2026 | kiyasi | |
| 30 | Tencent Hy4 preview (Tencent, 2026) | ~7.7 × 10¹¹ | 2026 | kiyasi | |
| 31 | GLM-5.1 (Z.ai (Zhipu AI), 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 32 | K-EXAONE 2.0 (LG AI Research, 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 33 | DeepSeek V4.1 Flash (DeepSeek, 2026) | ~7.5 × 10¹¹ | 2026 | kiyasi | |
| 34 | GLM-5 (Z.ai (Zhipu AI), 2026) | ~7.4 × 10¹¹ | 2026 | kiyasi | |
| 35 | GLM-5.3 (Z.ai (Zhipu AI), 2026) | 7.4 × 10¹¹ | 4 × 10¹⁰ | 2026 | gauraye na ƙwararru |
| 36 | A.X K2 (SK Telecom, 2026) | 6.9 × 10¹¹ | 6.9 × 10¹¹ | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 37 | DeepSeekMath-V2 (DeepSeek, 2025) | 6.8 × 10¹¹ | 6.8 × 10¹¹ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 38 | Mistral Large 3 (Mistral AI, 2025) | 6.8 × 10¹¹ | 4.1 × 10¹⁰ | 2025 | gauraye na ƙwararru |
| 39 | DeepSeek-V3 (DeepSeek, 2024) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2024 | gauraye na ƙwararru |
| 40 | DeepSeek-R1 (DeepSeek, 2025) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2025 | gauraye na ƙwararru |
| 41 | SenseChat 5.5 (SenseTime, 2024) | 6 × 10¹¹ | 6 × 10¹¹ | 2024 | ba a bayyana tsari ba |
| 42 | Step 5 Preview (StepFun, 2026) | ~6 × 10¹¹ | 2026 | kiyasi | |
| 43 | PaLM-E (Google, 2023) | ~5.6 × 10¹¹ | 2023 | kiyasi | |
| 44 | Hunyuan-TurboS (Tencent, 2025) | 5.6 × 10¹¹ | 5.6 × 10¹¹ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 45 | LongCat-Flash (Meituan Inc, 2025) | ~5.6 × 10¹¹ | 2025 | kiyasi | |
| 46 | Nemotron 3 Ultra (Nvidia, 2026) | ~5.5 × 10¹¹ | 2026 | kiyasi | |
| 47 | PaLM (Google Research, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense |
| 48 | Minerva (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense |
| 49 | U-PaLM (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense |
| 50 | LMSI-Palm (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense |
| 51 | Flan-PaLM 540B (Google, 2022) | 5.4 × 10¹¹ | 5.4 × 10¹¹ | 2022 | dense |
| 52 | Megatron-Turing NLG 530B (Microsoft, 2021) | 5.3 × 10¹¹ | 5.3 × 10¹¹ | 2021 | dense |
| 53 | MegaScale (ByteDance, 2024) | ~5.3 × 10¹¹ | 2024 | kiyasi | |
| 54 | A.X K1 (SK Telecom, 2025) | 5.2 × 10¹¹ | 5.2 × 10¹¹ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 55 | Doubao-pro (ByteDance, 2024) | ~5 × 10¹¹ | 2024 | kiyasi | |
| 56 | Grok 4.20 (xAI, 2026) | ~5 × 10¹¹ | 2026 | kiyasi | |
| 57 | Qwen3-Coder-480B-A35B (Alibaba, 2025) | 4.8 × 10¹¹ | 3.5 × 10¹⁰ | 2025 | gauraye na ƙwararru |
| 58 | MiniMax-M3 (MiniMax, 2026) | 4.3 × 10¹¹ | 2.2 × 10¹⁰ | 2026 | gauraye na ƙwararru |
| 59 | ERNIE-4.5-VL-424B-A47B (Baidu, 2025) | 4.2 × 10¹¹ | 4.7 × 10¹⁰ | 2025 | gauraye na ƙwararru |
| 60 | Llama 3.1-405B (Meta AI, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | dense |
| 61 | Llama 4 Maverick (Meta AI, 2025) | ~4 × 10¹¹ | 2025 | kiyasi | |
| 62 | Jamba 1.5-Large (AI21 Labs, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 63 | Qwen3.5 397B-A17B (Alibaba, 2026) | ~4 × 10¹¹ | 2026 | kiyasi | |
| 64 | Hunyuan-Large (Tencent, 2024) | 3.9 × 10¹¹ | 3.9 × 10¹¹ | 2024 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 65 | GLM-4.7 (Z.ai (Zhipu AI), 2025) | ~3.6 × 10¹¹ | 2025 | kiyasi | |
| 66 | GLM-4.6 (Z.ai (Zhipu AI), 2025) | ~3.6 × 10¹¹ | 2025 | kiyasi | |
| 67 | GLM-4.5 (Z.ai (Zhipu AI), 2025) | 3.5 × 10¹¹ | 3.2 × 10¹⁰ | 2025 | gauraye na ƙwararru |
| 68 | PaLM 2 (Google, 2023) | ~3.4 × 10¹¹ | 2023 | kiyasi | |
| 69 | Med-PaLM 2 (Google Research, 2023) | ~3.4 × 10¹¹ | 2023 | kiyasi | |
| 70 | Nemotron-4 340B (Nvidia, 2024) | 3.4 × 10¹¹ | 3.4 × 10¹¹ | 2024 | dense |
| 71 | GLM-5.3-Flash (Z.ai (Zhipu AI), 2026) | ~3.2 × 10¹¹ | 2026 | kiyasi | |
| 72 | Grok-1 (xAI, 2023) | ~3.1 × 10¹¹ | 2023 | kiyasi | |
| 73 | Motif-3 (Motif Technologies, 2026) | 3.1 × 10¹¹ | 3.1 × 10¹¹ | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 74 | SB-LM (Google, 2007) | ~3 × 10¹¹ | 2007 | kiyasi | |
| 75 | Tencent Hy3 preview (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 76 | Tencent Hy3 (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 77 | DeepSeek-V4-Flash (DeepSeek, 2026) | ~2.8 × 10¹¹ | 2026 | kiyasi | |
| 78 | Gopher (DeepMind, 2021) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2021 | dense |
| 79 | TML-Interaction-Small (Thinking Machines, 2026) | 2.8 × 10¹¹ | 2.8 × 10¹¹ | 2026 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 80 | ST-MoE (Google, 2022) | ~2.7 × 10¹¹ | 2022 | kiyasi | |
| 81 | ERNIE 3.0 Titan (Baidu, 2021) | 2.6 × 10¹¹ | 2.6 × 10¹¹ | 2021 | dense |
| 82 | Solar Open2 250B (Upstage, 2026) | ~2.5 × 10¹¹ | 2026 | kiyasi | |
| 83 | Aramco Metabrain AI (Saudi Aramco, 2024) | ~2.5 × 10¹¹ | 2024 | kiyasi | |
| 84 | Yuan 1.0 (Inspur, 2021) | 2.5 × 10¹¹ | 2.5 × 10¹¹ | 2021 | ba a bayyana tsari ba |
| 85 | DeepSeek-Coder-V2 236B (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 86 | DeepSeek-V2.5 (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 87 | K-EXAONE (LG AI Research, 2025) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2025 | ba a bayyana tsari ba |
| 88 | Qwen3-235B-A22B (Alibaba, 2025) | ~2.4 × 10¹¹ | 2025 | kiyasi | |
| 89 | Qwen3-235B-A22B-Thinking (Alibaba, 2025) | ~2.4 × 10¹¹ | 2025 | kiyasi | |
| 90 | P1-235B-A22B (Shanghai AI Lab, 2025) | ~2.4 × 10¹¹ | 2025 | kiyasi | |
| 91 | Seed-1.6-Thinking (ByteDance, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 92 | MiniMax-M2 (MiniMax, 2025) | 2.3 × 10¹¹ | 1 × 10¹⁰ | 2025 | gauraye na ƙwararru |
| 93 | MiniMax-M2.1 (MiniMax, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 94 | HyperCLOVA 204B (NAVER, 2021) | ~2 × 10¹¹ | 2021 | kiyasi | |
| 95 | Amazon Titan (Amazon, 2023) | ~2 × 10¹¹ | 2023 | kiyasi | |
| 96 | Falcon-180B (Technology Innovation Institute, 2023) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2023 | dense |
| 97 | Jurassic-1-Jumbo (AI21 Labs, 2021) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2021 | ba a bayyana tsari ba |
| 98 | BLOOM-176B (Hugging Face, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | dense |
| 99 | BLUUMI (University of Turku, 2023) | ~1.8 × 10¹¹ | 2023 | kiyasi | |
| 100 | InstructGPT 175B (OpenAI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | dense |
| 101 | OPT-175B (Meta AI, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | dense |
| 102 | BlenderBot 3 (McGill University, 2022) | ~1.8 × 10¹¹ | 2022 | kiyasi | |
| 103 | GPT-3 175B (OpenAI, 2020) | 1.7 × 10¹¹ | 1.7 × 10¹¹ | 2020 | dense |
| 104 | Palmyra X 004 (Writer, 2024) | ~1.5 × 10¹¹ | 2024 | kiyasi | |
| 105 | FLAN 137B (Google Research, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | ba a bayyana tsari ba |
| 106 | LaMDA (Google, 2022) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2022 | ba a bayyana tsari ba |
| 107 | MEB (Microsoft, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | ba a bayyana tsari ba |
| 108 | Pangu Ultra (Huawei, 2025) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2025 | ba a bayyana tsari ba |
| 109 | DBRX (Databricks, 2024) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2024 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 110 | GLM-130B (Tsinghua University, 2022) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2022 | dense |
| 111 | Pixtral Large (Mistral AI, 2024) | 1.2 × 10¹¹ | 1.2 × 10¹¹ | 2024 | ba a bayyana tsari ba |
| 112 | Mistral Large 2 (Mistral AI, 2024) | ~1.2 × 10¹¹ | 2024 | kiyasi | |
| 113 | Qwen3.5-122B-A10B (Alibaba, 2026) | ~1.2 × 10¹¹ | 2026 | kiyasi | |
| 114 | Galactica (Meta AI, 2022) | ~1.2 × 10¹¹ | 2022 | kiyasi | |
| 115 | Nemotron 3 Super (Nvidia, 2026) | ~1.2 × 10¹¹ | 2026 | kiyasi | |
| 116 | gpt-oss-120b (OpenAI, 2025) | 1.2 × 10¹¹ | 5.1 × 10⁹ | 2025 | gauraye na ƙwararru |
| 117 | Telechat2-115B (China Telecom, 2024) | 1.1 × 10¹¹ | 1.1 × 10¹¹ | 2024 | ba a bayyana tsari ba |
| 118 | Kaiju Large (Character.ai, 2025) | ~1.1 × 10¹¹ | 2025 | kiyasi | |
| 119 | Llama 4 Scout (Meta AI, 2025) | ~1.1 × 10¹¹ | 2025 | kiyasi | |
| 120 | InternLM (Shanghai AI Lab, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | dense |
| 121 | Solar Open 100B (Upstage, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | ba a bayyana tsari ba |
| 122 | DLRM-2020 (Facebook AI, 2019) | 1 × 10¹¹ | 1 × 10¹¹ | 2019 | dense |
| 123 | xTrimoPGLM -100B (Tsinghua University, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | ba a bayyana tsari ba |
| 124 | Yi-Large (01.AI, 2024) | ~1 × 10¹¹ | 2024 | kiyasi | |
| 125 | VAETKI (NC AI, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 126 | ESM3 (EvolutionaryScale, 2024) | 9.8 × 10¹⁰ | 9.8 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 127 | Neocortex na bera | 8.1 × 10¹⁰ | 8.1 × 10¹⁰ | synapses | |
| 128 | Flamingo (DeepMind, 2022) | 8 × 10¹⁰ | 8 × 10¹⁰ | 2022 | dense |
| 129 | Qwen3-Coder-Next (Alibaba, 2026) | ~8 × 10¹⁰ | 2026 | kiyasi | |
| 130 | Qwen2-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | dense |
| 131 | Qwen2.5-72B (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | dense |
| 132 | Qwen2.5 Instruct (Alibaba, 2024) | 7.3 × 10¹⁰ | 7.3 × 10¹⁰ | 2024 | dense |
| 133 | Qwen-72B (Alibaba, 2023) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2023 | dense |
| 134 | Palmyra X 003 (Writer, 2024) | ~7.2 × 10¹⁰ | 2024 | kiyasi | |
| 135 | Qwen1.5-72B (Alibaba, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | dense |
| 136 | LLaVA-OV-72B (ByteDance, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | dense |
| 137 | NVLM-X 72B (Nvidia, 2024) | ~7.2 × 10¹⁰ | 2024 | kiyasi | |
| 138 | NVLM-H 72B (Nvidia, 2024) | ~7.2 × 10¹⁰ | 2024 | kiyasi | |
| 139 | NVLM-D 72B (Nvidia, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 140 | MindLink-72B (Kunlun Inc., 2025) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2025 | ba a bayyana tsari ba |
| 141 | Chinchilla (DeepMind, 2022) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2022 | dense |
| 142 | Llama 2-70B (Meta AI, 2023) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2023 | dense |
| 143 | PPLX-70B-Online (Perplexity, 2023) | ~7 × 10¹⁰ | 2023 | kiyasi | |
| 144 | Llama 3-70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | dense |
| 145 | ALLaM adapted 70B (Saudi Data and Artificial Intelligence Authority, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 146 | Llama 3.3 70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | dense |
| 147 | Reka Core (Reka AI, 2024) | ~6.7 × 10¹⁰ | 2024 | kiyasi | |
| 148 | LLaMA-65B (Meta AI, 2023) | 6.5 × 10¹⁰ | 6.5 × 10¹⁰ | 2023 | dense |
| 149 | SNM-skip (Google, 2014) | ~6.2 × 10¹⁰ | 2014 | kiyasi | |
| 150 | PaLI-X (Google Research, 2023) | ~5.5 × 10¹⁰ | 2023 | kiyasi | |
| 151 | RT-2 (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 152 | RT-2-X (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 153 | NLLB (Meta AI, 2022) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2022 | dense |
| 154 | BloombergGPT (Bloomberg, 2023) | 5.1 × 10¹⁰ | 5.1 × 10¹⁰ | 2023 | dense |
| 155 | M4-50B (Google, 2019) | 5 × 10¹⁰ | 5 × 10¹⁰ | 2019 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 156 | Mixtral 8x7B (Mistral AI, 2023) | ~4.7 × 10¹⁰ | 2023 | kiyasi | |
| 157 | AlphaCode (DeepMind, 2022) | 4.1 × 10¹⁰ | 4.1 × 10¹⁰ | 2022 | ba a bayyana tsari ba |
| 158 | Falcon-40B (Technology Innovation Institute, 2023) | 4 × 10¹⁰ | 4 × 10¹⁰ | 2023 | dense |
| 159 | Qwen3-Omni-30B-A3B (Alibaba, 2025) | 3.5 × 10¹⁰ | 3 × 10⁹ | 2025 | gauraye na ƙwararru |
| 160 | Yi-34B (01.AI, 2023) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2023 | dense |
| 161 | Cambrian-1-34B (New York University (NYU), 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 162 | Oryx 34B (Tsinghua University, 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 163 | Kaiju Medium (Character.ai, 2025) | ~3.4 × 10¹⁰ | 2025 | kiyasi | |
| 164 | EXAONE 4.5 (LG AI Research, 2026) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2026 | ba a bayyana tsari ba |
| 165 | Qwen2.5-32B (Alibaba, 2024) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2024 | dense |
| 166 | QwQ-32B (Alibaba, 2025) | ~3.3 × 10¹⁰ | 2025 | kiyasi | |
| 167 | EXAONE 3.5 32B (LG AI Research, 2024) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 168 | EXAONE Deep 32B (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | ba a bayyana tsari ba |
| 169 | EXAONE 4.0 (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | ba a bayyana tsari ba |
| 170 | Olmo 3 (Allen Institute for AI, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | ba a bayyana tsari ba |
