Entities · minds and possible minds, sorted by evidence
If the world is a simulation, minds may be some of the hardest things in it to compute. This guide lists the known cases, from single cells to humans. It also covers the artificial systems built so far, and the minds that scientists and philosophers have proposed but nobody has observed.
Every figure has a source and a confidence tag. measured is a direct count, estimate is worked out from other data, and not counted means nobody has published a count. Whether any of these systems has experiences is a separate question. The evidence for that is under sentience criteria and consciousness indicators.
Individual minds
- Orca †43.1 billion
- Long-finned pilot whale †37.2 billion
- Human▲116.3 billion
- Harbour porpoise †14.9 billion
- Bottlenose dolphin †12.7 billion
- Cuvier’s beaked whale9.1 billion
- Gorilla8.5 billion
- Orangutan8.5 billion
- Chimpanzee7.4 billion
- Common dolphin6.7 billion
- Harp seal6.1 billion
- African elephant▼15.6 billion
- Fin whale5.2 billion
- Blue whale5.1 billion
- Minke whale3.2 billion
10610710810910101011neurons
Show the numbers
| # | Animal | Cortex or pallium | Whole brain |
|---|---|---|---|
| 1 | Orca † | 43.1 billion | |
| 2 | Long-finned pilot whale † | 37.2 billion | |
| 3 | Human | 16.3 billion | 86.1 billion |
| 4 | Harbour porpoise † | 14.9 billion | |
| 5 | Bottlenose dolphin † | 12.7 billion | |
| 6 | Cuvier’s beaked whale | 9.1 billion | |
| 7 | Gorilla | 8.5 billion | |
| 8 | Orangutan | 8.5 billion | |
| 9 | Chimpanzee | 7.4 billion | |
| 10 | Common dolphin | 6.7 billion | |
| 11 | Harp seal | 6.1 billion | |
| 12 | African elephant | 5.6 billion | 257 billion |
| 13 | Fin whale | 5.2 billion | |
| 14 | Blue whale | 5.1 billion | |
| 15 | Minke whale | 3.2 billion | 57.4 billion |
| 16 | Olive baboon | 2.88 billion | 10.9 billion |
| 17 | Beluga | 2.7 billion | |
| 18 | Pygmy sperm whale | 2.02 billion | |
| 19 | Blue-and-yellow macaw | 1.92 billion | 3.14 billion |
| 20 | Rhesus macaque | 1.71 billion | 6.39 billion |
| 21 | Giraffe | 1.67 billion | 11.2 billion |
| 22 | Squirrel monkey | 1.34 billion | 3.25 billion |
| 23 | Eurasian eagle-owl | 1.33 billion | 1.9 billion |
| 24 | Kea | 1.28 billion | 2.15 billion |
| 25 | Snowy owl | 1.27 billion | 1.81 billion |
| 26 | Common raven | 1.2 billion | 2.17 billion |
| 27 | Tawny owl | 1.15 billion | 1.58 billion |
| 28 | Capuchin monkey | 1.14 billion | 3.69 billion |
| 29 | Sulphur-crested cockatoo | 1.14 billion | 2.12 billion |
| 30 | African grey parrot | 850 million | 1.57 billion |
| 31 | Rook | 820 million | 1.51 billion |
| 32 | Greater kudu | 793 million | 6.09 billion |
| 33 | Long-eared owl | 673 million | 991 million |
| 34 | Goffin's cockatoo | 599 million | 1.16 billion |
| 35 | Lion | 545 million | 4.67 billion |
| 36 | White-tailed eagle | 543 million | 1.65 billion |
| 37 | Eurasian jay | 529 million | 1.09 billion |
| 38 | Domestic dog | 526 million | |
| 39 | Striped hyena | 495 million | 3.88 billion |
| 40 | Western jackdaw | 492 million | 968 million |
| 41 | Common ostrich | 479 million | 1.63 billion |
| 42 | Eurasian magpie | 443 million | 897 million |
| 43 | Saker falcon | 443 million | 1.2 billion |
| 44 | Emu | 439 million | 1.34 billion |
| 45 | Raccoon | 438 million | 2.15 billion |
| 46 | Barn owl | 437 million | 690 million |
| 47 | Little owl | 401 million | 586 million |
| 48 | Black vulture | 399 million | 1.01 billion |
| 49 | Eurasian pygmy owl | 364 million | 533 million |
| 50 | Common buzzard | 352 million | 1 billion |
| 51 | Greater rhea | 330 million | 1.03 billion |
| 52 | Mute swan | 324 million | 1 billion |
| 53 | Domestic pig | 293 million | 2.22 billion |
| 54 | Greylag goose | 259 million | 738 million |
| 55 | Brown bear | 251 million | 9.59 billion |
| 56 | Domestic cat | 250 million | 1.22 billion |
| 57 | Common marmoset | 245 million | 638 million |
| 58 | Common kestrel | 238 million | 625 million |
| 59 | Eurasian teal | 167 million | 375 million |
| 60 | Budgerigar | 149 million | 322 million |
| 61 | Red-breasted goose | 149 million | 483 million |
| 62 | Wood duck | 138 million | 306 million |
| 63 | Pygmy falcon | 132 million | 350 million |
| 64 | Marbled duck | 130 million | 387 million |
| 65 | Indian peafowl | 130 million | 571 million |
| 66 | Yellow-knobbed curassow | 125 million | 653 million |
| 67 | Victoria crowned pigeon | 118 million | 579 million |
| 68 | Mallard | 112 million | 367 million |
| 69 | Wild turkey | 106 million | 493 million |
| 70 | Eurasian sparrowhawk | 87.8 million | 402 million |
| 71 | Reeves’s pheasant | 80.7 million | 387 million |