| 171 | HyperCLOVA X SEED 32B Think (NAVER, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | ba a bayyana tsari ba |
| 172 | Nemotron 3-Nano-30B-A3B (Nvidia, 2025) | ~3.2 × 10¹⁰ | 2025 | kiyasi | |
| 173 | Tongyi DeepResearch (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | ba a bayyana tsari ba |
| 174 | MM1-30B (Apple, 2024) | ~3 × 10¹⁰ | 2024 | kiyasi | |
| 175 | Movie Gen Video (Meta AI, 2024) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 176 | AgentFounder-30B (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | ba a bayyana tsari ba |
| 177 | PLUG (Alibaba, 2021) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2021 | ba a bayyana tsari ba |
| 178 | Qwen Image (Alibaba, 2025) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2025 | ba a bayyana tsari ba |
| 179 | GenSLM (University of Chicago, 2022) | 2.5 × 10¹⁰ | 2.5 × 10¹⁰ | 2022 | ba a bayyana tsari ba |
| 180 | ViT-22B (Google, 2023) | 2.2 × 10¹⁰ | 2.2 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 181 | KN-LM (Google, 2007) | ~2.1 × 10¹⁰ | 2007 | kiyasi | |
| 182 | gpt-oss-20b (OpenAI, 2025) | 2.1 × 10¹⁰ | 2.1 × 10¹⁰ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 183 | RNN for 1B words (Google, 2013) | ~2 × 10¹⁰ | 2013 | kiyasi | |
| 184 | GPT-NeoX-20B (EleutherAI, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | dense |
| 185 | UL2 (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | ba a bayyana tsari ba |
| 186 | Parti (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | ba a bayyana tsari ba |
| 187 | GPT-3.5 Turbo (OpenAI, 2023) | ~2 × 10¹⁰ | 2023 | kiyasi | |
| 188 | GPT-3.5 Turbo Instruct (OpenAI, 2023) | ~2 × 10¹⁰ | 2023 | kiyasi | |
| 189 | SPHINX (Shanghai AI Lab, 2023) | ~2 × 10¹⁰ | 2023 | kiyasi | |
| 190 | AlexaTM 20B (Amazon, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | ba a bayyana tsari ba |
| 191 | CogAgent (Tsinghua University, 2023) | ~1.8 × 10¹⁰ | 2023 | kiyasi | |
| 192 | Turing-NLG (Microsoft, 2020) | ~1.7 × 10¹⁰ | 2020 | kiyasi | |
| 193 | CogVLM-17B (Tsinghua University, 2023) | 1.7 × 10¹⁰ | 1.7 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 194 | PaLI (Google, 2022) | ~1.7 × 10¹⁰ | 2022 | kiyasi | |
| 195 | CodeT5+ (Salesforce, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 196 | StarCoder (Hugging Face, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | dense |
| 197 | ESM2-15B (Meta AI, 2022) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2022 | ba a bayyana tsari ba |
| 198 | FunSearch (Google DeepMind, 2023) | ~1.5 × 10¹⁰ | 2023 | kiyasi | |
| 199 | NVILA 15B (Nvidia, 2024) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 200 | nekomata-14b (rinna, 2023) | 1.4 × 10¹⁰ | 1.4 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 201 | VILA1.5-13B (Nvidia, 2024) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2024 | ba a bayyana tsari ba |
| 202 | VILA-13B (Nvidia, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 203 | mT5-XXL (Google, 2020) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2020 | dense |
| 204 | mT0-13B (Hugging Face, 2022) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2022 | ba a bayyana tsari ba |
| 205 | LLaVA (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | dense |
| 206 | InstructBLIP (Salesforce Research, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 207 | Jais (Cerebras Systems, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 208 | FinGPT-13B (University of California Los Angeles (UCLA), 2023) | ~1.3 × 10¹⁰ | 2023 | kiyasi | |
| 209 | Ferret (Columbia University, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 210 | Skywork-13B (Kunlun Inc., 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 211 | LLaVA 1.5 (University of Wisconsin Madison, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | dense |
| 212 | Volcano 13B (Korea University, 2023) | ~1.3 × 10¹⁰ | 2023 | kiyasi | |
| 213 | Aya (Cohere Labs (formerly Cohere for AI), 2024) | ~1.3 × 10¹⁰ | 2024 | kiyasi | |
| 214 | Kaiju Small (Character.ai, 2025) | ~1.3 × 10¹⁰ | 2025 | kiyasi | |
| 215 | ByT5-XXL (Google, 2021) | ~1.3 × 10¹⁰ | 2021 | kiyasi | |
| 216 | CoRe (Tsinghua University, 2023) | ~1.2 × 10¹⁰ | 2023 | kiyasi | |
| 217 | DALL-E (OpenAI, 2021) | ~1.2 × 10¹⁰ | 2021 | kiyasi | |
| 218 | Codex (OpenAI, 2021) | ~1.2 × 10¹⁰ | 2021 | kiyasi | |
| 219 | T5-11B (Google, 2019) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2019 | dense |
| 220 | UnifiedQA (Allen Institute for AI, 2020) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2020 | dense |
| 221 | Unicorn (Allen Institute for AI, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | ba a bayyana tsari ba |
| 222 | PLATO-XL (Baidu, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | ba a bayyana tsari ba |
| 223 | CoEdiT-xxl (University of Minnesota, 2023) | ~1.1 × 10¹⁰ | 2023 | kiyasi | |
| 224 | GQA-8-XXL (Google Research, 2023) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2023 | ba a bayyana tsari ba |
| 225 | XLMR-XXL (Facebook AI Research, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | ba a bayyana tsari ba |
| 226 | Llama 3.2 11B (Meta AI, 2024) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2024 | dense |
| 227 | ERNIE 3.0 (Baidu, 2021) | 1 × 10¹⁰ | 1 × 10¹⁰ | 2021 | dense |
| 228 | ERNIE-ViLG (Baidu, 2021) | ~1 × 10¹⁰ | 2021 | kiyasi | |
| 229 | Qwen-VL (Alibaba, 2023) | ~9.6 × 10⁹ | 2023 | kiyasi | |
| 230 | Generative BST (Facebook AI Research, 2021) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 231 | Sparse all-MLP (Meta AI, 2022) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2022 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 232 | CogVideo (Tsinghua University, 2022) | ~9.4 × 10⁹ | 2022 | kiyasi | |
| 233 | eDiff-I (Nvidia, 2022) | ~9.1 × 10⁹ | 2022 | kiyasi | |
| 234 | Eagle 2 (Nvidia, 2025) | 8.9 × 10⁹ | 8.9 × 10⁹ | 2025 | ba a bayyana tsari ba |
| 235 | MoE-Multi (Jagiellonian University, 2017) | 8.7 × 10⁹ | 8.7 × 10⁹ | 2017 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 236 | Qwen-Audio-Chat (Alibaba, 2023) | ~8.5 × 10⁹ | 2023 | kiyasi | |
| 237 | Megatron-LM (Nvidia, 2019) | ~8.3 × 10⁹ | 2019 | kiyasi | |
| 238 | BigSSL (Google, 2021) | ~8 × 10⁹ | 2021 | kiyasi | |
| 239 | Nemotron-3-8B (Nvidia, 2023) | 8 × 10⁹ | 8 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 240 | Stable Diffusion 3 (Stability AI, 2024) | 8 × 10⁹ | 8 × 10⁹ | 2024 | dense |
| 241 | Qwen3 Embedding (Alibaba, 2025) | 8 × 10⁹ | 8 × 10⁹ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 242 | Imagen (Google Brain, 2022) | ~7.8 × 10⁹ | 2022 | kiyasi | |
| 243 | XGLM-7.5B (Meta AI, 2021) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 244 | RETRO-7B (DeepMind, 2022) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 245 | OpenVLA (Stanford University, 2024) | 7.2 × 10⁹ | 7.2 × 10⁹ | 2024 | ba a bayyana tsari ba |
| 246 | mPLUG-Owl2 (Alibaba, 2023) | ~7.1 × 10⁹ | 2023 | kiyasi | |
| 247 | Goat-7B (National University of Singapore, 2023) | ~7 × 10⁹ | 2023 | kiyasi | |
| 248 | Llama 2-7B (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | dense |
| 249 | Llama Guard (Meta AI, 2023) | 7 × 10⁹ | 7 × 10⁹ | 2023 | dense |
| 250 | Qwen-VL-Max (Alibaba, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | dense |
| 251 | Mathstral (Mistral AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | ba a bayyana tsari ba |
| 252 | Apollo 7B (Meta AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | ba a bayyana tsari ba |
| 253 | Eurus-2-7B-PRIME (Tsinghua University, 2025) | ~7 × 10⁹ | 2025 | kiyasi | |
| 254 | INTELLECT-MATH (Prime Intellect, 2025) | 7 × 10⁹ | 7 × 10⁹ | 2025 | ba a bayyana tsari ba |
| 255 | Incoder-6.7B (Facebook AI Research, 2023) | 6.7 × 10⁹ | 6.7 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 256 | ProGen2-xlarge (Salesforce Research, 2022) | 6.4 × 10⁹ | 6.4 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 257 | GPipe (Google, 2018) | ~6 × 10⁹ | 2018 | kiyasi | |
| 258 | InstructGPT 6B (OpenAI, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | dense |
| 259 | Vega v2 (Wuhan University, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 260 | ChatGLM3-6B (Z.ai (Zhipu AI), 2023) | ~6 × 10⁹ | 2023 | kiyasi | |
| 261 | MolmoAct 2 (Allen Institute for AI, 2026) | ~5.5 × 10⁹ | 2026 | kiyasi | |
| 262 | T-NLRv5 XXL (Microsoft, 2021) | 5.4 × 10⁹ | 5.4 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 263 | π0.6 (Physical Intelligence, 2025) | 5.3 × 10⁹ | 5.3 × 10⁹ | 2025 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 264 | PaLI-3 (Google DeepMind, 2023) | 5 × 10⁹ | 5 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 265 | Mesh-TensorFlow Transformer 4.9B (Google Brain, 2018) | 4.9 × 10⁹ | 4.9 × 10⁹ | 2018 | dense |
| 266 | W.A.L.T (Stanford University, 2023) | 4.7 × 10⁹ | 4.7 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 267 | CogView (Tsinghua University, 2021) | ~4 × 10⁹ | 2021 | kiyasi | |
| 268 | Make-A-Scene (Meta AI, 2022) | ~4 × 10⁹ | 2022 | kiyasi | |
| 269 | ONE-PEACE (Alibaba, 2023) | ~4 × 10⁹ | 2023 | kiyasi | |
| 270 | Megatron-BERT (Nvidia, 2019) | 3.9 × 10⁹ | 3.9 × 10⁹ | 2019 | dense |
| 271 | DALL·E 2 (OpenAI, 2022) | ~3.5 × 10⁹ | 2022 | kiyasi | |
| 272 | Stable Diffusion XL (Stability AI, 2023) | ~3.4 × 10⁹ | 2023 | kiyasi | |
| 273 | MusicGen (Meta AI, 2023) | ~3.4 × 10⁹ | 2023 | kiyasi | |
| 274 | Gemini Nano-2 (Google DeepMind, 2023) | 3.3 × 10⁹ | 3.3 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 275 | Zidong Taichu (Chinese Academy of Sciences, 2021) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 276 | DeepNet (Microsoft Research, 2022) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 277 | MS-ensemble-speech-recognition (Microsoft, 2016) | ~3.2 × 10⁹ | 2016 | kiyasi | |
| 278 | BASIC-L (Google, 2021) | ~3.1 × 10⁹ | 2021 | kiyasi | |
| 279 | BASIC-L + Lion (Google, 2023) | 3.1 × 10⁹ | 3.1 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 280 | LEP-AD (King Abdullah University of Science and Technology (KAUST), 2023) | 3 × 10⁹ | 3 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 281 | ViT-G/14 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 282 | ProtT5-XL-U50 (Technical University of Munich, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 283 | Swin Transformer V2 (Microsoft Research Asia, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 284 | LongT5 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 285 | ReALM (Apple, 2024) | 3 × 10⁹ | 3 × 10⁹ | 2024 | ba a bayyana tsari ba |
| 286 | Mesh-TensorFlow Transformer 2.9B (Google Brain, 2018) | ~2.9 × 10⁹ | 2018 | kiyasi | |
| 287 | T5-3B (Google, 2019) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2019 | dense |
| 288 | DPO on Pythia-2.8B (Stanford University, 2023) | 2.8 × 10⁹ | 2.8 × 10⁹ | 2023 | dense |
| 289 | AFM-on-device (Apple, 2024) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2024 | ba a bayyana tsari ba |
| 290 | PolyCoder (Carnegie Mellon University (CMU), 2022) | ~2.7 × 10⁹ | 2022 | kiyasi | |
| 291 | Hybrid H3-2.7B (Stanford University, 2022) | ~2.7 × 10⁹ | 2022 | kiyasi | |
| 292 | GPT3-2.7B (Stanford University, 2023) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 293 | Meena (Google Brain, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | dense |
| 294 | CPM-Large (Tsinghua University, 2020) | 2.6 × 10⁹ | 2.6 × 10⁹ | 2020 | dense |
| 295 | Nucleotide Transformer (Nvidia, 2023) | ~2.5 × 10⁹ | 2023 | kiyasi | |
| 296 | Fugatto 1 (Nvidia, 2024) | 2.5 × 10⁹ | 2.5 × 10⁹ | 2024 | ba a bayyana tsari ba |
| 297 | CoAtNet (Google, 2021) | 2.4 × 10⁹ | 2.4 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 298 | GShard (Google, 2020) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2020 | dense |
| 299 | SeamlessM4T (Facebook, 2023) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 300 | Turing ULRv5 (Microsoft, 2021) | 2.2 × 10⁹ | 2.2 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 301 | CoCa (Google Research, 2022) | 2.1 × 10⁹ | 2.1 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 302 | MuseNet (OpenAI, 2019) | ~2 × 10⁹ | 2019 | kiyasi | |
| 303 | Infinity (ByteDance, 2024) | 2 × 10⁹ | 2 × 10⁹ | 2024 | ba a bayyana tsari ba |
| 304 | Seq2Seq LSTM (Google, 2014) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2014 | dense |
| 305 | BEIT-3 (Microsoft, 2022) | ~1.9 × 10⁹ | 2022 | kiyasi | |
| 306 | Ankh_large (Technical University of Munich, 2023) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 307 | ViT-G/14 (Google Brain, 2021) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 308 | ViT-G (University of Washington, 2022) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 309 | Phenaki (University College London (UCL), 2022) | ~1.8 × 10⁹ | 2022 | kiyasi | |
| 310 | Gemini Nano-1 (Google DeepMind, 2023) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 311 | DistBelief Vision (Google, 2012) | ~1.7 × 10⁹ | 2012 | kiyasi | |
| 312 | Whisper (OpenAI, 2022) | ~1.6 × 10⁹ | 2022 | kiyasi | |
| 313 | GPT-2 (OpenAI, 2019) | ~1.5 × 10⁹ | 2019 | kiyasi | |
| 314 | DeBERTa (Microsoft, 2021) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 315 | AR-LDM (Alibaba, 2022) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 316 | AudioLM (Google Research, 2023) | ~1.5 × 10⁹ | 2023 | kiyasi | |
| 317 | BLIP-2 (Salesforce Research, 2023) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 318 | LDM-1.45B (Heidelberg University, 2021) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 319 | Stable Diffusion (Runway, 2022) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2022 | dense |
| 320 | SEER (Facebook AI Research, 2021) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2021 | ba a bayyana tsari ba |
| 321 | InstructGPT 1.3B (OpenAI, 2022) | 1.3 × 10⁹ | 1.3 × 10⁹ | 2022 | dense |
| 322 | Megatron-LM (Nvidia, 2019) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2019 | dense |
| 323 | RDT-1B (Tsinghua University, 2024) | ~1.2 × 10⁹ | 2024 | kiyasi | |
| 324 | Gato (DeepMind, 2022) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 325 | DINOv2 (Facebook AI Research, 2023) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 326 | Diffusion Renderer (Nvidia, 2025) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2025 | ba a bayyana tsari ba |
| 327 | InternImage (Shanghai AI Lab, 2022) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 328 | EVA-01 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | 1 × 10⁹ | 1 × 10⁹ | 2022 | ba a bayyana tsari ba |
| 329 | Unsupervised High-level Feature Learner (Google, 2012) | ~1 × 10⁹ | 2012 | kiyasi | |
| 330 | Conformer + Wav2vec 2.0 + Noisy Student (Google, 2020) | 1 × 10⁹ | 1 × 10⁹ | 2020 | dense |
| 331 | HuBERT (Facebook AI Research, 2021) | ~1 × 10⁹ | 2021 | kiyasi | |
| 332 | W2v-BERT (Google Brain, 2021) | 1 × 10⁹ | 1 × 10⁹ | 2021 | dense |
| 333 | AudioGen (Meta AI, 2023) | ~1 × 10⁹ | 2023 | kiyasi | |
| 334 | VideoMAE V2 (Nanjing University, 2023) | 1 × 10⁹ | 1 × 10⁹ | 2023 | ba a bayyana tsari ba |
| 335 | TransE (Universite de Technologie de Compiègne – CNRS, 2013) | ~9.4 × 10⁸ | 2013 | kiyasi | |
| 336 | ImageBind (Meta AI, 2023) | ~9.3 × 10⁸ | 2023 | kiyasi | |
| 337 | Big Transfer (Google Brain, 2019) | ~9.3 × 10⁸ | 2019 | kiyasi | |
| 338 | Florence (Microsoft, 2021) | 8.9 × 10⁸ | 8.9 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 339 | NÜWA (Microsoft Research, 2021) | 8.7 × 10⁸ | 8.7 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 340 | MusicLM (Google, 2023) | 8.6 × 10⁸ | 8.6 × 10⁸ | 2023 | ba a bayyana tsari ba |
| 341 | ResNeXt-101 32x48d (Facebook, 2018) | 8.3 × 10⁸ | 8.3 × 10⁸ | 2018 | dense |
| 342 | FixRes ResNeXt-101 WSL (Facebook AI, 2019) | ~8.3 × 10⁸ | 2019 | kiyasi | |
| 343 | ALIGN (Google Research, 2021) | 8.2 × 10⁸ | 8.2 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 344 | Statement Curriculum Learning (OpenAI, 2022) | ~7.7 × 10⁸ | 2022 | kiyasi | |
| 345 | CodeT5-large (Salesforce, 2022) | ~7.7 × 10⁸ | 2022 | kiyasi | |
| 346 | DITTO (Tsinghua University, 2022) | 7.5 × 10⁸ | 7.5 × 10⁸ | 2022 | ba a bayyana tsari ba |
| 347 | FGN (Google DeepMind, 2025) | 7.2 × 10⁸ | 7.2 × 10⁸ | 2025 | ba a bayyana tsari ba |
| 348 | data2vec (Meta AI, 2022) | ~7.1 × 10⁸ | 2022 | kiyasi | |
| 349 | Tranception (University of Oxford, 2022) | 7 × 10⁸ | 7 × 10⁸ | 2022 | ba a bayyana tsari ba |
| 350 | Word2Vec (Google, 2013) | ~6.9 × 10⁸ | 2013 | kiyasi | |
| 351 | DiT-XL/2 (New York University (NYU), 2023) | 6.8 × 10⁸ | 6.8 × 10⁸ | 2023 | ba a bayyana tsari ba |
| 352 | DiT-XL/2 + CADS (ETH Zurich, 2023) | ~6.8 × 10⁸ | 2023 | kiyasi | |
| 353 | OmegaPLM (Massachusetts Institute of Technology (MIT), 2022) | 6.7 × 10⁸ | 6.7 × 10⁸ | 2022 | ba a bayyana tsari ba |
| 354 | XLM (Facebook, 2019) | ~6.7 × 10⁸ | 2019 | kiyasi | |
| 355 | ESM1b (Facebook AI Research, 2020) | 6.5 × 10⁸ | 6.5 × 10⁸ | 2020 | dense |
| 356 | Segment Anything Model (Meta AI, 2023) | 6.4 × 10⁸ | 6.4 × 10⁸ | 2023 | ba a bayyana tsari ba |
| 357 | ViT-Huge/14 (Google Brain, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | dense |
| 358 | Masked Autoencoders ViT-H (Facebook AI Research, 2021) | ~6.3 × 10⁸ | 2021 | kiyasi | |
| 359 | Retrieval-Augmented Generator (Facebook, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | dense |
| 360 | DLDL (University of Oxford, 2016) | ~5.6 × 10⁸ | 2016 | kiyasi | |
| 361 | ADM (OpenAI, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 362 | TrOCR (Beihang University, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 363 | XLM-RoBERTa (Facebook AI, 2019) | 5.5 × 10⁸ | 5.5 × 10⁸ | 2019 | dense |
| 364 | LUKE (University of Washington, 2020) | ~4.8 × 10⁸ | 2020 | kiyasi | |
| 365 | EfficientNet-L2 (Google, 2019) | ~4.8 × 10⁸ | 2019 | kiyasi | |
| 366 | Noisy Student (Carnegie Mellon University (CMU), 2019) | 4.8 × 10⁸ | 4.8 × 10⁸ | 2019 | dense |
| 367 | Meta Pseudo Labels (Google Brain, 2021) | ~4.8 × 10⁸ | 2021 | kiyasi | |
| 368 | EMDR (Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), 2021) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 369 | NMT Transformer 437M (Google, 2019) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2019 | dense |
| 370 | Adaptive Inputs + LayerDrop (Facebook AI Research, 2019) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2019 | dense |
| 371 | ProtBERT-BFD (Technical University of Munich, 2021) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 372 | OntoProtein (Zhejiang University (ZJU), 2022) | ~4.2 × 10⁸ | 2022 | kiyasi | |
| 373 | DeBERTaV3large + KEAR (Microsoft, 2022) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2022 | ba a bayyana tsari ba |
| 374 | BART-large (Facebook AI, 2019) | ~4.1 × 10⁸ | 2019 | kiyasi | |
| 375 | SigLIP 400M (Google DeepMind, 2023) | 4 × 10⁸ | 4 × 10⁸ | 2023 | ba a bayyana tsari ba |
| 376 | BERT-Large-CAS (Amazon, 2019) | ~4 × 10⁸ | 2019 | kiyasi | |
| 377 | MoCo (Facebook AI, 2019) | ~3.8 × 10⁸ | 2019 | kiyasi | |
| 378 | SimCLR (Google Brain, 2020) | ~3.8 × 10⁸ | 2020 | kiyasi | |
| 379 | CLIP (OpenAI, 2021) | 3.7 × 10⁸ | 3.7 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 380 | RoBERTa Large (Facebook, 2019) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2019 | dense |
| 381 | AbLang (University of Oxford, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | ba a bayyana tsari ba |
| 382 | GPT-2 Medium (Stanford University, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | dense |
| 383 | VALL-E (Microsoft, 2023) | ~3.5 × 10⁸ | 2023 | kiyasi | |
| 384 | BERT-Large (Google, 2018) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2018 | dense |
| 385 | XLNet (Carnegie Mellon University (CMU), 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | dense |
| 386 | ERNIE-GEN (Baidu, 2020) | ~3.4 × 10⁸ | 2020 | kiyasi | |
| 387 | CamemBERT (Facebook, 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | dense |
| 388 | ELECTRA (Stanford University, 2020) | ~3.4 × 10⁸ | 2020 | kiyasi | |
| 389 | German ELECTRA Large (deepset, 2020) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2020 | dense |
| 390 | ALM 1.0 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | ~3.4 × 10⁸ | 2022 | kiyasi | |
| 391 | MT-DNN (Microsoft, 2019) | 3.3 × 10⁸ | 3.3 × 10⁸ | 2019 | dense |
| 392 | Fusion in Encoder (Samsung, 2022) | ~3.3 × 10⁸ | 2022 | kiyasi | |
| 393 | wave2vec 2.0 LARGE (Facebook, 2020) | 3.2 × 10⁸ | 3.2 × 10⁸ | 2020 | dense |
| 394 | CPC v2 (DeepMind, 2019) | ~3 × 10⁸ | 2019 | kiyasi | |
| 395 | Transformer - LibriVox + Decoding/Rescoring (Facebook, 2019) | 3 × 10⁸ | 3 × 10⁸ | 2019 | dense |
| 396 | DDPM-IP (Utrecht University, 2023) | ~3 × 10⁸ | 2023 | kiyasi | |
| 397 | GNMT (Google, 2016) | ~2.8 × 10⁸ | 2016 | kiyasi | |
| 398 | base LM+GNN+kNN (Shannon.AI, 2021) | 2.7 × 10⁸ | 2.7 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 399 | YOLO (University of Washington, 2015) | ~2.7 × 10⁸ | 2015 | kiyasi | |
| 400 | Transformer-XL (Carnegie Mellon University (CMU), 2019) | ~2.6 × 10⁸ | 2019 | kiyasi | |
| 401 | Transformer-XL + RMS dynamic eval (University of Edinburgh, 2019) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2019 | dense |
| 402 | Transformer-XL Large + Phrase Induction (Massachusetts Institute of Technology (MIT), 2019) | ~2.6 × 10⁸ | 2019 | kiyasi | |
| 403 | Segatron-XL large, M=384 + HCP (Microsoft Research, 2022) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2022 | ba a bayyana tsari ba |
| 404 | Denoising Diffusion Probabilistic Models (University of California (UC) Berkeley, 2021) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 405 | Pangu-Weather (Huawei, 2023) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2023 | ba a bayyana tsari ba |
| 406 | SPN (Google Brain, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | dense |
| 407 | S4 (Stanford University, 2021) | ~2.5 × 10⁸ | 2021 | kiyasi | |
| 408 | Base LM + kNN LM + Continuous Cache (Stanford University, 2019) | ~2.5 × 10⁸ | 2019 | kiyasi | |
| 409 | ERNIE-Doc (Baidu, 2020) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2020 | dense |
| 410 | Adaptive Input Transformer + RD (Microsoft Research Asia, 2021) | ~2.5 × 10⁸ | 2021 | kiyasi | |
| 411 | Transformer (Adaptive Input Embeddings) WT103 (Facebook AI Research, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | dense |
| 412 | TaLK Convolution (Carleton University, 2020) | ~2.4 × 10⁸ | 2020 | kiyasi | |
| 413 | ALBERT-xxlarge (Toyota Technological Institute at Chicago, 2020) | ~2.4 × 10⁸ | 2020 | kiyasi | |
| 414 | SRU++ Large (ASAPP, 2021) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 415 | GR-2 (ByteDance, 2024) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2024 | ba a bayyana tsari ba |
| 416 | MaskGIT (Google Research, 2022) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2022 | ba a bayyana tsari ba |
| 417 | MetNet (Google, 2020) | ~2.3 × 10⁸ | 2020 | kiyasi | |
| 418 | FFN SwiGLU (Google, 2020) | 2.2 × 10⁸ | 2.2 × 10⁸ | 2020 | dense |
| 419 | CodeT5-base (Salesforce, 2021) | ~2.2 × 10⁸ | 2021 | kiyasi | |
| 420 | Transformer (Google Research, 2017) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2017 | dense |
| 421 | Sandwich Transformer (Allen Institute for AI, 2019) | ~2.1 × 10⁸ | 2019 | kiyasi | |
| 422 | EfficientNetV2-XL (Google, 2021) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 423 | Word2Vec (Google, 2013) | ~2.1 × 10⁸ | 2013 | kiyasi | |
| 424 | Multilingual DNN (Google, 2013) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2013 | dense |
| 425 | DreamerV3 (DeepMind, 2023) | ~2 × 10⁸ | 2023 | kiyasi | |
| 426 | DL scaling speech (Baidu, 2017) | ~1.9 × 10⁸ | 2017 | kiyasi | |
| 427 | ResNeXt-101 Billion-scale (Facebook AI, 2019) | ~1.9 × 10⁸ | 2019 | kiyasi | |
| 428 | (NTT Communication Science Laboratories, 2018) | 1.9 × 10⁸ | 1.9 × 10⁸ | 2018 | dense |
| 429 | TrellisNet (Carnegie Mellon University (CMU), 2018) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2018 | dense |
| 430 | DL scaling LM (Baidu, 2017) | ~1.8 × 10⁸ | 2017 | kiyasi | |
| 431 | EXAONE Path 2.0 (LG AI Research, 2025) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2025 | ba a bayyana tsari ba |
| 432 | OpenAI Five Rerun (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | dense |
| 433 | OpenAI Five (OpenAI, 2019) | 1.6 × 10⁸ | 1.6 × 10⁸ | 2019 | dense |
| 434 | 4 layer QRNN (Salesforce Research, 2018) | ~1.5 × 10⁸ | 2018 | kiyasi | |
| 435 | TransformerXL + spectrum control (University of California Los Angeles (UCLA), 2020) | ~1.5 × 10⁸ | 2020 | kiyasi | |
| 436 | AlphaGeometry (Google DeepMind, 2024) | 1.5 × 10⁸ | 1.5 × 10⁸ | 2024 | ba a bayyana tsari ba |
| 437 | PermuteFormer (Peking University, 2021) | ~1.5 × 10⁸ | 2021 | kiyasi | |
| 438 | Fragment embedding (Stanford University, 2014) | ~1.4 × 10⁸ | 2014 | kiyasi | |
| 439 | OverFeat (New York University (NYU), 2013) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2013 | dense |
| 440 | VGG19 (University of Oxford, 2014) | ~1.4 × 10⁸ | 2014 | kiyasi | |
| 441 | LRCN (UT Austin, 2014) | ~1.4 × 10⁸ | 2014 | kiyasi | |