| 72 | Rock pigeon | 72 million | 310 million |
| 73 | European rabbit | 71.5 million | 513 million |
| 74 | Red junglefowl | 60.7 million | 221 million |
| 75 | Chilean tinamou | 59.1 million | 241 million |
| 76 | Zebra finch | 55.2 million | 136 million |
| 77 | Elegant crested tinamou | 51.4 million | 218 million |
| 78 | Common wood pigeon | 51.3 million | 259 million |
| 79 | Quebracho crested tinamou | 48.3 million | 236 million |
| 80 | African collared dove | 40 million | 139 million |
| 81 | Northern bobwhite | 39.1 million | 148 million |
| 82 | Grey partridge | 36.9 million | 170 million |
| 83 | Rat | 31 million | 200 million |
| 84 | Common quail | 22.6 million | 118 million |
| 85 | King quail | 20.5 million | 80.5 million |
| 86 | Diamond dove | 18.2 million | 87.9 million |
| 87 | Mouse | 13.7 million | 70.9 million |
| 88 | Naked mole-rat | 5.31 million | 33.6 million |
- Human neocortex1.6 × 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 or synapses
Show the numbers
| # | System | All parameters | Used per token | Year | Kind |
|---|---|---|---|---|---|
| 1 | Human neocortex | 1.6 × 10¹⁴ | 1.6 × 10¹⁴ | synapses | |
| 2 | GPT-5.5 (OpenAI, 2026) * | ~4.7 × 10¹² | 2026 | estimate | |
| 3 | Claude Fable 5 (Anthropic, 2026) * | ~3.5 × 10¹² | 2026 | estimate | |
| 4 | Grok 3 (xAI, 2025) | ~3 × 10¹² | 2025 | estimate | |
| 5 | Grok 4 (xAI, 2025) | ~3 × 10¹² | 2025 | estimate | |
| 6 | Gemini 2.5 Pro (Google, 2025) * | ~3 × 10¹² | 2025 | estimate | |
| 7 | Kimi K3 (Moonshot, 2026) | ~2.8 × 10¹² | 2026 | estimate | |
| 8 | Qwen 3.8 Max (Alibaba, 2026) | 2.4 × 10¹² | 2.4 × 10¹² | 2026 | architecture not stated |
| 9 | Qwen3.8-2.4T-A95B (Alibaba, 2026) | 2.4 × 10¹² | 9.5 × 10¹⁰ | 2026 | mixture of experts |
| 10 | Grok 4.7 (xAI, 2026) * | ~2.1 × 10¹² | 2026 | estimate | |
| 11 | Llama 4 Behemoth (Meta AI, 2025) | ~2 × 10¹² | 2025 | estimate | |
| 12 | GPT-4 (OpenAI, 2023) | ~1.8 × 10¹² | 2023 | estimate | |
| 13 | DeepSeek-V4-Pro (DeepSeek, 2026) | ~1.6 × 10¹² | 2026 | estimate | |
| 14 | LongCat-2.0 (Meituan Inc, 2026) | ~1.6 × 10¹² | 2026 | estimate | |
| 15 | Switch (Google, 2021) | 1.6 × 10¹² | 1.6 × 10¹² | 2021 | mixture of experts (active count unpublished) |
| 16 | GLaM (Google, 2021) | 1.2 × 10¹² | 1.2 × 10¹² | 2021 | mixture of experts (active count unpublished) |
| 17 | PanGu-Σ (Huawei Noah's Ark Lab, 2023) | 1.1 × 10¹² | 1.1 × 10¹² | 2023 | mixture of experts (active count unpublished) |
| 18 | Kimi K2.5 (Moonshot, 2026) | ~1 × 10¹² | 2026 | estimate | |
| 19 | Composer 2 (Cursor, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | mixture of experts (active count unpublished) |
| 20 | Kimi K2.6 (Moonshot, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | mixture of experts (active count unpublished) |
| 21 | Composer 2.5 (Cursor, 2026) | ~1 × 10¹² | 2026 | estimate | |
| 22 | MiMo-V2-Pro (Xiaomi Corp, 2026) | 1 × 10¹² | 1 × 10¹² | 2026 | architecture not stated |
| 23 | MiMo-V2.5-Pro (Xiaomi Corp, 2026) | ~1 × 10¹² | 2026 | estimate | |
| 24 | M6-T (Alibaba, 2021) | ~1 × 10¹² | 2021 | estimate | |
| 25 | Kimi K2 (Moonshot, 2025) | 1 × 10¹² | 1 × 10¹² | 2025 | mixture of experts (active count unpublished) |
| 26 | Qwen3-Max (Alibaba, 2025) | ~1 × 10¹² | 2025 | estimate | |
| 27 | Ling-1T (Ant Group, 2025) | 1 × 10¹² | 5 × 10¹⁰ | 2025 | mixture of experts |
| 28 | Kimi K2 Thinking (Moonshot, 2025) | ~1 × 10¹² | 2025 | estimate | |
| 29 | Inkling (Thinking Machines, 2026) | ~9.8 × 10¹¹ | 2026 | estimate | |
| 30 | Tencent Hy4 preview (Tencent, 2026) | ~7.7 × 10¹¹ | 2026 | estimate | |
| 31 | GLM-5.1 (Z.ai (Zhipu AI), 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | mixture of experts (active count unpublished) |
| 32 | K-EXAONE 2.0 (LG AI Research, 2026) | 7.5 × 10¹¹ | 7.5 × 10¹¹ | 2026 | mixture of experts (active count unpublished) |
| 33 | DeepSeek V4.1 Flash (DeepSeek, 2026) | ~7.5 × 10¹¹ | 2026 | estimate | |
| 34 | GLM-5 (Z.ai (Zhipu AI), 2026) | ~7.4 × 10¹¹ | 2026 | estimate | |
| 35 | GLM-5.3 (Z.ai (Zhipu AI), 2026) | 7.4 × 10¹¹ | 4 × 10¹⁰ | 2026 | mixture of experts |
| 36 | A.X K2 (SK Telecom, 2026) | 6.9 × 10¹¹ | 6.9 × 10¹¹ | 2026 | mixture of experts (active count unpublished) |
| 37 | DeepSeekMath-V2 (DeepSeek, 2025) | 6.8 × 10¹¹ | 6.8 × 10¹¹ | 2025 | mixture of experts (active count unpublished) |
| 38 | Mistral Large 3 (Mistral AI, 2025) | 6.8 × 10¹¹ | 4.1 × 10¹⁰ | 2025 | mixture of experts |
| 39 | DeepSeek-V3 (DeepSeek, 2024) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2024 | mixture of experts |
| 40 | DeepSeek-R1 (DeepSeek, 2025) | 6.7 × 10¹¹ | 3.7 × 10¹⁰ | 2025 | mixture of experts |