| 442 | Transformer-XL DeFINE (University of Washington, 2019) | ~1.4 × 10⁸ | 2019 | kiyasi | |
| 443 | Deep Autoencoders (University of Toronto, 2011) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2011 | dense |
| 444 | AlphaStar (DeepMind, 2019) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2019 | dense |
| 445 | VGG16 (University of Oxford, 2014) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2014 | dense |
| 446 | VGG-Face (University of Oxford, 2015) | 1.4 × 10⁸ | 1.4 × 10⁸ | 2015 | dense |
| 447 | SAF R-CNN (Beijing Institute of Technology, 2015) | ~1.4 × 10⁸ | 2015 | kiyasi | |
| 448 | Template Adaptation (University of Oxford, 2016) | ~1.4 × 10⁸ | 2016 | kiyasi | |
| 449 | LRR-4X (UC Irvine, 2016) | ~1.4 × 10⁸ | 2016 | kiyasi | |
| 450 | CMS-RCNN (IEEE, 2016) | ~1.4 × 10⁸ | 2016 | kiyasi | |
| 451 | QRNN (Salesforce Research, 2018) | ~1.4 × 10⁸ | 2018 | kiyasi | |
| 452 | Multiresolution CNN (Google, 2014) | ~1.3 × 10⁸ | 2014 | kiyasi | |
| 453 | FTW (DeepMind, 2018) | ~1.3 × 10⁸ | 2018 | kiyasi | |
| 454 | Feedback Transformer (LORIA, 2020) | ~1.3 × 10⁸ | 2020 | kiyasi | |
| 455 | KEPLER (Tsinghua University, 2020) | 1.3 × 10⁸ | 1.3 × 10⁸ | 2020 | dense |
| 456 | DL scaling Image (Baidu, 2017) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2017 | dense |
| 457 | GloVe (Stanford University, 2014) | ~1.2 × 10⁸ | 2014 | kiyasi | |
| 458 | Conformer (Google, 2020) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2020 | dense |
| 459 | GPT-1 (OpenAI, 2018) | ~1.2 × 10⁸ | 2018 | kiyasi | |
| 460 | ContextNet (Google, 2020) | ~1.1 × 10⁸ | 2020 | kiyasi | |
| 461 | SciBERT (Allen Institute for AI, 2019) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2019 | dense |
| 462 | DNABERT (Northeastern University, 2021) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2021 | ba a bayyana tsari ba |
| 463 | Contriever (Meta AI, 2021) | ~1.1 × 10⁸ | 2021 | kiyasi | |
| 464 | BERT-RBP (Waseda University, 2022) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2022 | dense |
| 465 | RoFormer (Zhuiyi Technology, 2023) | 1.1 × 10⁸ | 1.1 × 10⁸ | 2023 | ba a bayyana tsari ba |
| 466 | LSTM LM (RWTH Aachen University, 2012) | ~1 × 10⁸ | 2012 | kiyasi | |
| 467 | ReLU-Speech (Google, 2013) | ~1 × 10⁸ | 2013 | kiyasi | |
| 468 | STORM-B/8 (University of Southern California, 2024) | 1 × 10⁸ | 1 × 10⁸ | 2024 | ba a bayyana tsari ba |
| 469 | Support Vector Machines (AT&T, 1995) | ~1 × 10⁸ | 1995 | kiyasi | |
| 470 | GPU DBNs (Stanford University, 2009) | 1 × 10⁸ | 1 × 10⁸ | 2009 | dense |
| 471 | MSA Transformer (Facebook AI Research, 2021) | ~1 × 10⁸ | 2021 | kiyasi | |
| 472 | YOLOX-X (Megvii Inc, 2021) | ~9.9 × 10⁷ | 2021 | kiyasi | |
| 473 | DeLighT (University of Washington, 2020) | ~9.9 × 10⁷ | 2020 | kiyasi | |
| 474 | ELMo (University of Washington, 2018) | ~9.4 × 10⁷ | 2018 | kiyasi | |
| 475 | AlphaFold 2 (DeepMind, 2020) | ~9.3 × 10⁷ | 2020 | kiyasi | |
| 476 | AlphaMissense (Google DeepMind, 2023) | ~9.3 × 10⁷ | 2023 | kiyasi | |
| 477 | Octo-Base (University of California (UC) Berkeley, 2024) | 9.3 × 10⁷ | 9.3 × 10⁷ | 2024 | ba a bayyana tsari ba |
| 478 | PolyNet (Chinese University of Hong Kong (CUHK), 2016) | ~9.2 × 10⁷ | 2016 | kiyasi | |
| 479 | Transformer local-attention (Google Cloud, 2021) | 9 × 10⁷ | 9 × 10⁷ | 2021 | ba a bayyana tsari ba |
| 480 | Transformer + Simple Recurrent Unit (ASAPP, 2018) | 9 × 10⁷ | 9 × 10⁷ | 2018 | dense |
| 481 | NASNet-A (Google Brain, 2017) | ~8.9 × 10⁷ | 2017 | kiyasi | |
| 482 | CLIP (OpenAI, 2021) | ~8.9 × 10⁷ | 2021 | kiyasi | |
| 483 | Detic (Meta AI, 2022) | ~8.8 × 10⁷ | 2022 | kiyasi | |
| 484 | MSRA (Microsoft Research, 2015) | 8.7 × 10⁷ | 8.7 × 10⁷ | 2015 | dense |
| 485 | BigBiGAN (Google, 2019) | ~8.6 × 10⁷ | 2019 | kiyasi | |
| 486 | ViT-Base/32 (Google Brain, 2020) | ~8.6 × 10⁷ | 2020 | kiyasi | |
| 487 | DeiT-B (Meta AI, 2021) | 8.6 × 10⁷ | 8.6 × 10⁷ | 2021 | ba a bayyana tsari ba |
| 488 | CaLM (University of Oxford, 2022) | ~8.6 × 10⁷ | 2022 | kiyasi | |
| 489 | Tensor-Transformer (University of California (UC) Berkeley, 2020) | 8.5 × 10⁷ | 8.5 × 10⁷ | 2020 | dense |
| 490 | ResNeXt-101 (University of California San Diego, 2016) | 8.3 × 10⁷ | 8.3 × 10⁷ | 2016 | dense |
| 491 | RBM Image Classifier (University of Toronto, 2009) | ~8 × 10⁷ | 2009 | kiyasi | |
| 492 | Routing Transformer (Google Research, 2020) | 8 × 10⁷ | 8 × 10⁷ | 2020 | dense |
| 493 | Big-Little Net (IBM, 2018) | ~7.7 × 10⁷ | 2018 | kiyasi | |
| 494 | EfficientDet (Google Brain, 2020) | ~7.7 × 10⁷ | 2020 | kiyasi | |
| 495 | CODEFUSION (Microsoft, 2023) | 7.5 × 10⁷ | 7.5 × 10⁷ | 2023 | ba a bayyana tsari ba |
| 496 | RNN LM (Johns Hopkins University, 2010) | ~7 × 10⁷ | 2010 | kiyasi | |
| 497 | R-CNN (University of California (UC) Berkeley, 2013) | ~6.9 × 10⁷ | 2013 | kiyasi | |
| 498 | Variational (University of Cambridge, 2015) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2015 | dense |
| 499 | DistilBERT (Hugging Face, 2019) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2019 | dense |
| 500 | Theseus 6/768 (University of California San Diego, 2020) | 6.6 × 10⁷ | 6.6 × 10⁷ | 2020 | dense |
| 501 | ResNet-152 (Microsoft, 2015) | 6 × 10⁷ | 6 × 10⁷ | 2015 | dense |
| 502 | AlexNet (University of Toronto, 2012) | 6 × 10⁷ | 6 × 10⁷ | 2012 | dense |
| 503 | DETR (Facebook, 2020) | 6 × 10⁷ | 6 × 10⁷ | 2020 | dense |
| 504 | YOLOv3 (University of Washington, 2018) | ~5.7 × 10⁷ | 2018 | kiyasi | |
| 505 | Inception-ResNet-V2 (Google, 2016) | ~5.6 × 10⁷ | 2016 | kiyasi | |
| 506 | Transformer ELMo (Allen Institute for AI, 2019) | 5.6 × 10⁷ | 5.6 × 10⁷ | 2019 | dense |
| 507 | Kwakwalwar ƙudan ’ya’yan itace | 5.5 × 10⁷ | 5.5 × 10⁷ | synapses | |
| 508 | NAS with base 8 and shared embeddings (Google Brain, 2016) | 5.4 × 10⁷ | 5.4 × 10⁷ | 2016 | dense |
| 509 | RetinaNet-R101 (Facebook AI Research, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | dense |
| 510 | AWD-LSTM+WT+Cache+IOG (NTT Communication Science Laboratories, 2017) | 5.3 × 10⁷ | 5.3 × 10⁷ | 2017 | dense |
| 511 | Hierarchical Scene Labeling (New York University (NYU), 2013) | 5.2 × 10⁷ | 5.2 × 10⁷ | 2013 | dense |
| 512 | VD-LSTM+REAL Large (Salesforce Research, 2016) | 5.1 × 10⁷ | 5.1 × 10⁷ | 2016 | dense |
| 513 | YOLOv2 (University of Washington, 2016) | ~5.1 × 10⁷ | 2016 | kiyasi | |
| 514 | Dropout-LSTM+Noise (Columbia University, 2018) | ~5.1 × 10⁷ | 2018 | kiyasi | |
| 515 | LSTM + dynamic eval (University of Edinburgh, 2017) | ~5 × 10⁷ | 2017 | kiyasi | |
| 516 | Dropout (University of Toronto, 2012) | ~4.9 × 10⁷ | 2012 | kiyasi | |
| 517 | Dropout: SVHN (University of Toronto, 2014) | 4.8 × 10⁷ | 4.8 × 10⁷ | 2014 | dense |
| 518 | DistBelief Speech (Google, 2012) | ~4.7 × 10⁷ | 2012 | kiyasi | |
| 519 | AlphaGo Zero (DeepMind, 2017) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2017 | dense |
| 520 | Fine-tuned-AWD-LSTM-DOC (Samsung R&D Institute Russia, 2018) | 4.6 × 10⁷ | 4.6 × 10⁷ | 2018 | dense |
| 521 | CT-MoS (Google, 2020) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2020 | dense |
| 522 | JFT (Google Research, 2017) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2017 | dense |
| 523 | ResNet-101 (Microsoft, 2015) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2015 | dense |
| 524 | HR-ResNet101 (Carnegie Mellon University (CMU), 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | dense |
| 525 | 3DMM-CNN (University of Southern California, 2016) | 4.5 × 10⁷ | 4.5 × 10⁷ | 2016 | dense |
| 526 | LaNet-L (Brown University, 2019) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2019 | dense |
| 527 | ProBERTa (University of Illinois Urbana-Champaign (UIUC), 2020) | 4.4 × 10⁷ | 4.4 × 10⁷ | 2020 | dense |
| 528 | PreTrans-3L-250H (University of Toronto, 2013) | ~4.3 × 10⁷ | 2013 | kiyasi | |
| 529 | Inceptionv4 (Google, 2016) | ~4.3 × 10⁷ | 2016 | kiyasi | |
| 530 | GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (Ben-Gurion University of the Negev, 2017) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2017 | dense |
| 531 | U-Net (University of Freiburg, 2015) | 3.8 × 10⁷ | 3.8 × 10⁷ | 2015 | dense |
| 532 | NASv3 (Google Brain, 2016) | ~3.7 × 10⁷ | 2016 | kiyasi | |
| 533 | MuZero (DeepMind, 2019) | ~3.7 × 10⁷ | 2019 | kiyasi | |
| 534 | AWD-LSTM-MoS + dynamic evaluation (Carnegie Mellon University (CMU), 2017) | ~3.5 × 10⁷ | 2017 | kiyasi | |
| 535 | AWD-LSTM-MoS+PDR + dynamic evaluation (IBM, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | dense |
| 536 | AWD-LSTM-MoS + dynamic evaluation (Peking University, 2018) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2018 | dense |
| 537 | AWD-LSTM + MoS + Partial Shuffled (University of Texas at Austin, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | dense |
| 538 | Mogrifier (d2, MoS2, MC) + dynamic eval (DeepMind, 2019) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2019 | dense |
| 539 | Mogrifier RLSTM (DeepMind, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | ba a bayyana tsari ba |
| 540 | RT-1 (Google, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | ba a bayyana tsari ba |
| 541 | DenseNet-264 (Tsinghua University, 2016) | 3.4 × 10⁷ | 3.4 × 10⁷ | 2016 | dense |
| 542 | RetinaNet-R50 (Facebook AI Research, 2017) | ~3.4 × 10⁷ | 2017 | kiyasi | |
| 543 | Fraternal dropout + AWD-LSTM 3-layer (Jagiellonian University, 2017) | ~3.4 × 10⁷ | 2017 | kiyasi | |
| 544 | AWD-LSTM-DRILL + dynamic evaluation† (IDIAP, 2019) | ~3.4 × 10⁷ | 2019 | kiyasi | |
| 545 | AWD-LSTM - 3-layer LSTM (Salesforce Research, 2017) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2017 | dense |
| 546 | TCAN (Nanjing University, 2020) | 3.3 × 10⁷ | 3.3 × 10⁷ | 2020 | dense |
| 547 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | ~3.2 × 10⁷ | 2019 | kiyasi | |
| 548 | Paragraph Vector (Google, 2014) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2014 | dense |
| 549 | aLSTM (University of Manchester, 2018) | 3.2 × 10⁷ | 3.2 × 10⁷ | 2018 | dense |
| 550 | StyleGAN2 (Nvidia, 2019) | 3 × 10⁷ | 3 × 10⁷ | 2019 | dense |
| 551 | UDSMProt (Fraunhofer Heinrich Hertz Institute, 2019) | ~2.8 × 10⁷ | 2019 | kiyasi | |
| 552 | SENet (Chinese Academy of Sciences, 2017) | ~2.8 × 10⁷ | 2017 | kiyasi | |
| 553 | Perceiver IO (DeepMind, 2020) | 2.8 × 10⁷ | 2.8 × 10⁷ | 2020 | dense |
| 554 | Fractional Max-Pooling (University of Warwick, 2014) | ~2.7 × 10⁷ | 2014 | kiyasi | |
| 555 | Hierarchical Reasoning Model (Sapient Intelligence, 2025) | 2.7 × 10⁷ | 2.7 × 10⁷ | 2025 | ba a bayyana tsari ba |
| 556 | RNN-WER (DeepMind, 2014) | ~2.6 × 10⁷ | 2014 | kiyasi | |
| 557 | StyleGAN (Nvidia, 2018) | 2.6 × 10⁷ | 2.6 × 10⁷ | 2018 | dense |
| 558 | PyramidNet (Korea Advanced Institute of Science and Technology (KAIST), 2017) | ~2.6 × 10⁷ | 2017 | kiyasi | |
| 559 | ResNeXt-50 (University of California San Diego, 2016) | ~2.5 × 10⁷ | 2016 | kiyasi | |
| 560 | AWD-LSTM (DeepMind, 2017) | 2.4 × 10⁷ | 2.4 × 10⁷ | 2017 | dense |
| 561 | Inception v3 (Google, 2015) | ~2.4 × 10⁷ | 2015 | kiyasi | |
| 562 | Mamba-24M (Carnegie Mellon University (CMU), 2023) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2023 | ba a bayyana tsari ba |
| 563 | WeNet (Amazon, 2019) | ~2.3 × 10⁷ | 2019 | kiyasi | |
| 564 | EN^2AS with performance reward (Beijing Institute of Technology, 2019) | ~2.3 × 10⁷ | 2019 | kiyasi | |
| 565 | NAS+ESS (Northeastern University (China), 2020) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2020 | dense |
| 566 | Xception (Google, 2016) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2016 | dense |
| 567 | MetaMimic (Google, 2018) | ~2.2 × 10⁷ | 2018 | kiyasi | |
| 568 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | ~2.1 × 10⁷ | 2019 | kiyasi | |
| 569 | Pointer Sentinel-LSTM (MetaMind Inc, 2016) | 2.1 × 10⁷ | 2.1 × 10⁷ | 2016 | dense |
| 570 | DiffDock (Massachusetts Institute of Technology (MIT), 2022) | ~2 × 10⁷ | 2022 | kiyasi | |
| 571 | EI-REHN-1000D (Korea Advanced Institute of Science and Technology (KAIST), 2017) | 1.9 × 10⁷ | 1.9 × 10⁷ | 2017 | dense |
| 572 | TC-DNN-BLSTM-DNN (Carnegie Mellon University (CMU), 2015) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2015 | dense |
| 573 | OR-WideResNet (Duke University, 2017) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2017 | dense |
| 574 | BP-DBN (University of Toronto, 2009) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2009 | dense |
| 575 | ALBERT (Toyota Technological Institute at Chicago, 2019) | 1.8 × 10⁷ | 1.8 × 10⁷ | 2019 | dense |