| 41 | SenseChat 5.5 (SenseTime, 2024) | 6 × 10¹¹ | 6 × 10¹¹ | 2024 | architecture not stated |
| 42 | Step 5 Preview (StepFun, 2026) | ~6 × 10¹¹ | 2026 | estimate | |
| 43 | PaLM-E (Google, 2023) | ~5.6 × 10¹¹ | 2023 | estimate | |
| 44 | Hunyuan-TurboS (Tencent, 2025) | 5.6 × 10¹¹ | 5.6 × 10¹¹ | 2025 | mixture of experts (active count unpublished) |
| 45 | LongCat-Flash (Meituan Inc, 2025) | ~5.6 × 10¹¹ | 2025 | estimate | |
| 46 | Nemotron 3 Ultra (Nvidia, 2026) | ~5.5 × 10¹¹ | 2026 | estimate | |
| 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 | estimate | |
| 54 | A.X K1 (SK Telecom, 2025) | 5.2 × 10¹¹ | 5.2 × 10¹¹ | 2025 | mixture of experts (active count unpublished) |
| 55 | Doubao-pro (ByteDance, 2024) | ~5 × 10¹¹ | 2024 | estimate | |
| 56 | Grok 4.20 (xAI, 2026) | ~5 × 10¹¹ | 2026 | estimate | |
| 57 | Qwen3-Coder-480B-A35B (Alibaba, 2025) | 4.8 × 10¹¹ | 3.5 × 10¹⁰ | 2025 | mixture of experts |
| 58 | MiniMax-M3 (MiniMax, 2026) | 4.3 × 10¹¹ | 2.2 × 10¹⁰ | 2026 | mixture of experts |
| 59 | ERNIE-4.5-VL-424B-A47B (Baidu, 2025) | 4.2 × 10¹¹ | 4.7 × 10¹⁰ | 2025 | mixture of experts |
| 60 | Llama 3.1-405B (Meta AI, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | dense |
| 61 | Llama 4 Maverick (Meta AI, 2025) | ~4 × 10¹¹ | 2025 | estimate | |
| 62 | Jamba 1.5-Large (AI21 Labs, 2024) | 4 × 10¹¹ | 4 × 10¹¹ | 2024 | mixture of experts (active count unpublished) |
| 63 | Qwen3.5 397B-A17B (Alibaba, 2026) | ~4 × 10¹¹ | 2026 | estimate | |
| 64 | Hunyuan-Large (Tencent, 2024) | 3.9 × 10¹¹ | 3.9 × 10¹¹ | 2024 | mixture of experts (active count unpublished) |
| 65 | GLM-4.7 (Z.ai (Zhipu AI), 2025) | ~3.6 × 10¹¹ | 2025 | estimate | |
| 66 | GLM-4.6 (Z.ai (Zhipu AI), 2025) | ~3.6 × 10¹¹ | 2025 | estimate | |
| 67 | GLM-4.5 (Z.ai (Zhipu AI), 2025) | 3.5 × 10¹¹ | 3.2 × 10¹⁰ | 2025 | mixture of experts |
| 68 | PaLM 2 (Google, 2023) | ~3.4 × 10¹¹ | 2023 | estimate | |
| 69 | Med-PaLM 2 (Google Research, 2023) | ~3.4 × 10¹¹ | 2023 | estimate | |
| 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 | estimate | |
| 72 | Grok-1 (xAI, 2023) | ~3.1 × 10¹¹ | 2023 | estimate | |
| 73 | Motif-3 (Motif Technologies, 2026) | 3.1 × 10¹¹ | 3.1 × 10¹¹ | 2026 | mixture of experts (active count unpublished) |
| 74 | SB-LM (Google, 2007) | ~3 × 10¹¹ | 2007 | estimate | |
| 75 | Tencent Hy3 preview (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | mixture of experts (active count unpublished) |
| 76 | Tencent Hy3 (Tencent, 2026) | 3 × 10¹¹ | 3 × 10¹¹ | 2026 | mixture of experts (active count unpublished) |
| 77 | DeepSeek-V4-Flash (DeepSeek, 2026) | ~2.8 × 10¹¹ | 2026 | estimate | |
| 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 | mixture of experts (active count unpublished) |
| 80 | ST-MoE (Google, 2022) | ~2.7 × 10¹¹ | 2022 | estimate | |
| 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 | estimate | |
| 83 | Aramco Metabrain AI (Saudi Aramco, 2024) | ~2.5 × 10¹¹ | 2024 | estimate | |
| 84 | Yuan 1.0 (Inspur, 2021) | 2.5 × 10¹¹ | 2.5 × 10¹¹ | 2021 | architecture not stated |
| 85 | DeepSeek-Coder-V2 236B (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | mixture of experts (active count unpublished) |
| 86 | DeepSeek-V2.5 (DeepSeek, 2024) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2024 | mixture of experts (active count unpublished) |
| 87 | K-EXAONE (LG AI Research, 2025) | 2.4 × 10¹¹ | 2.4 × 10¹¹ | 2025 | architecture not stated |
| 88 | Qwen3-235B-A22B (Alibaba, 2025) | ~2.4 × 10¹¹ | 2025 | estimate | |
| 89 | Qwen3-235B-A22B-Thinking (Alibaba, 2025) | ~2.4 × 10¹¹ | 2025 | estimate | |
| 90 | P1-235B-A22B (Shanghai AI Lab, 2025) | ~2.4 × 10¹¹ | 2025 | estimate | |
| 91 | Seed-1.6-Thinking (ByteDance, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | mixture of experts (active count unpublished) |
| 92 | MiniMax-M2 (MiniMax, 2025) | 2.3 × 10¹¹ | 1 × 10¹⁰ | 2025 | mixture of experts |
| 93 | MiniMax-M2.1 (MiniMax, 2025) | 2.3 × 10¹¹ | 2.3 × 10¹¹ | 2025 | mixture of experts (active count unpublished) |
| 94 | HyperCLOVA 204B (NAVER, 2021) | ~2 × 10¹¹ | 2021 | estimate | |
| 95 | Amazon Titan (Amazon, 2023) | ~2 × 10¹¹ | 2023 | estimate | |
| 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 | architecture not stated |
| 98 | BLOOM-176B (Hugging Face, 2022) | 1.8 × 10¹¹ | 1.8 × 10¹¹ | 2022 | dense |
| 99 | BLUUMI (University of Turku, 2023) | ~1.8 × 10¹¹ | 2023 | estimate | |
| 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 | estimate | |
| 103 | GPT-3 175B (OpenAI, 2020) | 1.7 × 10¹¹ | 1.7 × 10¹¹ | 2020 | dense |
| 104 | Palmyra X 004 (Writer, 2024) | ~1.5 × 10¹¹ | 2024 | estimate | |
| 105 | FLAN 137B (Google Research, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | architecture not stated |
| 106 | LaMDA (Google, 2022) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2022 | architecture not stated |
| 107 | MEB (Microsoft, 2021) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2021 | architecture not stated |
| 108 | Pangu Ultra (Huawei, 2025) | 1.4 × 10¹¹ | 1.4 × 10¹¹ | 2025 | architecture not stated |
| 109 | DBRX (Databricks, 2024) | 1.3 × 10¹¹ | 1.3 × 10¹¹ | 2024 | mixture of experts (active count unpublished) |
| 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 | architecture not stated |
| 112 | Mistral Large 2 (Mistral AI, 2024) | ~1.2 × 10¹¹ | 2024 | estimate | |
| 113 | Qwen3.5-122B-A10B (Alibaba, 2026) | ~1.2 × 10¹¹ | 2026 | estimate | |
| 114 | Galactica (Meta AI, 2022) | ~1.2 × 10¹¹ | 2022 | estimate | |
| 115 | Nemotron 3 Super (Nvidia, 2026) | ~1.2 × 10¹¹ | 2026 | estimate | |
| 116 | gpt-oss-120b (OpenAI, 2025) | 1.2 × 10¹¹ | 5.1 × 10⁹ | 2025 | mixture of experts |
| 117 | Telechat2-115B (China Telecom, 2024) | 1.1 × 10¹¹ | 1.1 × 10¹¹ | 2024 | architecture not stated |
| 118 | Kaiju Large (Character.ai, 2025) | ~1.1 × 10¹¹ | 2025 | estimate | |
| 119 | Llama 4 Scout (Meta AI, 2025) | ~1.1 × 10¹¹ | 2025 | estimate | |
| 120 | InternLM (Shanghai AI Lab, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | dense |
| 121 | Solar Open 100B (Upstage, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | architecture not stated |
| 122 | DLRM-2020 (Facebook AI, 2019) | 1 × 10¹¹ | 1 × 10¹¹ | 2019 | dense |
| 123 | xTrimoPGLM -100B (Tsinghua University, 2023) | 1 × 10¹¹ | 1 × 10¹¹ | 2023 | architecture not stated |
| 124 | Yi-Large (01.AI, 2024) | ~1 × 10¹¹ | 2024 | estimate | |
| 125 | VAETKI (NC AI, 2025) | 1 × 10¹¹ | 1 × 10¹¹ | 2025 | mixture of experts (active count unpublished) |
| 126 | ESM3 (EvolutionaryScale, 2024) | 9.8 × 10¹⁰ | 9.8 × 10¹⁰ | 2024 | architecture not stated |
| 127 | Mouse neocortex | 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 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 138 | NVLM-H 72B (Nvidia, 2024) | ~7.2 × 10¹⁰ | 2024 | estimate | |
| 139 | NVLM-D 72B (Nvidia, 2024) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2024 | architecture not stated |
| 140 | MindLink-72B (Kunlun Inc., 2025) | 7.2 × 10¹⁰ | 7.2 × 10¹⁰ | 2025 | architecture not stated |
| 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 | estimate | |
| 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 | architecture not stated |
| 146 | Llama 3.3 70B (Meta AI, 2024) | 7 × 10¹⁰ | 7 × 10¹⁰ | 2024 | dense |
| 147 | Reka Core (Reka AI, 2024) | ~6.7 × 10¹⁰ | 2024 | estimate | |
| 148 | LLaMA-65B (Meta AI, 2023) | 6.5 × 10¹⁰ | 6.5 × 10¹⁰ | 2023 | dense |
| 149 | SNM-skip (Google, 2014) | ~6.2 × 10¹⁰ | 2014 | estimate | |
| 150 | PaLI-X (Google Research, 2023) | ~5.5 × 10¹⁰ | 2023 | estimate | |
| 151 | RT-2 (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | architecture not stated |
| 152 | RT-2-X (Google DeepMind, 2023) | 5.5 × 10¹⁰ | 5.5 × 10¹⁰ | 2023 | architecture not stated |
| 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 | mixture of experts (active count unpublished) |
| 156 | Mixtral 8x7B (Mistral AI, 2023) | ~4.7 × 10¹⁰ | 2023 | estimate | |
| 157 | AlphaCode (DeepMind, 2022) | 4.1 × 10¹⁰ | 4.1 × 10¹⁰ | 2022 | architecture not stated |
| 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 | mixture of experts |
| 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 | architecture not stated |
| 162 | Oryx 34B (Tsinghua University, 2024) | 3.4 × 10¹⁰ | 3.4 × 10¹⁰ | 2024 | architecture not stated |
| 163 | Kaiju Medium (Character.ai, 2025) | ~3.4 × 10¹⁰ | 2025 | estimate | |
| 164 | EXAONE 4.5 (LG AI Research, 2026) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2026 | architecture not stated |
| 165 | Qwen2.5-32B (Alibaba, 2024) | 3.3 × 10¹⁰ | 3.3 × 10¹⁰ | 2024 | dense |
| 166 | QwQ-32B (Alibaba, 2025) | ~3.3 × 10¹⁰ | 2025 | estimate | |
| 167 | EXAONE 3.5 32B (LG AI Research, 2024) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2024 | architecture not stated |
| 168 | EXAONE Deep 32B (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | architecture not stated |
| 169 | EXAONE 4.0 (LG AI Research, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | architecture not stated |
| 170 | Olmo 3 (Allen Institute for AI, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | architecture not stated |
| 171 | HyperCLOVA X SEED 32B Think (NAVER, 2025) | 3.2 × 10¹⁰ | 3.2 × 10¹⁰ | 2025 | architecture not stated |
| 172 | Nemotron 3-Nano-30B-A3B (Nvidia, 2025) | ~3.2 × 10¹⁰ | 2025 | estimate | |
| 173 | Tongyi DeepResearch (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | architecture not stated |