| 576 | MedBERT (Peng Cheng Laboratory, 2021) | ~1.7 × 10⁷ | 2021 | kiyasi | |
| 577 | LCNP NORB (, 2009) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2009 | dense |
| 578 | LF-MMI (Johns Hopkins University, 2016) | 1.7 × 10⁷ | 1.7 × 10⁷ | 2016 | dense |
| 579 | AlphaFold (DeepMind, 2020) | ~1.6 × 10⁷ | 2020 | kiyasi | |
| 580 | GNoME for crystal discovery (Google DeepMind, 2023) | ~1.6 × 10⁷ | 2023 | kiyasi | |
| 581 | ReLU (University of Toronto, 2010) | ~1.6 × 10⁷ | 2010 | kiyasi | |
| 582 | ProteinBERT (Hebrew University of Jerusalem, 2022) | 1.6 × 10⁷ | 1.6 × 10⁷ | 2022 | ba a bayyana tsari ba |
| 583 | Eve (Harvard Medical School, 2021) | ~1.5 × 10⁷ | 2021 | kiyasi | |
| 584 | LCNP LabelMe (University of Bonn, 2009) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2009 | dense |
| 585 | BatchNorm (Google, 2015) | 1.4 × 10⁷ | 1.4 × 10⁷ | 2015 | dense |
| 586 | Fold2Seq (IBM, 2021) | ~1.2 × 10⁷ | 2021 | kiyasi | |
| 587 | NPLM (University of Montreal / Université de Montréal, 2003) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2003 | dense |
| 588 | LCNP MNIST (, 2009) | 1.2 × 10⁷ | 1.2 × 10⁷ | 2009 | dense |
| 589 | ISS (Duke University, 2017) | 1.1 × 10⁷ | 1.1 × 10⁷ | 2017 | dense |
| 590 | ResNet-1001 (Microsoft, 2016) | ~1 × 10⁷ | 2016 | kiyasi | |
| 591 | RNN+LDA+KN5+cache (Microsoft, 2012) | ~9 × 10⁶ | 2012 | kiyasi | |
| 592 | Gated HORNN (York University, 2016) | ~9 × 10⁶ | 2016 | kiyasi | |
| 593 | AlphaGo Fan (DeepMind, 2015) | ~8.2 × 10⁶ | 2015 | kiyasi | |
| 594 | genCNN + dyn eval (Chinese Academy of Sciences, 2015) | ~8 × 10⁶ | 2015 | kiyasi | |
| 595 | Once for All (MIT-IBM Watson AI Lab, 2020) | 7.7 × 10⁶ | 7.7 × 10⁶ | 2020 | dense |
| 596 | Multi-cell LSTM (University of Hyderabad, 2018) | ~7.2 × 10⁶ | 2018 | kiyasi | |
| 597 | Feedforward NN (University of Montreal / Université de Montréal, 2010) | 7.1 × 10⁶ | 7.1 × 10⁶ | 2010 | dense |
| 598 | True-Regularization+Finetune+Dynamic-Eval (Mobvoi, 2019) | 7 × 10⁶ | 7 × 10⁶ | 2019 | dense |
| 599 | Neural LM (University of Montreal / Université de Montréal, 2000) | 6.9 × 10⁶ | 6.9 × 10⁶ | 2000 | dense |
| 600 | GoogLeNet / InceptionV1 (Google, 2014) | 6.8 × 10⁶ | 6.8 × 10⁶ | 2014 | dense |
| 601 | HyenaDNA (Stanford University, 2023) | 6.6 × 10⁶ | 6.6 × 10⁶ | 2023 | ba a bayyana tsari ba |
| 602 | Dropout (University of Toronto, 2012) | 5.6 × 10⁶ | 5.6 × 10⁶ | 2012 | dense |
| 603 | SimpleNet (Sensifai, 2016) | 5.5 × 10⁶ | 5.5 × 10⁶ | 2016 | dense |
| 604 | AlphaX-1 (Facebook AI Research, 2019) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2019 | dense |
| 605 | 3DDFA (Chinese Academy of Sciences, 2015) | 5.4 × 10⁶ | 5.4 × 10⁶ | 2015 | dense |
| 606 | Deep CNN + COTS (IEEE, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | dense |
| 607 | DCNN (University of Maryland, 2015) | 5 × 10⁶ | 5 × 10⁶ | 2015 | dense |
| 608 | NLP from scratch (NEC Laboratories, 2011) | ~5 × 10⁶ | 2011 | kiyasi | |
| 609 | SPN-4+KN5 (Singapore University of Technology & Design, 2014) | ~5 × 10⁶ | 2014 | kiyasi | |
| 610 | 6-Act Tether (Facebook AI Research, 2021) | 5 × 10⁶ | 5 × 10⁶ | 2021 | ba a bayyana tsari ba |
| 611 | High Performance CNN (IDSIA, 2011) | ~4.9 × 10⁶ | 2011 | kiyasi | |
| 612 | MobileNet (Google, 2017) | ~4.2 × 10⁶ | 2017 | kiyasi | |
| 613 | NPLM (University of Montreal / Université de Montréal, 2003) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2003 | dense |
| 614 | LMICA (, 2004) | 4.1 × 10⁶ | 4.1 × 10⁶ | 2004 | dense |
| 615 | PoE MNIST (University College London (UCL), 2000) | 3.9 × 10⁶ | 3.9 × 10⁶ | 2000 | dense |
| 616 | Dimensionality Reduction (University of Toronto, 2006) | ~3.8 × 10⁶ | 2006 | kiyasi | |
| 617 | GOAT (DeepMind, 2021) | ~3.5 × 10⁶ | 2021 | kiyasi | |
| 618 | MobileNetV2 (Google, 2018) | ~3.4 × 10⁶ | 2018 | kiyasi | |
| 619 | Big-Little Net (IBM, 2018) | ~3.3 × 10⁶ | 2018 | kiyasi | |
| 620 | DLWP (University of Washington, 2021) | 2.7 × 10⁶ | 2.7 × 10⁶ | 2021 | ba a bayyana tsari ba |
| 621 | BIDAF (University of Washington, 2016) | 2.6 × 10⁶ | 2.6 × 10⁶ | 2016 | dense |
| 622 | DeepStack (University of Alberta, 2017) | ~2.5 × 10⁶ | 2017 | kiyasi | |
| 623 | KataGo (Jane Street, 2019) | ~2.5 × 10⁶ | 2019 | kiyasi | |
| 624 | ShuffleNet v1 (Megvii Inc, 2017) | ~2.4 × 10⁶ | 2017 | kiyasi | |
| 625 | ADAM (University of Amsterdam, 2014) | 2.4 × 10⁶ | 2.4 × 10⁶ | 2014 | dense |
| 626 | StyleGAN3-T (Nvidia, 2021) | 2.2 × 10⁶ | 2.2 × 10⁶ | 2021 | ba a bayyana tsari ba |
| 627 | LSTM+NeuralCache (KU Leuven, 2018) | ~2.1 × 10⁶ | 2018 | kiyasi | |
| 628 | HLBL (University of Toronto, 2008) | 1.8 × 10⁶ | 1.8 × 10⁶ | 2008 | dense |
| 629 | ResNet-110 (Microsoft, 2015) | ~1.7 × 10⁶ | 2015 | kiyasi | |
| 630 | Dueling DQN (Google DeepMind, 2016) | 1.7 × 10⁶ | 1.7 × 10⁶ | 2016 | dense |
| 631 | DQN-2015 (Google, 2015) | ~1.7 × 10⁶ | 2015 | kiyasi | |
| 632 | Rational DQN Average (TU Darmstadt, 2021) | ~1.7 × 10⁶ | 2021 | kiyasi | |
| 633 | IBM-5 (IBM, 1993) | ~1.7 × 10⁶ | 1993 | kiyasi | |
| 634 | Deep Belief Nets (University of Toronto, 2006) | ~1.6 × 10⁶ | 2006 | kiyasi | |
| 635 | IMPALA (DeepMind, 2018) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2018 | dense |
| 636 | StyleGAN3-R (Nvidia, 2021) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2021 | ba a bayyana tsari ba |
| 637 | Deep Multitask NLP Network (NEC Laboratories, 2008) | ~1.5 × 10⁶ | 2008 | kiyasi | |
| 638 | Double DQN (Google DeepMind, 2016) | 1.5 × 10⁶ | 1.5 × 10⁶ | 2016 | dense |
| 639 | CNN committee (IDSIA, 2011) | ~1.4 × 10⁶ | 2011 | kiyasi | |
| 640 | SqueezeNet (DeepScale, 2016) | ~1.2 × 10⁶ | 2016 | kiyasi | |
| 641 | Neocognitron (NHK Broadcasting Science Research Laboratories, 1980) | 1.1 × 10⁶ | 1.1 × 10⁶ | 1980 | dense |
| 642 | CURL (University of California (UC) Berkeley, 2020) | ~9.1 × 10⁵ | 2020 | kiyasi | |
| 643 | DQN (DeepMind, 2013) | 8.4 × 10⁵ | 8.4 × 10⁵ | 2013 | dense |
| 644 | EnhanceNet (Max Planck Institute for Intelligent Systems, 2016) | 8.1 × 10⁵ | 8.1 × 10⁵ | 2016 | dense |
| 645 | Image generation (University of Amsterdam, 2013) | 7.8 × 10⁵ | 7.8 × 10⁵ | 2013 | dense |
| 646 | Hanabi 4 player (DeepMind, 2019) | 7.6 × 10⁵ | 7.6 × 10⁵ | 2019 | dense |
| 647 | TA-CNN (Chinese University of Hong Kong (CUHK), 2014) | ~7.1 × 10⁵ | 2014 | kiyasi | |
| 648 | Robot Parkour (Shanghai Qi Zhi institute, 2023) | 5 × 10⁵ | 5 × 10⁵ | 2023 | ba a bayyana tsari ba |
| 649 | LISSOM (University of Texas at Austin, 1995) | ~4.3 × 10⁵ | 1995 | kiyasi | |
| 650 | SPIDER2 (Griffith University, 2016) | ~4.1 × 10⁵ | 2016 | kiyasi | |
| 651 | Mixture of linear models (, 1994) | ~3.8 × 10⁵ | 1994 | kiyasi | |
| 652 | NPD (IEEE, 2014) | 3.1 × 10⁵ | 3.1 × 10⁵ | 2014 | dense |
| 653 | Pooling CNN (University of Bonn, 2010) | 2.9 × 10⁵ | 2.9 × 10⁵ | 2010 | dense |
| 654 | Pooling CNN (University of Bonn, 2010) | 2.7 × 10⁵ | 2.7 × 10⁵ | 2010 | dense |
| 655 | Two Stage Feature Extraction (New York University (NYU), 2009) | 2.6 × 10⁵ | 2.6 × 10⁵ | 2009 | dense |
| 656 | Vector Space Model (Stanford University, 2011) | 2.5 × 10⁵ | 2.5 × 10⁵ | 2011 | dense |
| 657 | HyperNEAT (University of Texas at Austin, 2014) | ~2.4 × 10⁵ | 2014 | kiyasi | |
| 658 | DNN EM segmentation (IDSIA, 2012) | 2.2 × 10⁵ | 2.2 × 10⁵ | 2012 | dense |
| 659 | Predictive Coding NN (Technical University of Munich, 1994) | 2.1 × 10⁵ | 2.1 × 10⁵ | 1994 | dense |
| 660 | Table Tennis Agent (Google DeepMind, 2024) | ~1.9 × 10⁵ | 2024 | kiyasi | |
| 661 | CNN Committee (IDSIA, 2011) | ~1.3 × 10⁵ | 2011 | kiyasi | |
| 662 | CNN Committee (IDSIA, 2011) | ~1.2 × 10⁵ | 2011 | kiyasi | |
| 663 | RECONTRA-uncategorized (, 1999) | ~1.1 × 10⁵ | 1999 | kiyasi | |
| 664 | BLSTM for handwriting (University of Bern, 2007) | ~1 × 10⁵ | 2007 | kiyasi | |
| 665 | SVM-CNN (New York University (NYU), 2006) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2006 | dense |
| 666 | Invariant CNN (New York University (NYU), 2004) | 9.1 × 10⁴ | 9.1 × 10⁴ | 2004 | dense |
| 667 | SexNet compression (, 1990) | 7.3 × 10⁴ | 7.3 × 10⁴ | 1990 | dense |
| 668 | NeuroChess (, 1994) | ~7.2 × 10⁴ | 1994 | kiyasi | |
| 669 | RECONTRA-categorized (, 1999) | ~6.7 × 10⁴ | 1999 | kiyasi | |
| 670 | LeNet-5 (AT&T, 1998) | 6 × 10⁴ | 6 × 10⁴ | 1998 | dense |
| 671 | Swift (Intel Labs, 2023) | ~5.7 × 10⁴ | 2023 | kiyasi | |
| 672 | TCN (Carnegie Mellon University (CMU), 2018) | 4.2 × 10⁴ | 4.2 × 10⁴ | 2018 | dense |
| 673 | TRPO (University of California (UC) Berkeley, 2015) | 3.4 × 10⁴ | 3.4 × 10⁴ | 2015 | dense |
| 674 | SOM-CNN (, 1997) | 3.2 × 10⁴ | 3.2 × 10⁴ | 1997 | dense |
| 675 | NETtalk reimplementation (Oregon State University, 1990) | 2.7 × 10⁴ | 2.7 × 10⁴ | 1990 | dense |
| 676 | TD-Gammon (IBM, 1992) | ~2.5 × 10⁴ | 1992 | kiyasi | |
| 677 | Cognitron (Biological Cybernetics, 1975) | 2.2 × 10⁴ | 2.2 × 10⁴ | 1975 | dense |
| 678 | NetTalk (Princeton University, 1987) | 1.9 × 10⁴ | 1.9 × 10⁴ | 1987 | dense |
| 679 | CFSS (SenseTime, 2015) | ~1.7 × 10⁴ | 2015 | kiyasi | |
| 680 | MUSIC perceptron (, 1996) | 1.4 × 10⁴ | 1.4 × 10⁴ | 1996 | dense |
| 681 | AdaRNN (Beihang University, 2014) | 1.3 × 10⁴ | 1.3 × 10⁴ | 2014 | dense |
| 682 | Bidirectional RNN (Advanced Telecommunications Research Institute, 1997) | ~1.3 × 10⁴ | 1997 | kiyasi | |
| 683 | Decision tree (Mitsubishi Electric Research Labs, 2001) | ~1.2 × 10⁴ | 2001 | kiyasi | |
| 684 | LSTM (Technical University of Munich, 1997) | 1.1 × 10⁴ | 1.1 × 10⁴ | 1997 | dense |
| 685 | MLN-ASR (McGill University, 1988) | 1 × 10⁴ | 1 × 10⁴ | 1988 | dense |
| 686 | Hopfield network (California Institute of Technology, 1982) | ~9.9 × 10³ | 1982 | kiyasi | |
| 687 | Zip CNN (AT&T, 1989) | 9.8 × 10³ | 9.8 × 10³ | 1989 | dense |
| 688 | Hierarchical Cognitron (NHK Broadcasting Science Research Laboratories, 1984) | ~9.3 × 10³ | 1984 | kiyasi | |
| 689 | Weight Decay (, 1991) | 8.4 × 10³ | 8.4 × 10³ | 1991 | dense |
| 690 | Deep Blue (IBM, 1997) | ~8 × 10³ | 1997 | kiyasi | |
| 691 | Nematode (C. elegans) | 7 × 10³ | 7 × 10³ | synapses | |
| 692 | System 11 (Carnegie Mellon University (CMU), 1996) | 6.5 × 10³ | 6.5 × 10³ | 1996 | dense |
| 693 | ACF-WIDER (Chinese Academy of Sciences, 2014) | 6.1 × 10³ | 6.1 × 10³ | 2014 | dense |
| 694 | RankNet (Microsoft Research, 2005) | 5.7 × 10³ | 5.7 × 10³ | 2005 | dense |
| 695 | ANN Eye Tracker (, 1993) | 5.6 × 10³ | 5.6 × 10³ | 1993 | dense |
| 696 | JPMAX (, 1994) | ~4.4 × 10³ | 1994 | kiyasi | |
| 697 | Kohonen network (Helsinki University of Technology, 1981) | ~4.1 × 10³ | 1981 | kiyasi | |
| 698 | Speaker-independent vowel classification (University of Washington, 1989) | 3 × 10³ | 3 × 10³ | 1989 | dense |
| 699 | Pattern recognition and reading by machine (Sandia Corporation, 1959) | ~2.6 × 10³ | 1959 | kiyasi | |
| 700 | Handwritten digit recognition network (AT&T, 1989) | 2.6 × 10³ | 2.6 × 10³ | 1989 | dense |
| 701 | Boosting (Bell Laboratories, 1992) | ~2.6 × 10³ | 1992 | kiyasi | |
| 702 | Decision tree adaline (Tokyo Medical and Dental University, 1969) | 2.5 × 10³ | 2.5 × 10³ | 1969 | dense |
| 703 | LTE speaker verification system (IBM, 1966) | ~2.1 × 10³ | 1966 | kiyasi | |
| 704 | Ceramic-MLP (Sapienza Università di Roma, 1994) | ~1.9 × 10³ | 1994 | kiyasi | |
| 705 | SexNet classification (, 1990) | ~1.6 × 10³ | 1990 | kiyasi | |
| 706 | RAAM (, 1990) | 1.5 × 10³ | 1.5 × 10³ | 1990 | dense |
| 707 | Super-vector coding (University of Illinois Urbana-Champaign (UIUC), 2010) | ~1 × 10³ | 2010 | kiyasi | |
| 708 | Perceptron Mark I (Cornell Aeronautical Laboratory, 1957) | ~1 × 10³ | 1957 | kiyasi | |
| 709 | Perceptron (Cornell Aeronautical Laboratory, 1960) | ~1 × 10³ | 1960 | kiyasi | |
| 710 | Translation-invariant MLP (Carnegie Mellon University (CMU), 1987) | 8.2 × 10² | 8.2 × 10² | 1987 | gauraye na ƙwararru (ba a buga adadin masu aiki ba) |
| 711 | Truck backer-upper (Stanford University, 1989) | 8.1 × 10² | 8.1 × 10² | 1989 | dense |
| 712 | Siamese-TDNN (Bell Laboratories, 1993) | ~7.4 × 10² | 1993 | kiyasi | |
| 713 | MLP with back-propagation (University of California San Diego, 1986) | 7.2 × 10² | 7.2 × 10² | 1986 | dense |
| 714 | Cancer drug mechanism prediction (National Cancer Institute, 1992) | ~5.9 × 10² | 1992 | kiyasi | |
| 715 | Distributed representation NN (Carnegie Mellon University (CMU), 1986) | 4.3 × 10² | 4.3 × 10² | 1986 | dense |
| 716 | Piecewise linear model (University of Kansas, 1973) | 3.6 × 10² | 3.6 × 10² | 1973 | dense |
| 717 | ASE+ACE (University of Massachusetts Amherst, 1983) | ~3.2 × 10² | 1983 | kiyasi | |
| 718 | LSTM with forget gates (IDSIA, 1999) | ~2.8 × 10² | 1999 | kiyasi | |
| 719 | Self Organizing System (Massachusetts Institute of Technology (MIT), 1955) | ~2.3 × 10² | 1955 | kiyasi | |
| 720 | Theseus (Bell Laboratories, 1950) | 4 × 10¹ | 4 × 10¹ | 1950 | dense |
| 721 | SNARC (Harvard University, 1952) | ~4 × 10¹ | 1952 | kiyasi | |
| 722 | Bankruptcy-NN (, 1990) | 3.6 × 10¹ | 3.6 × 10¹ | 1990 | dense |
| 723 | GNN (University of Siena, 2008) | 3 × 10¹ | 3 × 10¹ | 2008 | dense |
| 724 | ADALINE (Stanford University, 1960) | 1.7 × 10¹ | 1.7 × 10¹ | 1960 | dense |
| 725 | Samuel Neural Checkers (IBM, 1959) | ~1.6 × 10¹ | 1959 | kiyasi |
Gungu
Waɗannan zane-zane suna jera al’ummomin dabbobi, addinai, ƙasashe, jam’iyyu da kamfanoni ta hanyoyi biyu. Ɗaya bisa mambobi. Ɗayan bisa kwatankwacin jijiyoyi, wanda ke auna kowane memba bisa kason lokacin farkawarsa da umarnin ƙungiyar ke jagoranta. Dokokin addini, dokokin gwamnati, ƙa’idodin jam’iyya da bayanin aiki duk ana ƙirga su iri ɗaya. Duk jerin umarni ne, don haka muna auna su da ƙa’ida ɗaya. Yadda ake kiyasin lokacin da aka jagoranta.
- Al’ummar chimpanzee▲17.4 × 10¹¹
- Ƙungiyar tururuwar leafcutter▼16.3 × 10¹¹
- Al’ummar bonobo▲24.4 × 10¹¹
- Sansanin Hadza (ɗan Adam, don kwatantawa)4.1 × 10¹¹
- Ƙungiyar orca ta Bigg’s▼22.6 × 10¹¹
- Matriline na resident orca2.4 × 10¹¹
- Garken baboon8.6 × 10¹⁰
- Ƙawancen dolfin bottlenose▲17.6 × 10¹⁰
- Rukunin zamantakewa na sperm whale▲16.4 × 10¹⁰
- Ƙungiyar kudan zuma▼25.8 × 10¹⁰
- Iyalin giwa5.6 × 10¹⁰
- Garken pinyon jay5.3 × 10¹⁰
- Dangin spotted hyena2.2 × 10¹⁰
- Garken karen daji na Afirka7.2 × 10⁹
- Garken kyarkeci4.2 × 10⁹
- Ƙungiyar meerkat1.7 × 10⁹
- Ƙungiyar naked mole-rat4.1 × 10⁸
108109101010111012neurons
Nuna lambobin
| # | Rukuni | Jijiyoyi a cikin ƙungiyar | A cikin aikin ƙungiya | Mambobi | Rabon da ake jagoranta | Nau’in kira |
|---|---|---|---|---|---|---|
| 1 | Al’ummar chimpanzee | 7.4 × 10¹¹ | 2.5 × 10¹¹ | 100 | 34% | — |
| 2 | Ƙungiyar tururuwar leafcutter | 6.3 × 10¹¹ | 5 × 10¹¹ | Miliyan 2.5 | 80% | — |
| 3 | Al’ummar bonobo | 4.4 × 10¹¹ | 1.9 × 10¹¹ | 59 | 43% | — |
| 4 | Sansanin Hadza (ɗan Adam, don kwatantawa) | 4.1 × 10¹¹ | 2.1 × 10¹¹ | 25 | 53% | — |
| 5 | Ƙungiyar orca ta Bigg’s | 2.6 × 10¹¹ | 2.4 × 10¹¹ | 6 | 91% | — |
| 6 | Matriline na resident orca | 2.4 × 10¹¹ | 1.7 × 10¹¹ | 6 | 70% | 11 |
| 7 | Garken baboon | 8.6 × 10¹⁰ | 5.2 × 10¹⁰ | 30 | 60% | 9 |
| 8 | Ƙawancen dolfin bottlenose | 7.6 × 10¹⁰ | 4.2 × 10¹⁰ | 6 | 55% | — |
| 9 | Rukunin zamantakewa na sperm whale | 6.4 × 10¹⁰ | 4.1 × 10¹⁰ | 8 | 65% | — |
| 10 | Ƙungiyar kudan zuma | 5.8 × 10¹⁰ | 4.9 × 10¹⁰ | 60,000 | 85% | — |
| 11 | Iyalin giwa | 5.6 × 10¹⁰ | 3.2 × 10¹⁰ | 10 | 57% | — |
| 12 | Garken pinyon jay | 5.3 × 10¹⁰ | 3.2 × 10¹⁰ | 100 | 60% | — |
| 13 | Dangin spotted hyena | 2.2 × 10¹⁰ | 6.7 × 10⁹ | 45 | 30% | 11 |
| 14 | Garken karen daji na Afirka | 7.2 × 10⁹ | 4.8 × 10⁹ | 14 | 67% | 11 |
| 15 | Garken kyarkeci | 4.2 × 10⁹ | 2.6 × 10⁹ | 8 | 63% | — |
| 16 | Ƙungiyar meerkat | 1.7 × 10⁹ | 1 × 10⁹ | 15 | 57% | 12 |
| 17 | Ƙungiyar naked mole-rat | 4.1 × 10⁸ | 2.9 × 10⁸ | 78 | 70% | — |
- Kiristanci▲1Biliyan 2.27
- Musulunci▼1Biliyan 2.02
- Addinin HinduBiliyan 1.18
- Addinan jama’aMiliyan 400
- Addinin BuddhaMiliyan 324.19
- Addinan gargajiya na Afirka▲1Miliyan 98.73
- Shinto▲1Miliyan 87.92
- Sikhism▲1Miliyan 25
- Addinin Yahudanci▼3Miliyan 14.78
- Taoism▲1Miliyan 8
- Addinin Baháʼí▲1Miliyan 8
- Falun Gong▲1Miliyan 7
- Jainism▼3Miliyan 4.45
- SpiritismMiliyan 3.8
- Cao ĐàiMiliyan 2.5
- TenrikyoMiliyan 1.12
- Brahma KumarisMiliyan 1
1051061071081091010mambobi
Nuna lambobin
| # | Addini | Mambobi | Yana bin umarninsa | Rabon da ake jagoranta | Sa’o’i a mako |
|---|---|---|---|---|---|
| 1 | Kiristanci | Biliyan 2.27 | 1.7 × 10¹⁸ | 0.88% | 0.9 h |
| 2 | Musulunci | Biliyan 2.02 | 8.9 × 10¹⁸ | 5.1% | 5.4 h |
| 3 | Addinin Hindu | Biliyan 1.18 | 1.6 × 10¹⁸ | 1.6% | 1.6 h |
| 4 | Addinan jama’a | Miliyan 400 | 2 × 10¹⁷ | 0.59% (madadi) | 0.6 h |
| 5 | Addinin Buddha | Miliyan 324.19 | 7.5 × 10¹⁶ | 0.27% | 0.3 h |
| 6 | Addinan gargajiya na Afirka | Miliyan 98.73 | 5 × 10¹⁶ | 0.59% (madadi) | 0.6 h |
| 7 | Shinto | Miliyan 87.92 | 4.5 × 10¹⁶ | 0.59% (madadi) | 0.6 h |
| 8 | Sikhism | Miliyan 25 | 3.7 × 10¹⁶ | 1.7% | 1.8 h |
| 9 | Addinin Yahudanci | Miliyan 14.78 | 5.3 × 10¹⁶ | 4.2% | 4.4 h |
| 10 | Taoism | Miliyan 8 | 4.1 × 10¹⁵ | 0.59% (madadi) | 0.6 h |
| 11 | Addinin Baháʼí | Miliyan 8 | 4.1 × 10¹⁵ | 0.59% (madadi) | 0.6 h |
| 12 | Falun Gong | Miliyan 7 | 3.6 × 10¹⁵ | 0.59% (madadi) | 0.6 h |
| 13 | Jainism | Miliyan 4.45 | 1.8 × 10¹⁶ | 4.6% | 4.8 h |
| 14 | Spiritism | Miliyan 3.8 | 1.9 × 10¹⁵ | 0.59% (madadi) | 0.6 h |
| 15 | Cao Đài | Miliyan 2.5 | 1.3 × 10¹⁵ | 0.59% (madadi) | 0.6 h |
| 16 | Tenrikyo | Miliyan 1.12 | 5.7 × 10¹⁴ | 0.59% (madadi) | 0.6 h |
| 17 | Brahma Kumaris | Miliyan 1 | 5.1 × 10¹⁴ | 0.59% (madadi) | 0.6 h |
- Indiya▲1Biliyan 1.46
- China▼1Biliyan 1.41
- AmurkaMiliyan 341.78
- IndonesiyaMiliyan 285.72
- Pakistan▲5Miliyan 255.22
- Najeriya▲1Miliyan 237.53
- Brazil▼2Miliyan 212.81
- Bangladesh▲5Miliyan 175.69
- Rasha▼3Miliyan 143.51
- Habasha▲7Miliyan 135.47
- Meziko▼2Miliyan 131.95
- Japan▲11Miliyan 123.37
- Masar▼2Miliyan 118.37
- FilifinMiliyan 116.79
- Jamhuriyar Demokuraɗiyyar Kongo▼3Miliyan 112.83
1041061081010mazauna
Nuna lambobin
| # | Ƙasa | Mazauna | Yana bin umarninsa | Rabon da ake jagoranta | Sa’o’i a mako |
|---|---|---|---|---|---|
| 1 | Indiya | Biliyan 1.46 | 2.5 × 10¹⁸ | 2% | 2.1 h |
| 2 | China | Biliyan 1.41 | 5 × 10¹⁸ | 4.1% | 4.3 h |
| 3 | Amurka | Miliyan 341.78 | 1.6 × 10¹⁸ | 5.6% | 5.8 h |
| 4 | Indonesiya | Miliyan 285.72 | 1 × 10¹⁸ | 4.2% | 4.4 h |
| 5 | Pakistan | Miliyan 255.22 | 5.7 × 10¹⁷ | 2.6% | 2.7 h |
| 6 | Najeriya | Miliyan 237.53 | 6.6 × 10¹⁷ | 3.2% | 3.4 h |
| 7 | Brazil | Miliyan 212.81 | 8.7 × 10¹⁷ | 4.8% | 5 h |
| 8 | Bangladesh | Miliyan 175.69 | 4.6 × 10¹⁷ | 3% | 3.1 h |
| 9 | Rasha | Miliyan 143.51 | 7.5 × 10¹⁷ | 6.1% | 6.4 h |
| 10 | Habasha | Miliyan 135.47 | 3.9 × 10¹⁷ | 3.4% | 3.5 h |
| 11 | Meziko | Miliyan 131.95 | 5.9 × 10¹⁷ | 5.2% | 5.5 h |
| 12 | Japan | Miliyan 123.37 | 2.8 × 10¹⁷ | 2.7% | 2.8 h |
| 13 | Masar | Miliyan 118.37 | 5.5 × 10¹⁷ | 5.4% | 5.7 h |
| 14 | Filifin | Miliyan 116.79 | 4.4 × 10¹⁷ | 4.4% | 4.6 h |
| 15 | Jamhuriyar Demokuraɗiyyar Kongo | Miliyan 112.83 | 4.7 × 10¹⁷ | 4.8% | 5.1 h |
| 16 | Vietnam | Miliyan 101.6 | 4.2 × 10¹⁷ | 4.8% | 5 h |
| 17 | Iran | Miliyan 92.42 | 3.5 × 10¹⁷ | 4.3% | 4.6 h |
| 18 | Turkiyya | Miliyan 85.88 | 4.3 × 10¹⁷ | 5.8% | 6 h |
| 19 | Jamus | Miliyan 83.49 | 2.9 × 10¹⁷ | 4% | 4.2 h |
| 20 | Thailand | Miliyan 71.62 | 2.4 × 10¹⁷ | 3.9% | 4.1 h |
| 21 | Tanzaniya | Miliyan 70.55 | 2.2 × 10¹⁷ | 3.6% | 3.8 h |
| 22 | Birtaniya | Miliyan 69.49 | 2.8 × 10¹⁷ | 4.6% | 4.8 h |
| 23 | Faransa | Miliyan 68.72 | 3.1 × 10¹⁷ | 5.3% | 5.5 h |
| 24 | Afirka ta Kudu | Miliyan 64.75 | 2.9 × 10¹⁷ | 5.1% | 5.4 h |
| 25 | Italiya | Miliyan 58.92 | 2.1 × 10¹⁷ | 4.2% | 4.4 h |
| 26 | Kenya | Miliyan 57.53 | 2.3 × 10¹⁷ | 4.7% | 5 h |
| 27 | Myanmar | Miliyan 54.85 | 1.9 × 10¹⁷ | 4% | 4.2 h |
| 28 | Kolombiya | Miliyan 53.43 | 1.7 × 10¹⁷ | 3.7% | 3.9 h |
| 29 | Koriya ta Kudu | Miliyan 51.68 | 1.4 × 10¹⁷ | 3% | 3.2 h |
| 30 | Sudan | Miliyan 51.66 | 1.5 × 10¹⁷ | 3.4% | 3.6 h |
| 31 | Uganda | Miliyan 51.38 | 1.5 × 10¹⁷ | 3.4% | 3.6 h |
| 32 | Sifaniya | Miliyan 49.36 | 1.8 × 10¹⁷ | 4.2% | 4.3 h |
| 33 | Aljeriya | Miliyan 47.44 | 3.2 × 10¹⁷ | 7.9% | 8.3 h |
| 34 | Iraƙi | Miliyan 47.02 | 1.1 × 10¹⁷ | 2.8% | 2.9 h |
| 35 | Ajantina | Miliyan 45.85 | 1.7 × 10¹⁷ | 4.4% | 4.6 h |
| 36 | Afghanistan | Miliyan 43.84 | 1.8 × 10¹⁷ | 4.8% | 5 h |
| 37 | Yemen | Miliyan 41.77 | 1.3 × 10¹⁷ | 3.7% | 3.9 h |
| 38 | Kanada | Miliyan 41.65 | 2 × 10¹⁷ | 5.5% | 5.8 h |
| 39 | Angola | Miliyan 39.04 | 1.4 × 10¹⁷ | 4.2% | 4.4 h |
| 40 | Ukraine | Miliyan 38.98 | 2.1 × 10¹⁷ | 6.2% | 6.5 h |
| 41 | Maroko | Miliyan 38.43 | 1.5 × 10¹⁷ | 4.5% | 4.7 h |
| 42 | Uzbekistan | Miliyan 37.05 | 2 × 10¹⁷ | 6.3% | 6.6 h |
| 43 | Saudiyya | Miliyan 36.97 | 1.4 × 10¹⁷ | 4.3% | 4.6 h |
| 44 | Poland | Miliyan 36.44 | 2.1 × 10¹⁷ | 6.7% | 7 h |
| 45 | Malaysia | Miliyan 35.98 | 1.8 × 10¹⁷ | 5.8% | 6.1 h |
| 46 | Mozambique | Miliyan 35.63 | 1.5 × 10¹⁷ | 4.8% | 5.1 h |
| 47 | Ghana | Miliyan 35.06 | 1.2 × 10¹⁷ | 3.9% | 4.1 h |
| 48 | Peru | Miliyan 34.58 | 1.4 × 10¹⁷ | 4.8% | 5 h |
| 49 | Madagaska | Miliyan 32.74 | 9.4 × 10¹⁶ | 3.3% | 3.5 h |
| 50 | Côte d’Ivoire | Miliyan 32.71 | 9.5 × 10¹⁶ | 3.4% | 3.5 h |
| 51 | Kamaru | Miliyan 29.88 | 9.8 × 10¹⁶ | 3.8% | 4 h |
| 52 | Nepal | Miliyan 29.62 | 1.1 × 10¹⁷ | 4.2% | 4.4 h |
| 53 | Venezuela | Miliyan 28.52 | 1.5 × 10¹⁷ | 6.1% | 6.4 h |
| 54 | Nijar | Miliyan 27.92 | 6.4 × 10¹⁶ | 2.6% | 2.8 h |
| 55 | Ostiraliya | Miliyan 27.61 | 1.1 × 10¹⁷ | 4.7% | 5 h |
| 56 | Koriya ta Arewa | Miliyan 26.57 | 6.6 × 10¹⁷ | 29% | 30.1 h |
| 57 | Siriya | Miliyan 25.62 | 9.8 × 10¹⁶ | 4.4% | 4.6 h |
| 58 | Mali | Miliyan 25.2 | 4.9 × 10¹⁶ | 2.3% | 2.4 h |
| 59 | Burkina Faso | Miliyan 24.07 | 5.7 × 10¹⁶ | 2.8% | 2.9 h |
| 60 | Taiwan | Miliyan 23.31 | 1 × 10¹⁷ | 5% | 5.3 h |
| 61 | Malawi | Miliyan 22.22 | 1 × 10¹⁷ | 5.4% | 5.6 h |
| 62 | Zambiya | Miliyan 21.91 | 10 × 10¹⁶ | 5.3% | 5.5 h |
| 63 | Sri Lanka | Miliyan 21.76 | 1.1 × 10¹⁷ | 6% | 6.3 h |
| 64 | Cadi | Miliyan 21 | 4.9 × 10¹⁶ | 2.7% | 2.9 h |
| 65 | Kazakhstan | Miliyan 20.84 | 1.4 × 10¹⁷ | 7.6% | 7.9 h |
| 66 | Chile | Miliyan 19.86 | 6.2 × 10¹⁶ | 3.6% | 3.8 h |
| 67 | Somaliya | Miliyan 19.65 | 1.5 × 10¹⁶ | 0.88% | 0.9 h |
| 68 | Romaniya | Miliyan 19.02 | 7.5 × 10¹⁶ | 4.6% | 4.8 h |
| 69 | Senegal | Miliyan 18.93 | 5.9 × 10¹⁶ | 3.6% | 3.8 h |
| 70 | Guatemala | Miliyan 18.69 | 6 × 10¹⁶ | 3.7% | 3.9 h |
| 71 | Ecuador | Miliyan 18.29 | 6.6 × 10¹⁶ | 4.2% | 4.4 h |
| 72 | Netherlands | Miliyan 18.09 | 7.6 × 10¹⁶ | 4.9% | 5.1 h |
| 73 | Kambodiya | Miliyan 17.85 | 7.4 × 10¹⁶ | 4.8% | 5.1 h |
| 74 | Zimbabwe | Miliyan 16.95 | 6.1 × 10¹⁶ | 4.2% | 4.4 h |
| 75 | Gini | Miliyan 15.1 | 3.1 × 10¹⁶ | 2.4% | 2.5 h |
| 76 | Benin | Miliyan 14.81 | 4.4 × 10¹⁶ | 3.4% | 3.6 h |
| 77 | Ruwanda | Miliyan 14.57 | 7 × 10¹⁶ | 5.6% | 5.8 h |
| 78 | Burundi | Miliyan 14.39 | 5.2 × 10¹⁶ | 4.2% | 4.4 h |
| 79 | Bolibiya | Miliyan 12.58 | 5.8 × 10¹⁶ | 5.4% | 5.6 h |
| 80 | Tunisiya | Miliyan 12.35 | 6.4 × 10¹⁶ | 6% | 6.3 h |
| 81 | Sudan ta Kudu | Miliyan 12.19 | 4.4 × 10¹⁶ | 4.2% | 4.4 h |
| 82 | Belgiyom | Miliyan 11.94 | 4.5 × 10¹⁶ | 4.3% | 4.5 h |
| 83 | Haiti | Miliyan 11.91 | 1.9 × 10¹⁶ | 1.8% | 1.9 h |
| 84 | Jordan | Miliyan 11.52 | 4.9 × 10¹⁶ | 5% | 5.2 h |
| 85 | Jamhuriyar Dominica | Miliyan 11.52 | 4.9 × 10¹⁶ | 4.9% | 5.2 h |
| 86 | Haɗaɗɗiyar Daular Larabawa | Miliyan 11.51 | 3.1 × 10¹⁶ | 3.1% | 3.2 h |
| 87 | Honduras | Miliyan 11.01 | 3.2 × 10¹⁶ | 3.3% | 3.5 h |
| 88 | Cuba | Miliyan 10.94 | 1.6 × 10¹⁷ | 17% | 17.4 h |
| 89 | Czechia | Miliyan 10.89 | 5.8 × 10¹⁶ | 6.2% | 6.5 h |
| 90 | Portugal | Miliyan 10.8 | 5.1 × 10¹⁶ | 5.5% | 5.7 h |
| 91 | Tajikistan | Miliyan 10.79 | 5.3 × 10¹⁶ | 5.7% | 6 h |
| 92 | Papua New Guinea | Miliyan 10.76 | 4.3 × 10¹⁶ | 4.6% | 4.8 h |
| 93 | Sweden | Miliyan 10.6 | 6.8 × 10¹⁶ | 7.5% | 7.8 h |
| 94 | Girka | Miliyan 10.41 | 4.9 × 10¹⁶ | 5.4% | 5.7 h |
| 95 | Azerbaijan | Miliyan 10.25 | 6.2 × 10¹⁶ | 7% | 7.4 h |
| 96 | Isra’ila | Miliyan 10.12 | 6.4 × 10¹⁶ | 7.3% | 7.7 h |
| 97 | Hungary | Miliyan 9.51 | 5.7 × 10¹⁶ | 6.9% | 7.3 h |
| 98 | Austriya | Miliyan 9.21 | 2.5 × 10¹⁶ | 3.2% | 3.4 h |
| 99 | Switzerland | Miliyan 9.09 | 3.9 × 10¹⁶ | 5% | 5.3 h |
| 100 | Belarus | Miliyan 9.09 | 7.9 × 10¹⁶ | 10% | 10.6 h |
| 101 | Saliyo | Miliyan 8.82 | 4.9 × 10¹⁶ | 6.5% | 6.8 h |
| 102 | Togo | Miliyan 8.59 | 3 × 10¹⁶ | 4% | 4.2 h |
| 103 | Laos | Miliyan 7.87 | 3.2 × 10¹⁶ | 4.8% | 5 h |
| 104 | Turkmenistan | Miliyan 7.62 | 4.2 × 10¹⁶ | 6.5% | 6.8 h |
| 105 | Libya | Miliyan 7.46 | 3.9 × 10¹⁶ | 6.1% | 6.4 h |
| 106 | Kyrgyzstan | Miliyan 7.34 | 4.1 × 10¹⁶ | 6.5% | 6.8 h |
| 107 | Paraguay | Miliyan 7.01 | 2.9 × 10¹⁶ | 4.8% | 5.1 h |
| 108 | Nicaragua | Miliyan 7.01 | 2.8 × 10¹⁶ | 4.6% | 4.8 h |
| 109 | Serbia | Miliyan 6.55 | 4.1 × 10¹⁶ | 7.2% | 7.5 h |
| 110 | Jamhuriyar Kongo | Miliyan 6.48 | 1.7 × 10¹⁶ | 3% | 3.1 h |
| 111 | Bulgariya | Miliyan 6.43 | 3.3 × 10¹⁶ | 5.9% | 6.2 h |
| 112 | El Salvador | Miliyan 6.37 | 3.2 × 10¹⁶ | 5.8% | 6.1 h |
| 113 | Singapore | Miliyan 6.11 | 1.9 × 10¹⁶ | 3.6% | 3.7 h |
| 114 | Denmark | Miliyan 6.01 | 3.9 × 10¹⁶ | 7.6% | 8 h |