| 174 | MM1-30B (Apple, 2024) | ~3 × 10¹⁰ | 2024 | estimate | |
| 175 | Movie Gen Video (Meta AI, 2024) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2024 | architecture not stated |
| 176 | AgentFounder-30B (Alibaba, 2025) | 3 × 10¹⁰ | 3 × 10¹⁰ | 2025 | architecture not stated |
| 177 | PLUG (Alibaba, 2021) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2021 | architecture not stated |
| 178 | Qwen Image (Alibaba, 2025) | 2.7 × 10¹⁰ | 2.7 × 10¹⁰ | 2025 | architecture not stated |
| 179 | GenSLM (University of Chicago, 2022) | 2.5 × 10¹⁰ | 2.5 × 10¹⁰ | 2022 | architecture not stated |
| 180 | ViT-22B (Google, 2023) | 2.2 × 10¹⁰ | 2.2 × 10¹⁰ | 2023 | architecture not stated |
| 181 | KN-LM (Google, 2007) | ~2.1 × 10¹⁰ | 2007 | estimate | |
| 182 | gpt-oss-20b (OpenAI, 2025) | 2.1 × 10¹⁰ | 2.1 × 10¹⁰ | 2025 | mixture of experts (active count unpublished) |
| 183 | RNN for 1B words (Google, 2013) | ~2 × 10¹⁰ | 2013 | estimate | |
| 184 | GPT-NeoX-20B (EleutherAI, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | dense |
| 185 | UL2 (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | architecture not stated |
| 186 | Parti (Google Research, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | architecture not stated |
| 187 | GPT-3.5 Turbo (OpenAI, 2023) | ~2 × 10¹⁰ | 2023 | estimate | |
| 188 | GPT-3.5 Turbo Instruct (OpenAI, 2023) | ~2 × 10¹⁰ | 2023 | estimate | |
| 189 | SPHINX (Shanghai AI Lab, 2023) | ~2 × 10¹⁰ | 2023 | estimate | |
| 190 | AlexaTM 20B (Amazon, 2022) | 2 × 10¹⁰ | 2 × 10¹⁰ | 2022 | architecture not stated |
| 191 | CogAgent (Tsinghua University, 2023) | ~1.8 × 10¹⁰ | 2023 | estimate | |
| 192 | Turing-NLG (Microsoft, 2020) | ~1.7 × 10¹⁰ | 2020 | estimate | |
| 193 | CogVLM-17B (Tsinghua University, 2023) | 1.7 × 10¹⁰ | 1.7 × 10¹⁰ | 2023 | architecture not stated |
| 194 | PaLI (Google, 2022) | ~1.7 × 10¹⁰ | 2022 | estimate | |
| 195 | CodeT5+ (Salesforce, 2023) | 1.6 × 10¹⁰ | 1.6 × 10¹⁰ | 2023 | architecture not stated |
| 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 | architecture not stated |
| 198 | FunSearch (Google DeepMind, 2023) | ~1.5 × 10¹⁰ | 2023 | estimate | |
| 199 | NVILA 15B (Nvidia, 2024) | 1.5 × 10¹⁰ | 1.5 × 10¹⁰ | 2024 | architecture not stated |
| 200 | nekomata-14b (rinna, 2023) | 1.4 × 10¹⁰ | 1.4 × 10¹⁰ | 2023 | architecture not stated |
| 201 | VILA1.5-13B (Nvidia, 2024) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2024 | architecture not stated |
| 202 | VILA-13B (Nvidia, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | architecture not stated |
| 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 | architecture not stated |
| 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 | architecture not stated |
| 207 | Jais (Cerebras Systems, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | architecture not stated |
| 208 | FinGPT-13B (University of California Los Angeles (UCLA), 2023) | ~1.3 × 10¹⁰ | 2023 | estimate | |
| 209 | Ferret (Columbia University, 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | architecture not stated |
| 210 | Skywork-13B (Kunlun Inc., 2023) | 1.3 × 10¹⁰ | 1.3 × 10¹⁰ | 2023 | architecture not stated |
| 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 | estimate | |
| 213 | Aya (Cohere Labs (formerly Cohere for AI), 2024) | ~1.3 × 10¹⁰ | 2024 | estimate | |
| 214 | Kaiju Small (Character.ai, 2025) | ~1.3 × 10¹⁰ | 2025 | estimate | |
| 215 | ByT5-XXL (Google, 2021) | ~1.3 × 10¹⁰ | 2021 | estimate | |
| 216 | CoRe (Tsinghua University, 2023) | ~1.2 × 10¹⁰ | 2023 | estimate | |
| 217 | DALL-E (OpenAI, 2021) | ~1.2 × 10¹⁰ | 2021 | estimate | |
| 218 | Codex (OpenAI, 2021) | ~1.2 × 10¹⁰ | 2021 | estimate | |
| 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 | architecture not stated |
| 222 | PLATO-XL (Baidu, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | architecture not stated |
| 223 | CoEdiT-xxl (University of Minnesota, 2023) | ~1.1 × 10¹⁰ | 2023 | estimate | |
| 224 | GQA-8-XXL (Google Research, 2023) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2023 | architecture not stated |
| 225 | XLMR-XXL (Facebook AI Research, 2021) | 1.1 × 10¹⁰ | 1.1 × 10¹⁰ | 2021 | architecture not stated |
| 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 | estimate | |
| 229 | Qwen-VL (Alibaba, 2023) | ~9.6 × 10⁹ | 2023 | estimate | |
| 230 | Generative BST (Facebook AI Research, 2021) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2021 | architecture not stated |
| 231 | Sparse all-MLP (Meta AI, 2022) | 9.4 × 10⁹ | 9.4 × 10⁹ | 2022 | mixture of experts (active count unpublished) |
| 232 | CogVideo (Tsinghua University, 2022) | ~9.4 × 10⁹ | 2022 | estimate | |