| 115 | Lebanon | Miliyan 5.85 | 1.5 × 10¹⁶ | 3% | 3.1 h |
| 116 | Liberiya | Miliyan 5.73 | 1.5 × 10¹⁶ | 3.1% | 3.3 h |
| 117 | Finland | Miliyan 5.65 | 3.2 × 10¹⁶ | 6.6% | 6.9 h |
| 118 | Norway | Miliyan 5.61 | 3.7 × 10¹⁶ | 7.6% | 8 h |
| 119 | Jamhuriyar Afirka ta Tsakiya | Miliyan 5.51 | 1.7 × 10¹⁶ | 3.5% | 3.7 h |
| 120 | Oman | Miliyan 5.49 | 2.9 × 10¹⁶ | 6.2% | 6.5 h |
| 121 | Ireland | Miliyan 5.48 | 3.4 × 10¹⁶ | 7.2% | 7.6 h |
| 122 | Slovakia | Miliyan 5.41 | 3.1 × 10¹⁶ | 6.7% | 7 h |
| 123 | Falasɗinu | Miliyan 5.41 | 2 × 10¹⁶ | 4.3% | 4.5 h |
| 124 | New Zealand | Miliyan 5.32 | 2.7 × 10¹⁶ | 5.9% | 6.2 h |
| 125 | Mauritaniya | Miliyan 5.32 | 2 × 10¹⁶ | 4.3% | 4.5 h |
| 126 | Costa Rica | Miliyan 5.15 | 2.6 × 10¹⁶ | 5.9% | 6.2 h |
| 127 | Kuwait | Miliyan 4.87 | 2.7 × 10¹⁶ | 6.5% | 6.8 h |
| 128 | Panama | Miliyan 4.57 | 2.3 × 10¹⁶ | 5.9% | 6.2 h |
| 129 | Georgia | Miliyan 3.94 | 2.1 × 10¹⁶ | 6.1% | 6.4 h |
| 130 | Kuroshiya | Miliyan 3.88 | 2.2 × 10¹⁶ | 6.7% | 7 h |
| 131 | Eritrea | Miliyan 3.61 | 1.5 × 10¹⁶ | 5% | 5.2 h |
| 132 | Mongoliya | Miliyan 3.57 | 2.2 × 10¹⁶ | 7% | 7.3 h |
| 133 | Uruguay | Miliyan 3.38 | 1.7 × 10¹⁶ | 5.9% | 6.2 h |
| 134 | Bosnia da Herzegovina | Miliyan 3.14 | 1.6 × 10¹⁶ | 5.9% | 6.2 h |
| 135 | Namibiya | Miliyan 3.09 | 1.8 × 10¹⁶ | 6.9% | 7.2 h |
| 136 | Armeniya | Miliyan 3.09 | 1.7 × 10¹⁶ | 6.5% | 6.8 h |
| 137 | Katar | Miliyan 2.97 | 9.6 × 10¹⁵ | 3.8% | 3.9 h |
| 138 | Lithuania | Miliyan 2.89 | 1.8 × 10¹⁶ | 7.4% | 7.7 h |
| 139 | Jamaika | Miliyan 2.84 | 1.2 × 10¹⁶ | 5% | 5.2 h |
| 140 | Gambiya | Miliyan 2.82 | 1.1 × 10¹⁶ | 4.5% | 4.7 h |
| 141 | Gabon | Miliyan 2.59 | 9.9 × 10¹⁵ | 4.4% | 4.6 h |
| 142 | Botswana | Miliyan 2.56 | 1.3 × 10¹⁶ | 5.7% | 6 h |
| 143 | Lesotho | Miliyan 2.36 | 9.5 × 10¹⁵ | 4.7% | 4.9 h |
| 144 | Moldova | Miliyan 2.36 | 1.5 × 10¹⁶ | 7.3% | 7.6 h |
| 145 | Albaniya | Miliyan 2.35 | 1.2 × 10¹⁶ | 5.7% | 6 h |
| 146 | Gini-Bissau | Miliyan 2.25 | 5.7 × 10¹⁵ | 2.9% | 3.1 h |
| 147 | Slovenia | Miliyan 2.13 | 1.2 × 10¹⁶ | 6.6% | 6.9 h |
| 148 | Equatorial Guinea | Miliyan 1.94 | 4 × 10¹⁵ | 2.4% | 2.5 h |
| 149 | Latviya | Miliyan 1.85 | 1.2 × 10¹⁶ | 7.6% | 7.9 h |
| 150 | North Macedonia | Miliyan 1.82 | 9.2 × 10¹⁵ | 5.9% | 6.2 h |
| 151 | Baharen | Miliyan 1.6 | 5.9 × 10¹⁵ | 4.3% | 4.5 h |
| 152 | Kosovo | Miliyan 1.58 | 8 × 10¹⁵ | 5.9% | 6.2 h |
| 153 | Timor-Leste | Miliyan 1.42 | 8 × 10¹⁵ | 6.6% | 6.9 h |
| 154 | Cyprus | Miliyan 1.37 | 5.5 × 10¹⁵ | 4.7% | 4.9 h |
| 155 | Trinidad da Tobago | Miliyan 1.37 | 9.3 × 10¹⁵ | 7.9% | 8.2 h |
| 156 | Estoniya | Miliyan 1.37 | 8.3 × 10¹⁵ | 7% | 7.4 h |
| 157 | Eswatini | Miliyan 1.26 | 7.5 × 10¹⁵ | 6.9% | 7.2 h |
| 158 | Mauritius | Miliyan 1.24 | 5.1 × 10¹⁵ | 4.8% | 5 h |
| 159 | Djibouti | Miliyan 1.18 | 4.6 × 10¹⁵ | 4.5% | 4.8 h |
| 160 | Fiji | 933,154 | 5.6 × 10¹⁵ | 7% | 7.4 h |
| 161 | Comoros | 882,847 | 3.5 × 10¹⁵ | 4.6% | 4.8 h |
| 162 | Tsibirin Solomon | 838,645 | 3.2 × 10¹⁵ | 4.4% | 4.6 h |
| 163 | Guyana | 835,986 | 4.6 × 10¹⁵ | 6.4% | 6.7 h |
| 164 | Bhutan | 796,682 | 5.8 × 10¹⁵ | 8.5% | 8.9 h |
| 165 | Luxembourg | 686,970 | 2.4 × 10¹⁵ | 4% | 4.2 h |
| 166 | Suriname | 639,850 | 3.7 × 10¹⁵ | 6.7% | 7.1 h |
| 167 | Montenegro | 623,129 | 4.3 × 10¹⁵ | 8.1% | 8.5 h |
| 168 | Malta | 579,704 | 3.1 × 10¹⁵ | 6.2% | 6.5 h |
| 169 | Maldives | 529,676 | 4.7 × 10¹⁵ | 10% | 10.8 h |
| 170 | Cape Verde | 527,326 | 3.3 × 10¹⁵ | 7.2% | 7.6 h |
| 171 | Brunei | 466,330 | 3.6 × 10¹⁵ | 8.9% | 9.3 h |
| 172 | Belize | 422,924 | 1.1 × 10¹⁵ | 2.9% | 3.1 h |
| 173 | Bahamas | 403,033 | 2.2 × 10¹⁵ | 6.4% | 6.7 h |
| 174 | Iceland | 392,404 | 2.5 × 10¹⁵ | 7.4% | 7.7 h |
| 175 | Vanuatu | 335,169 | 2 × 10¹⁵ | 6.9% | 7.3 h |
| 176 | Barbados | 282,623 | 1.5 × 10¹⁵ | 6% | 6.3 h |
| 177 | São Tomé da Príncipe | 240,254 | 1.1 × 10¹⁵ | 5.4% | 5.7 h |
| 178 | Samoa | 219,306 | 1.1 × 10¹⁵ | 5.7% | 5.9 h |
| 179 | Saint Lucia | 180,149 | 9.3 × 10¹⁴ | 6% | 6.3 h |
| 180 | Kiribati | 136,488 | 7.6 × 10¹⁴ | 6.4% | 6.7 h |
| 181 | Seychelles | 122,730 | 8.9 × 10¹⁴ | 8.4% | 8.8 h |
| 182 | Grenada | 117,303 | 7.3 × 10¹⁴ | 7.2% | 7.5 h |
| 183 | Micronesia | 113,683 | 5.7 × 10¹⁴ | 5.8% | 6.1 h |
| 184 | Tonga | 103,742 | 5.9 × 10¹⁴ | 6.6% | 6.9 h |
| 185 | Saint Vincent da Grenadines | 99,924 | 4.9 × 10¹⁴ | 5.7% | 5.9 h |
| 186 | Antigua da Barbuda | 94,209 | 6.5 × 10¹⁴ | 8% | 8.3 h |
| 187 | Andorra | 82,904 | 3.9 × 10¹⁴ | 5.5% | 5.8 h |
| 188 | Dominica | 65,871 | 2.3 × 10¹⁴ | 4.1% | 4.3 h |
| 189 | Saint Kitts da Nevis | 46,922 | 2.1 × 10¹⁴ | 5.2% | 5.5 h |
| 190 | Liechtenstein | 41,024 | 2 × 10¹⁴ | 5.7% | 5.9 h |
| 191 | Monaco | 38,341 | 2.3 × 10¹⁴ | 6.9% | 7.3 h |
| 192 | Tsibirin Marshall | 36,282 | 3.3 × 10¹⁴ | 10% | 10.9 h |
| 193 | San Marino | 34,109 | 1.3 × 10¹⁴ | 4.6% | 4.8 h |
| 194 | Palau | 17,663 | 1.6 × 10¹⁴ | 10% | 11 h |
| 195 | Nauru | 12,025 | 9.3 × 10¹³ | 9% | 9.4 h |
| 196 | Tuvalu | 9,492 | 8 × 10¹³ | 9.8% | 10.2 h |
- Bharatiya Janata Party (Indiya)▲1Miliyan 140
- Jam’iyyar Kwaminisanci ta China▼1Miliyan 101.29
- Democratic Party (Amurka)▲13Miliyan 44.9
- Republican Party (Amurka)▲16Miliyan 38
- Indian National Congress▼2Miliyan 26
- AIADMK (Indiya)▼2Miliyan 20.04
- DMK (Indiya)▼2Miliyan 20
- Prosperity Party (Habasha)▼1Miliyan 15.7
- TVK (Indiya)▼1Miliyan 15
- Chama Cha Mapinduzi (Tanzaniya)▼1Miliyan 13
- All Progressives Congress (Najeriya)▼1Miliyan 12.9
- AK Party (Turkiyya)▼1Miliyan 11.71
- Morena (Meziko)▼1Miliyan 11.63
- Telugu Desam Party (Indiya)▼1Miliyan 10.05
- Cambodian People’s Party▼1Miliyan 7
104105106107108109mambobi
Nuna lambobin
| # | Jam’iyya | Mambobi | Yana bin umarninsa | Rabon da ake jagoranta | Sa’o’i a mako |
|---|---|---|---|---|---|
| 1 | Bharatiya Janata Party (Indiya) | Miliyan 140 | 6.3 × 10¹⁶ | 0.52% (madadi) | 0.6 h |
| 2 | Jam’iyyar Kwaminisanci ta China | Miliyan 101.29 | 8.2 × 10¹⁶ | 0.94% | 1 h |
| 3 | Democratic Party (Amurka) | Miliyan 44.9 | 2.3 × 10¹⁵ | 0.06% | 0.1 h |
| 4 | Republican Party (Amurka) | Miliyan 38 | 2 × 10¹⁵ | 0.06% | 0.1 h |
| 5 | Indian National Congress | Miliyan 26 | 1.2 × 10¹⁶ | 0.52% (madadi) | 0.6 h |
| 6 | AIADMK (Indiya) | Miliyan 20.04 | 9 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 7 | DMK (Indiya) | Miliyan 20 | 9 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 8 | Prosperity Party (Habasha) | Miliyan 15.7 | 7 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 9 | TVK (Indiya) | Miliyan 15 | 6.7 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 10 | Chama Cha Mapinduzi (Tanzaniya) | Miliyan 13 | 5.8 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 11 | All Progressives Congress (Najeriya) | Miliyan 12.9 | 5.8 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 12 | AK Party (Turkiyya) | Miliyan 11.71 | 5.2 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 13 | Morena (Meziko) | Miliyan 11.63 | 5.2 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 14 | Telugu Desam Party (Indiya) | Miliyan 10.05 | 4.5 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 15 | Cambodian People’s Party | Miliyan 7 | 3.1 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 16 | Workers’ Party of Korea | Miliyan 6.18 | 7.6 × 10¹⁵ | 1.4% | 1.5 h |
| 17 | FRELIMO (Mozambique) | Miliyan 6 | 2.7 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 18 | Jam’iyyar Kwaminisanci ta Vietnam | Miliyan 5.6 | 2.1 × 10¹⁵ | 0.44% | 0.5 h |
| 19 | Democratic Party of Korea | Miliyan 5 | 2.2 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 20 | People Power Party (Koriya ta Kudu) | Miliyan 4.44 | 2 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 21 | PSUV (Venezuela) | Miliyan 4.24 | 1.9 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 22 | ZANU-PF (Zimbabwe) | Miliyan 4.06 | 1.8 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 23 | UMNO (Malaysia) | Miliyan 3.25 | 1.5 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 24 | Justicialist Party (Ajantina) | Miliyan 3.1 | 1.4 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 25 | Colorado Party (Paraguay) | Miliyan 3 | 1.3 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 26 | United Russia | Miliyan 2.6 | 1.2 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 27 | UzLiDeP (Uzbekistan) | Miliyan 2.31 | 1 × 10¹⁵ | 0.52% (madadi) | 0.6 h |
| 28 | PLD (Jamhuriyar Dominica) | Miliyan 2.18 | 9.7 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 29 | MDB (Brazil) | Miliyan 2.02 | 9 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 30 | CHP (Turkiyya) | Miliyan 1.88 | 8.4 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 31 | Radical Civic Union (Ajantina) | Miliyan 1.79 | 8 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 32 | Workers’ Party, PT (Brazil) | Miliyan 1.67 | 7.5 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 33 | PRM (Jamhuriyar Dominica) | Miliyan 1.55 | 6.9 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 34 | Authentic Radical Liberal Party (Paraguay) | Miliyan 1.55 | 6.9 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 35 | Partido Federal ng Pilipinas | Miliyan 1.5 | 6.7 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 36 | PAS (Malaysia) | Miliyan 1.3 | 5.8 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 37 | PRD (Brazil) | Miliyan 1.29 | 5.8 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 38 | Progressistas (Brazil) | Miliyan 1.29 | 5.8 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 39 | PSDB (Brazil) | Miliyan 1.26 | 5.6 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 40 | PKR (Malaysia) | Miliyan 1.15 | 5.2 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 41 | Economic Freedom Fighters (Afirka ta Kudu) | Miliyan 1.09 | 4.9 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 42 | PDT (Brazil) | Miliyan 1.07 | 4.8 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 43 | União Brasil | Miliyan 1.07 | 4.8 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 44 | CPI(M) (Indiya) | Miliyan 1.02 | 4.6 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 45 | Liberal Democratic Party (Japan) | Miliyan 1 | 4.5 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 46 | FLN (Aljeriya) | Miliyan 1 | 4.5 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 47 | Liberal Party, PL (Brazil) | 952,881 | 4.3 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 48 | Jam’iyyar Kwaminisanci ta Cuba | 700,000 | 2.7 × 10¹⁴ | 0.44% | 0.5 h |
| 49 | African National Congress | 688,454 | 3.1 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 50 | Conservative Party of Canada | 678,708 | 2.7 × 10¹⁴ | 0.46% | 0.5 h |
| 51 | CDU (Jamus) | 364,202 | 1.6 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 52 | SPD (Jamus) | 357,116 | 1.6 × 10¹⁴ | 0.52% (madadi) | 0.6 h |
| 53 | Labour Party (Birtaniya) | 333,235 | 1.6 × 10¹⁴ | 0.55% | 0.6 h |
| 54 | Liberal Party of Canada | 163,836 | 6.5 × 10¹³ | 0.46% | 0.5 h |
| 55 | Greens (Jamus) | 155,296 | 7 × 10¹³ | 0.52% (madadi) | 0.6 h |
| 56 | CSU (Jamus) | 125,153 | 5.6 × 10¹³ | 0.52% (madadi) | 0.6 h |
| 57 | Conservative Party (Birtaniya) | 123,000 | 3.7 × 10¹³ | 0.35% | 0.4 h |
| 58 | New Democratic Party (Kanada) | 100,542 | 4 × 10¹³ | 0.46% | 0.5 h |
| 59 | AfD (Jamus) | 51,335 | 2.3 × 10¹³ | 0.52% (madadi) | 0.6 h |
- WalmartMiliyan 2.1
- Amazon▲1Miliyan 1.58
- State Grid▼1Miliyan 1.36
- China National PetroleumMiliyan 1.03
- JD.com951,420
- BYD▲1869,622
- Accenture▼1779,000
- Allied Universal▲3770,000
- China Post Group▼1719,448
- Russian Railways (RZD)▲2677,363
- Volkswagen Group▲6662,942
- Foxconn (Hon Hai)▼2633,167
- Schwarz Group▲16604,000
- Compass Group▲18590,000
- Tata Consultancy Services▼6584,510
00.5M1M1.5M2M2.5Memployees
Nuna lambobin
| # | Kamfani | Ma’aikata | Yana bin umarninsa | Sa’o’i a mako | Rabon da ake jagoranta |
|---|---|---|---|---|---|
| 1 | Walmart | Miliyan 2.1 | 6.3 × 10¹⁶ | 36.3 h (Amurka) | 35% |
| 2 | Amazon | Miliyan 1.58 | 4.7 × 10¹⁶ | 36.3 h (Amurka) | 35% |
| 3 | State Grid | Miliyan 1.36 | 5.5 × 10¹⁶ | 49 h (China) | 47% |
| 4 | China National Petroleum | Miliyan 1.03 | 4.1 × 10¹⁶ | 49 h (China) | 47% |
| 5 | JD.com | 951,420 | 3.8 × 10¹⁶ | 49 h (China) | 47% |
| 6 | BYD | 869,622 | 3.5 × 10¹⁶ | 49 h (China) | 47% |
| 7 | Accenture | 779,000 | 3.7 × 10¹⁶ | 57.58 h (Indiya) | 55% |
| 8 | Allied Universal | 770,000 | 2.3 × 10¹⁶ | 36.52 h (Amurka) | 35% |
| 9 | China Post Group | 719,448 | 2.9 × 10¹⁶ | 49 h (China) | 47% |
| 10 | Russian Railways (RZD) | 677,363 | 2.2 × 10¹⁶ | 39.96 h (Rasha) | 38% |
| 11 | Volkswagen Group | 662,942 | 2 × 10¹⁶ | 35.95 h (Jamus) | 34% |
| 12 | Foxconn (Hon Hai) | 633,167 | 2.5 × 10¹⁶ | 49 h (China) | 47% |
| 13 | Schwarz Group | 604,000 | 1.6 × 10¹⁶ | 31.49 h (Jamus) | 30% |
| 14 | Compass Group | 590,000 | 1.5 × 10¹⁶ | 31.7 h (Amurka) | 30% |
| 15 | Tata Consultancy Services | 584,510 | 2.8 × 10¹⁶ | 58.23 h (Indiya) | 56% |
| 16 | DHL Group | 583,998 | 1.7 × 10¹⁶ | 35.81 h (Jamus) | 34% |
| 17 | Sinopec | 513,434 | 2.1 × 10¹⁶ | 49 h (China) | 47% |
| 18 | Gazprom | 501,000 | 1.7 × 10¹⁶ | 40.47 h (Rasha) | 39% |
| 19 | FedEx | 474,200 | 1.6 × 10¹⁶ | 39.98 h (Amurka) | 38% |
| 20 | Home Depot | 470,000 | 1.4 × 10¹⁶ | 36.3 h (Amurka) | 35% |
| 21 | China Mobile | 468,496 | 1.9 × 10¹⁶ | 49 h (China) | 47% |
| 22 | UPS | 460,000 | 1.5 × 10¹⁶ | 39.98 h (Amurka) | 38% |
| 23 | Agricultural Bank of China | 457,835 | 1.8 × 10¹⁶ | 49 h (China) | 47% |
| 24 | Concentrix | 455,000 | 1.7 × 10¹⁶ | 46.71 h (Filifin) | 45% |
| 25 | Teleperformance | 446,716 | 2.2 × 10¹⁶ | 58.89 h (Indiya) | 56% |
| 26 | Jardine Matheson | 443,000 | 1.5 × 10¹⁶ | 41.93 h (Indonesiya) | 40% |
| 27 | Sodexo | 426,464 | 1.1 × 10¹⁶ | 31.7 h (Amurka) | 30% |
| 28 | Capgemini | 423,400 | 2 × 10¹⁶ | 58.23 h (Indiya) | 56% |
| 29 | Reliance Industries | 419,911 | 2.1 × 10¹⁶ | 59.57 h (Indiya) | 57% |
| 30 | Edeka | 417,500 | 1.1 × 10¹⁶ | 31.49 h (Jamus) | 30% |
| 31 | Luxshare Precision | 416,197 | 1.7 × 10¹⁶ | 49 h (China) | 47% |
| 32 | Target | 415,000 | 1.2 × 10¹⁶ | 36.3 h (Amurka) | 35% |
| 33 | Marriott International | 414,000 | 1.1 × 10¹⁶ | 31.7 h (Amurka) | 30% |
| 34 | Bosch | 412,400 | 1.2 × 10¹⁶ | 35.95 h (Jamus) | 34% |
| 35 | ICBC | 409,758 | 1.6 × 10¹⁶ | 49 h (China) | 47% |
| 36 | Kroger | 403,000 | 1.2 × 10¹⁶ | 36.3 h (Amurka) | 35% |
| 37 | China Resources | 397,398 | 1.6 × 10¹⁶ | 49 h (China) | 47% |
| 38 | Magnit | 394,000 | 1.3 × 10¹⁶ | 39.69 h (Rasha) | 38% |
| 39 | Toyota | 390,927 | 1.3 × 10¹⁶ | 39.6 h (Japan) | 38% |
| 40 | UnitedHealth Group | 390,000 | 1.2 × 10¹⁶ | 36.87 h (Amurka) | 35% |
| 41 | X5 Group | 389,000 | 1.3 × 10¹⁶ | 39.69 h (Rasha) | 38% |
| 42 | Berkshire Hathaway | 387,800 | 1.2 × 10¹⁶ | 37.75 h (Amurka) | 36% |
| 43 | AVIC | 384,000 | 1.5 × 10¹⁶ | 49 h (China) | 47% |
| 44 | Ahold Delhaize | 384,000 | 1.1 × 10¹⁶ | 36.3 h (Amurka) | 35% |
| 45 | China Telecom | 381,436 | 1.5 × 10¹⁶ | 49 h (China) | 47% |
| 46 | Starbucks | 381,000 | 9.9 × 10¹⁵ | 31.7 h (Amurka) | 30% |
| 47 | Rewe Group | 380,000 | 9.8 × 10¹⁵ | 31.49 h (Jamus) | 30% |
| 48 | China Construction Bank | 378,344 | 1.5 × 10¹⁶ | 49 h (China) | 47% |
| 49 | TJX Companies | 377,000 | 1.1 × 10¹⁶ | 36.3 h (Amurka) | 35% |
| 50 | China State Construction Engineering | 370,272 | 1.5 × 10¹⁶ | 49 h (China) | 47% |
| 51 | FEMSA | 368,776 | 1.4 × 10¹⁶ | 47.09 h (Meziko) | 45% |
| 52 | Japan Post Holdings | 360,000 | 1.2 × 10¹⁶ | 41.6 h (Japan) | 40% |
| 53 | Tesco | 354,744 | 9.3 × 10¹⁵ | 31.81 h (Birtaniya) | 30% |
| 54 | Cognizant | 351,600 | 1.7 × 10¹⁶ | 58.23 h (Indiya) | 56% |
| 55 | NTT | 344,196 | 1.1 × 10¹⁶ | 40.2 h (Japan) | 38% |
| 56 | Costco | 341,000 | 1 × 10¹⁶ | 36.3 h (Amurka) | 35% |
| 57 | Infosys | 328,594 | 1.6 × 10¹⁶ | 58.23 h (Indiya) | 56% |
| 58 | ISS | 325,000 | 1.6 × 10¹⁶ | 58.89 h (Indiya) | 56% |
| 59 | Securitas | 322,000 | 9.7 × 10¹⁵ | 36.52 h (Amurka) | 35% |
| 60 | HCA Healthcare | 320,000 | 9.7 × 10¹⁵ | 36.87 h (Amurka) | 35% |
| 61 | Rosneft | 320,000 | 1.1 × 10¹⁶ | 40.47 h (Rasha) | 39% |
| 62 | JPMorgan Chase | 318,512 | 1.1 × 10¹⁶ | 40.14 h (Amurka) | 38% |
| 63 | Siemens | 318,000 | 9.4 × 10¹⁵ | 35.95 h (Jamus) | 34% |
| 64 | Bank of China | 313,746 | 1.3 × 10¹⁶ | 49 h (China) | 47% |
| 65 | Walgreens Boots Alliance | 312,000 | 9.3 × 10¹⁵ | 36.3 h (Amurka) | 35% |
| 66 | PepsiCo | 306,000 | 1 × 10¹⁶ | 40.7 h (Amurka) | 39% |
| 67 | China Railway Construction (CRCC) | 304,927 | 1.2 × 10¹⁶ | 49 h (China) | 47% |
| 68 | CHN Energy (China Energy Investment) | 303,418 | 1.2 × 10¹⁶ | 49 h (China) | 47% |
| 69 | China Railway Group (CREC) | 302,527 | 1.2 × 10¹⁶ | 49 h (China) | 47% |
| 70 | CK Hutchison | 300,000 | 8.6 × 10¹⁵ | 35.08 h (Birtaniya) | 33% |
| 71 | CVS Health | 300,000 | 8.9 × 10¹⁵ | 36.3 h (Amurka) | 35% |
Dabbobi da sauran halittu
Daga ƙwayoyin halitta guda ɗaya marasa jijiyoyi zuwa kwakwalwar ɗan Adam. Ana auna adadin jijiyoyi inda suke.
Babu jijiyoyi
- Escherichia coliEscherichia coli0 an aunaƘwayar halitta ɗaya da ba ta da jijiyoyi. Tana karkata zuwa abinci kuma tana da ƙwaƙwalwar sinadari da take ɗaukar daƙiƙa kaɗan.
- Slime mouldPhysarum polycephalum0 an aunaBabbar ƙwayar halitta ɗaya da ba ta da jijiyoyi. Tana warware hanyoyin ruɗani kuma tana ƙirƙirar hanyoyin sufuri masu inganci.
- Shuka mai hankaliMimosa pudica0 an aunaShuka da ganyenta suke naɗewa idan an taɓa. Wani bincike ya ba da rahoton cewa tana koyon yin watsi da taɓawa marar lahani. Sake nazari na baya ya yi jayayya da hakan.
- SosoPorifera (e.g. Amphimedon queenslandica)0 an aunaDabba marar jijiyoyi ko synapses da har yanzu take matse duka jikinta ta hanyar haɗin kai.
Raga na jijiyoyi, ba kwakwalwa
- HydraHydra vulgariskusan 3,000 zuwa 5,000 kiyasiƘaramin cnidarian na ruwan zaki. Ana sarrafa shi da raga mai yaɗuwa na jijiyoyi kuma ba shi da kwakwalwa.
- Box jellyfishTripedalia cystophoraBabu ƙidayar da aka bugaBox jellyfish ba shi da kwakwalwa ta tsakiya, amma ya nuna koyon haɗa abubuwa.
Dabbobi marasa ƙashin baya masu kwakwalwa
- Tsutsar zagayeCaenorhabditis elegans302 an aunaTsutsa mai tsawon kusan milimita ɗaya da jijiyoyi 302 daidai. Ita ce dabba ta farko da aka zana cikakken zane na wayoyinta.
- Ƙudan ’ya’yan itaceDrosophila melanogaster139,255 an aunaMafi girman cikakken zane na wayoyin kwakwalwa da aka zana zuwa yanzu, yana rufe kusan jijiyoyi 139,000.
- Kudan zumaApis melliferakusan miliyan 1 kiyasiKusan jijiyoyi miliyan ɗaya, kuma kudan zuma na iya koyon ra’ayoyin tunani kamar iri ɗaya da dabam.
- Tururuwa (Camponotus / Formica)Camponotus spp.Babu ƙidayar da aka bugaƘaramar kwakwalwa da take sarrafa tafiya mai nisa ta ƙidayar matakai (path integration) da tuna alamomin ƙasa.
- Gizo-gizo mai tsalle PortiaPortia fimbriata / labiataBabu ƙidayar da aka bugaGizo-gizo mai tsalle da yake tsara kewayen hanya. Yana bi hanyoyin da suke nesa da abin farautarsa kuma daga gani.
- Kaguwar bakin teku / crayfishCarcinus maenas; Procambarus / Astacus spp.Babu ƙidayar da aka bugaKaguwa da crayfish. Bita na gwamnati na 2021 ya sami shaida mai ƙarfi cewa kaguwa na gaske suna iya ji.
- Octopus na gama gariOctopus vulgarismiliyan 500 kiyasiYawancin jijiyoyinsa suna cikin hannayensa. Bita na 2021 ya sami shaida mai ƙarfi sosai cewa yana iya ji.
Kifaye da dabbobin ruwa da ƙasa
- ZebrafishDanio rerioBabu ƙidayar da aka bugaƘaramin kifi da ake amfani da shi sosai a neuroscience. Ba a ƙidaya jijiyoyinsa gaba ɗaya ba. Kwakwalwar babba tana da kusan ƙwayoyin halitta miliyan 10.
- Cleaner wrasseLabroides dimidiatusBabu ƙidayar da aka bugaKifin duwatsun murjani da ya ci wani nau’in gwajin alamar madubi. Mutane ba su yarda da abin da hakan yake nunawa ba.
- KwaɗoAnura (e.g. Xenopus / Rana)Babu ƙidayar da aka bugaSanarwar New York ta 2024 ta ce akwai yiwuwar gaske na ƙwarewar sani a cikin dabbobin ruwa da ƙasa.
Tsuntsaye
- Kurciya ta dutseColumba liviamiliyan 310 an aunaƊaruruwan miliyoyin jijiyoyi a ƙaramar kwakwalwa. Kurciyoyi suna iya rarraba hotuna masu sarƙaƙiya zuwa rukuni.
- Hankaka na gama gariCorvus coraxbiliyan 2.17 an aunaFiye da jijiyoyi biliyan biyu, a cunkushe. Hankaka suna tunawa da abokan gasa ɗaya-ɗaya.
- Hankaka na New CaledoniaCorvus moneduloidesBabu ƙidayar da aka bugaYana yin kayan aiki masu ƙugiya daga sanduna da waya. Ba a ƙidaya jijiyoyinsa ba.
- Aku launin toka na AfirkaPsittacus erithacusbiliyan 1.57 an aunaKusan jijiyoyi biliyan 1.6. Shekaru goma-goma na nazarin dakin gwaji kan ƙidaya da rarrabawa.
Mammals
- BeraMus musculusmiliyan 70.9 an aunaKusan jijiyoyi miliyan 71. Beraye suna nuna fuskokin ciwo da za a iya aunawa.
- BeraRattus norvegicusmiliyan 200 an aunaKusan jijiyoyi miliyan 200. Beraye suna sakin abokan keji da aka kama.
- Kyanwa ta gidaFelis catusbiliyan 1.22 an aunaKusan jijiyoyi miliyan 250 a cerebral cortex.
- Kare na gidaCanis familiarisbiliyan 1.8 zuwa 2.6, gwargwadon kare an aunaHar zuwa kusan jijiyoyin cortex miliyan 600, gwargwadon kare. Wannan ya fi kyanwa.
- Alade na gidaSus scrofa domesticusbiliyan 2.22 an aunaAladu suna koyon abin da madubi yake nunawa kuma suna amfani da shi don samun abinci.
- Dolfin bottlenoseTursiops truncatusBabu ƙidayar da aka bugaYana cin gwajin gane kai a madubi. Babu ƙidayar jijiyoyi da aka auna ga dolfin bottlenose.
- Giwar AfirkaLoxodonta africanabiliyan 257 an aunaJijiyoyi biliyan 257, sau uku fiye da ɗan Adam, kusan duka a cerebellum.
- ChimpanzeePan troglodyteskusan biliyan 28 (an hasasshe daga ƙa’idojin girma, ba a ƙidaya ba) kiyasiƊaya daga cikin dangi biyu mafi kusa da mu da suke raye (tare da bonobo). Yana cin gwajin madubi.
- Ɗan AdamHomo sapiensbiliyan 86.1 an aunaJijiyoyi biliyan 86, biliyan 16 daga cikinsu a cerebral cortex. Mu kaɗai ne nau’in da membobinsa za su iya ba da rahoton baka na ƙwarewarsu.
Tsarin gamayya
Ƙungiyoyin da suke yanke shawarwarin da babu memba ɗaya da zai yanke shi kaɗai. Ana nazarin su a matsayin hankalin gamayya, ba a matsayin hankula ba.
- Ƙungiyoyin tururuwaƘungiyoyi suna zaɓar gida mafi kyau domin kowace tururuwa tana bin iyakar quorum. Babu tururuwa da take kwatanta wuraren gaba ɗaya.
- Garken kudan zumaGarken kudan zuma suna zaɓar mafi kyawun rami ta hanyar gasa tsakanin masu bincike da quorum. Siginar tsayawa tana ba da hana-juna, wanda ke karya tsaiko.
- Garken starlingKowane tsuntsu yana bin maƙwabta tabbatattu (kusan 6-7), ba kowane tsuntsu a cikin tazara tabbatacciya ba. Ana kiran wannan mu’amalar topological, ba metric ba.
- Garken kifayeGarken kifaye suna amfani da dokokin quorum don yanke shawara. Manyan ƙungiyoyi na iya jin canjin muhalli da babu kifi ɗaya da zai iya ji.
Tsarin wucin gadi
Nau’ikan hankalin injuna, an tsara su bisa yadda suke aiki. Ba a ɗaukar wani tsari na yanzu a matsayin ɗan takara mai ƙarfi na sani ba.
- Tsarin ƙwararru na tushen ƙa’idaShirye-shiryen da suke rubuta ilimin ƙwararren fanni a matsayin ƙa’idodin idan-to bayyanannu. Injin ƙarshe yana amfani da ƙa’idodin a jere don kaiwa ga ƙarshe, yawanci tare da bayanin dalilinsa.
- Injunan neman bishiyar wasa (chess)Shirye-shiryen da suke lissafa jerin motsi masu yiwuwa zuwa zurfi tabbatacce kuma suna ba da maki ga matsayin da suka samu da aikin kimantawa da aka gina da hannu. Suna yanke rassan da ba za su canza zaɓin ba (minimax tare da alpha-beta pruning).
- Wakilan koyon ƙarfafawa mai zurfiHanyoyin sadarwa na jijiyoyi da aka horar ta gwaji da kuskure don ƙara siginar lada. Suna koyon manufa ko aikin ƙima ta hulɗa da muhalli, sau da yawa na kwaikwayo kamar wasa.
- Manyan samfuran harsheHanyoyin sadarwa na jijiyoyi na Transformer da aka horar don hasashen token na gaba a manyan tarin rubutu, sannan sau da yawa a gyara su da ra’ayin ɗan Adam.
- Robobi masu jikiInjuna masu na’urorin ji da masu aiki da suke aiki a duniyar zahiri. Sau da yawa suna amfani da sarrafawa mai amsawa a matakai maimakon samfurin duniya na tsakiya, don haka hali yana fitowa daga madauki tsakanin jiki da muhalli.
- Kayan aiki na neuromorphicChips da da’irorinsu suke kwaikwayon jijiyoyi masu harbi da synapses. Suna aiki ne da abin da ya faru kuma suna amfani da ƙarancin wuta, ba kamar tsarin na’urar sarrafawa da ma’adana na yau da kullum ba.
Hankula na hasashe
Hankula da aka gabatar a rubuce-rubucen kimiyya da falsafa. Babu wanda ya taɓa lura da ɗaya.
- Kwaikwayon kwakwalwa gaba ɗayaba a lura baDuba kwakwalwa dalla-dalla da gudanar da samfurin software nata.
- Hankulan kwaikwayo a cikin kwaikwayon kakanniba a lura baMatsalar uku ta Bostrom: ko dai kusan babu wata wayewa da take kaiwa mataki bayan ɗan Adam, ko kusan babu wadda take gudanar da kwaikwayon kakanni, ko kuma kusan tabbas muna rayuwa a ɗaya.
- Kwakwalwar Boltzmannba a lura baMasu kallo masu kama da kwakwalwa da suke fitowa daga sauye-sauye na zafi ko quantum da ba kasafai ba.
- Ɗora hankali a kwamfuta ta maye gurbin a hankaliba a lura baMaye gurbin jijiyoyi ɗaya bayan ɗaya da sassan wucin gadi masu aiki iri ɗaya.
- Superintelligenceba a lura baWani hankali na hasashe da ya zarce aikin ɗan Adam a kusan dukan fannoni. Littafin Bostrom yana nazarin hanyoyin zuwa gare shi (kwaikwayon kwakwalwa gaba ɗaya, fahimta ta halitta, AI), siffofinsa da matsalolin sarrafawa.
Sigar bayanai 2.0.0. Zazzage (JSON).