| 233 | eDiff-I (Nvidia, 2022) | ~9.1 × 10⁹ | 2022 | estimate | |
| 234 | Eagle 2 (Nvidia, 2025) | 8.9 × 10⁹ | 8.9 × 10⁹ | 2025 | architecture not stated |
| 235 | MoE-Multi (Jagiellonian University, 2017) | 8.7 × 10⁹ | 8.7 × 10⁹ | 2017 | mixture of experts (active count unpublished) |
| 236 | Qwen-Audio-Chat (Alibaba, 2023) | ~8.5 × 10⁹ | 2023 | estimate | |
| 237 | Megatron-LM (Nvidia, 2019) | ~8.3 × 10⁹ | 2019 | estimate | |
| 238 | BigSSL (Google, 2021) | ~8 × 10⁹ | 2021 | estimate | |
| 239 | Nemotron-3-8B (Nvidia, 2023) | 8 × 10⁹ | 8 × 10⁹ | 2023 | architecture not stated |
| 240 | Stable Diffusion 3 (Stability AI, 2024) | 8 × 10⁹ | 8 × 10⁹ | 2024 | dense |
| 241 | Qwen3 Embedding (Alibaba, 2025) | 8 × 10⁹ | 8 × 10⁹ | 2025 | mixture of experts (active count unpublished) |
| 242 | Imagen (Google Brain, 2022) | ~7.8 × 10⁹ | 2022 | estimate | |
| 243 | XGLM-7.5B (Meta AI, 2021) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2021 | architecture not stated |
| 244 | RETRO-7B (DeepMind, 2022) | 7.5 × 10⁹ | 7.5 × 10⁹ | 2022 | architecture not stated |
| 245 | OpenVLA (Stanford University, 2024) | 7.2 × 10⁹ | 7.2 × 10⁹ | 2024 | architecture not stated |
| 246 | mPLUG-Owl2 (Alibaba, 2023) | ~7.1 × 10⁹ | 2023 | estimate | |
| 247 | Goat-7B (National University of Singapore, 2023) | ~7 × 10⁹ | 2023 | estimate | |
| 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 | architecture not stated |
| 252 | Apollo 7B (Meta AI, 2024) | 7 × 10⁹ | 7 × 10⁹ | 2024 | architecture not stated |
| 253 | Eurus-2-7B-PRIME (Tsinghua University, 2025) | ~7 × 10⁹ | 2025 | estimate | |
| 254 | INTELLECT-MATH (Prime Intellect, 2025) | 7 × 10⁹ | 7 × 10⁹ | 2025 | architecture not stated |
| 255 | Incoder-6.7B (Facebook AI Research, 2023) | 6.7 × 10⁹ | 6.7 × 10⁹ | 2023 | architecture not stated |
| 256 | ProGen2-xlarge (Salesforce Research, 2022) | 6.4 × 10⁹ | 6.4 × 10⁹ | 2022 | architecture not stated |
| 257 | GPipe (Google, 2018) | ~6 × 10⁹ | 2018 | estimate | |
| 258 | InstructGPT 6B (OpenAI, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | dense |
| 259 | Vega v2 (Wuhan University, 2022) | 6 × 10⁹ | 6 × 10⁹ | 2022 | architecture not stated |
| 260 | ChatGLM3-6B (Z.ai (Zhipu AI), 2023) | ~6 × 10⁹ | 2023 | estimate | |
| 261 | MolmoAct 2 (Allen Institute for AI, 2026) | ~5.5 × 10⁹ | 2026 | estimate | |
| 262 | T-NLRv5 XXL (Microsoft, 2021) | 5.4 × 10⁹ | 5.4 × 10⁹ | 2021 | architecture not stated |
| 263 | π0.6 (Physical Intelligence, 2025) | 5.3 × 10⁹ | 5.3 × 10⁹ | 2025 | mixture of experts (active count unpublished) |
| 264 | PaLI-3 (Google DeepMind, 2023) | 5 × 10⁹ | 5 × 10⁹ | 2023 | architecture not stated |
| 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 | architecture not stated |
| 267 | CogView (Tsinghua University, 2021) | ~4 × 10⁹ | 2021 | estimate | |
| 268 | Make-A-Scene (Meta AI, 2022) | ~4 × 10⁹ | 2022 | estimate | |
| 269 | ONE-PEACE (Alibaba, 2023) | ~4 × 10⁹ | 2023 | estimate | |
| 270 | Megatron-BERT (Nvidia, 2019) | 3.9 × 10⁹ | 3.9 × 10⁹ | 2019 | dense |
| 271 | DALL·E 2 (OpenAI, 2022) | ~3.5 × 10⁹ | 2022 | estimate | |
| 272 | Stable Diffusion XL (Stability AI, 2023) | ~3.4 × 10⁹ | 2023 | estimate | |
| 273 | MusicGen (Meta AI, 2023) | ~3.4 × 10⁹ | 2023 | estimate | |
| 274 | Gemini Nano-2 (Google DeepMind, 2023) | 3.3 × 10⁹ | 3.3 × 10⁹ | 2023 | architecture not stated |
| 275 | Zidong Taichu (Chinese Academy of Sciences, 2021) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2021 | architecture not stated |
| 276 | DeepNet (Microsoft Research, 2022) | 3.2 × 10⁹ | 3.2 × 10⁹ | 2022 | architecture not stated |
| 277 | MS-ensemble-speech-recognition (Microsoft, 2016) | ~3.2 × 10⁹ | 2016 | estimate | |
| 278 | BASIC-L (Google, 2021) | ~3.1 × 10⁹ | 2021 | estimate | |
| 279 | BASIC-L + Lion (Google, 2023) | 3.1 × 10⁹ | 3.1 × 10⁹ | 2023 | architecture not stated |
| 280 | LEP-AD (King Abdullah University of Science and Technology (KAUST), 2023) | 3 × 10⁹ | 3 × 10⁹ | 2023 | architecture not stated |
| 281 | ViT-G/14 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | architecture not stated |
| 282 | ProtT5-XL-U50 (Technical University of Munich, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | architecture not stated |
| 283 | Swin Transformer V2 (Microsoft Research Asia, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | architecture not stated |
| 284 | LongT5 (Google Research, 2021) | 3 × 10⁹ | 3 × 10⁹ | 2021 | architecture not stated |
| 285 | ReALM (Apple, 2024) | 3 × 10⁹ | 3 × 10⁹ | 2024 | architecture not stated |
| 286 | Mesh-TensorFlow Transformer 2.9B (Google Brain, 2018) | ~2.9 × 10⁹ | 2018 | estimate | |
| 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 | architecture not stated |
| 290 | PolyCoder (Carnegie Mellon University (CMU), 2022) | ~2.7 × 10⁹ | 2022 | estimate | |
| 291 | Hybrid H3-2.7B (Stanford University, 2022) | ~2.7 × 10⁹ | 2022 | estimate | |
| 292 | GPT3-2.7B (Stanford University, 2023) | 2.7 × 10⁹ | 2.7 × 10⁹ | 2023 | architecture not stated |
| 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 | estimate | |
| 296 | Fugatto 1 (Nvidia, 2024) | 2.5 × 10⁹ | 2.5 × 10⁹ | 2024 | architecture not stated |
| 297 | CoAtNet (Google, 2021) | 2.4 × 10⁹ | 2.4 × 10⁹ | 2021 | architecture not stated |
| 298 | GShard (Google, 2020) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2020 | dense |
| 299 | SeamlessM4T (Facebook, 2023) | 2.3 × 10⁹ | 2.3 × 10⁹ | 2023 | architecture not stated |
| 300 | Turing ULRv5 (Microsoft, 2021) | 2.2 × 10⁹ | 2.2 × 10⁹ | 2021 | architecture not stated |
| 301 | CoCa (Google Research, 2022) | 2.1 × 10⁹ | 2.1 × 10⁹ | 2022 | architecture not stated |
| 302 | MuseNet (OpenAI, 2019) | ~2 × 10⁹ | 2019 | estimate | |
| 303 | Infinity (ByteDance, 2024) | 2 × 10⁹ | 2 × 10⁹ | 2024 | architecture not stated |
| 304 | Seq2Seq LSTM (Google, 2014) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2014 | dense |
| 305 | BEIT-3 (Microsoft, 2022) | ~1.9 × 10⁹ | 2022 | estimate | |
| 306 | Ankh_large (Technical University of Munich, 2023) | 1.9 × 10⁹ | 1.9 × 10⁹ | 2023 | architecture not stated |
| 307 | ViT-G/14 (Google Brain, 2021) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2021 | architecture not stated |
| 308 | ViT-G (University of Washington, 2022) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2022 | architecture not stated |
| 309 | Phenaki (University College London (UCL), 2022) | ~1.8 × 10⁹ | 2022 | estimate | |
| 310 | Gemini Nano-1 (Google DeepMind, 2023) | 1.8 × 10⁹ | 1.8 × 10⁹ | 2023 | architecture not stated |
| 311 | DistBelief Vision (Google, 2012) | ~1.7 × 10⁹ | 2012 | estimate | |
| 312 | Whisper (OpenAI, 2022) | ~1.6 × 10⁹ | 2022 | estimate | |
| 313 | GPT-2 (OpenAI, 2019) | ~1.5 × 10⁹ | 2019 | estimate | |
| 314 | DeBERTa (Microsoft, 2021) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2021 | architecture not stated |
| 315 | AR-LDM (Alibaba, 2022) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2022 | architecture not stated |
| 316 | AudioLM (Google Research, 2023) | ~1.5 × 10⁹ | 2023 | estimate | |
| 317 | BLIP-2 (Salesforce Research, 2023) | 1.5 × 10⁹ | 1.5 × 10⁹ | 2023 | architecture not stated |
| 318 | LDM-1.45B (Heidelberg University, 2021) | 1.4 × 10⁹ | 1.4 × 10⁹ | 2021 | architecture not stated |
| 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 | architecture not stated |
| 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 | estimate | |
| 324 | Gato (DeepMind, 2022) | 1.2 × 10⁹ | 1.2 × 10⁹ | 2022 | architecture not stated |
| 325 | DINOv2 (Facebook AI Research, 2023) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2023 | architecture not stated |
| 326 | Diffusion Renderer (Nvidia, 2025) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2025 | architecture not stated |
| 327 | InternImage (Shanghai AI Lab, 2022) | 1.1 × 10⁹ | 1.1 × 10⁹ | 2022 | architecture not stated |
| 328 | EVA-01 (Beijing Academy of Artificial Intelligence / BAAI, 2022) | 1 × 10⁹ | 1 × 10⁹ | 2022 | architecture not stated |
| 329 | Unsupervised High-level Feature Learner (Google, 2012) | ~1 × 10⁹ | 2012 | estimate | |
| 330 | Conformer + Wav2vec 2.0 + Noisy Student (Google, 2020) | 1 × 10⁹ | 1 × 10⁹ | 2020 | dense |
| 331 | HuBERT (Facebook AI Research, 2021) | ~1 × 10⁹ | 2021 | estimate | |
| 332 | W2v-BERT (Google Brain, 2021) | 1 × 10⁹ | 1 × 10⁹ | 2021 | dense |
| 333 | AudioGen (Meta AI, 2023) | ~1 × 10⁹ | 2023 | estimate | |
| 334 | VideoMAE V2 (Nanjing University, 2023) | 1 × 10⁹ | 1 × 10⁹ | 2023 | architecture not stated |
| 335 | TransE (Universite de Technologie de Compiègne – CNRS, 2013) | ~9.4 × 10⁸ | 2013 | estimate | |
| 336 | ImageBind (Meta AI, 2023) | ~9.3 × 10⁸ | 2023 | estimate | |
| 337 | Big Transfer (Google Brain, 2019) | ~9.3 × 10⁸ | 2019 | estimate | |
| 338 | Florence (Microsoft, 2021) | 8.9 × 10⁸ | 8.9 × 10⁸ | 2021 | architecture not stated |
| 339 | NÜWA (Microsoft Research, 2021) | 8.7 × 10⁸ | 8.7 × 10⁸ | 2021 | architecture not stated |
| 340 | MusicLM (Google, 2023) | 8.6 × 10⁸ | 8.6 × 10⁸ | 2023 | architecture not stated |
| 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 | estimate | |
| 343 | ALIGN (Google Research, 2021) | 8.2 × 10⁸ | 8.2 × 10⁸ | 2021 | architecture not stated |
| 344 | Statement Curriculum Learning (OpenAI, 2022) | ~7.7 × 10⁸ | 2022 | estimate | |
| 345 | CodeT5-large (Salesforce, 2022) | ~7.7 × 10⁸ | 2022 | estimate | |
| 346 | DITTO (Tsinghua University, 2022) | 7.5 × 10⁸ | 7.5 × 10⁸ | 2022 | architecture not stated |
| 347 | FGN (Google DeepMind, 2025) | 7.2 × 10⁸ | 7.2 × 10⁸ | 2025 | architecture not stated |
| 348 | data2vec (Meta AI, 2022) | ~7.1 × 10⁸ | 2022 | estimate | |
| 349 | Tranception (University of Oxford, 2022) | 7 × 10⁸ | 7 × 10⁸ | 2022 | architecture not stated |
| 350 | Word2Vec (Google, 2013) | ~6.9 × 10⁸ | 2013 | estimate | |
| 351 | DiT-XL/2 (New York University (NYU), 2023) | 6.8 × 10⁸ | 6.8 × 10⁸ | 2023 | architecture not stated |
| 352 | DiT-XL/2 + CADS (ETH Zurich, 2023) | ~6.8 × 10⁸ | 2023 | estimate | |
| 353 | OmegaPLM (Massachusetts Institute of Technology (MIT), 2022) | 6.7 × 10⁸ | 6.7 × 10⁸ | 2022 | architecture not stated |
| 354 | XLM (Facebook, 2019) | ~6.7 × 10⁸ | 2019 | estimate | |
| 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 | architecture not stated |
| 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 | estimate | |
| 359 | Retrieval-Augmented Generator (Facebook, 2020) | 6.3 × 10⁸ | 6.3 × 10⁸ | 2020 | dense |
| 360 | DLDL (University of Oxford, 2016) | ~5.6 × 10⁸ | 2016 | estimate | |
| 361 | ADM (OpenAI, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | architecture not stated |
| 362 | TrOCR (Beihang University, 2021) | 5.6 × 10⁸ | 5.6 × 10⁸ | 2021 | architecture not stated |
| 363 | XLM-RoBERTa (Facebook AI, 2019) | 5.5 × 10⁸ | 5.5 × 10⁸ | 2019 | dense |
| 364 | LUKE (University of Washington, 2020) | ~4.8 × 10⁸ | 2020 | estimate | |
| 365 | EfficientNet-L2 (Google, 2019) | ~4.8 × 10⁸ | 2019 | estimate | |
| 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 | estimate | |
| 368 | EMDR (Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), 2021) | 4.4 × 10⁸ | 4.4 × 10⁸ | 2021 | architecture not stated |
| 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 | architecture not stated |
| 372 | OntoProtein (Zhejiang University (ZJU), 2022) | ~4.2 × 10⁸ | 2022 | estimate | |
| 373 | DeBERTaV3large + KEAR (Microsoft, 2022) | 4.2 × 10⁸ | 4.2 × 10⁸ | 2022 | architecture not stated |
| 374 | BART-large (Facebook AI, 2019) | ~4.1 × 10⁸ | 2019 | estimate | |
| 375 | SigLIP 400M (Google DeepMind, 2023) | 4 × 10⁸ | 4 × 10⁸ | 2023 | architecture not stated |
| 376 | BERT-Large-CAS (Amazon, 2019) | ~4 × 10⁸ | 2019 | estimate | |
| 377 | MoCo (Facebook AI, 2019) | ~3.8 × 10⁸ | 2019 | estimate | |
| 378 | SimCLR (Google Brain, 2020) | ~3.8 × 10⁸ | 2020 | estimate | |
| 379 | CLIP (OpenAI, 2021) | 3.7 × 10⁸ | 3.7 × 10⁸ | 2021 | architecture not stated |
| 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 | architecture not stated |
| 382 | GPT-2 Medium (Stanford University, 2022) | 3.5 × 10⁸ | 3.5 × 10⁸ | 2022 | dense |
| 383 | VALL-E (Microsoft, 2023) | ~3.5 × 10⁸ | 2023 | estimate | |
| 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 | estimate | |
| 387 | CamemBERT (Facebook, 2019) | 3.4 × 10⁸ | 3.4 × 10⁸ | 2019 | dense |
| 388 | ELECTRA (Stanford University, 2020) | ~3.4 × 10⁸ | 2020 | estimate | |
| 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 | estimate | |
| 391 | MT-DNN (Microsoft, 2019) | 3.3 × 10⁸ | 3.3 × 10⁸ | 2019 | dense |
| 392 | Fusion in Encoder (Samsung, 2022) | ~3.3 × 10⁸ | 2022 | estimate | |
| 393 | wave2vec 2.0 LARGE (Facebook, 2020) | 3.2 × 10⁸ | 3.2 × 10⁸ | 2020 | dense |
| 394 | CPC v2 (DeepMind, 2019) | ~3 × 10⁸ | 2019 | estimate | |
| 395 | Transformer - LibriVox + Decoding/Rescoring (Facebook, 2019) | 3 × 10⁸ | 3 × 10⁸ | 2019 | dense |
| 396 | DDPM-IP (Utrecht University, 2023) | ~3 × 10⁸ | 2023 | estimate | |
| 397 | GNMT (Google, 2016) | ~2.8 × 10⁸ | 2016 | estimate | |
| 398 | base LM+GNN+kNN (Shannon.AI, 2021) | 2.7 × 10⁸ | 2.7 × 10⁸ | 2021 | architecture not stated |
| 399 | YOLO (University of Washington, 2015) | ~2.7 × 10⁸ | 2015 | estimate | |
| 400 | Transformer-XL (Carnegie Mellon University (CMU), 2019) | ~2.6 × 10⁸ | 2019 | estimate | |
| 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 | estimate | |
| 403 | Segatron-XL large, M=384 + HCP (Microsoft Research, 2022) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2022 | architecture not stated |
| 404 | Denoising Diffusion Probabilistic Models (University of California (UC) Berkeley, 2021) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2021 | architecture not stated |
| 405 | Pangu-Weather (Huawei, 2023) | 2.6 × 10⁸ | 2.6 × 10⁸ | 2023 | architecture not stated |
| 406 | SPN (Google Brain, 2018) | 2.5 × 10⁸ | 2.5 × 10⁸ | 2018 | dense |
| 407 | S4 (Stanford University, 2021) | ~2.5 × 10⁸ | 2021 | estimate | |
| 408 | Base LM + kNN LM + Continuous Cache (Stanford University, 2019) | ~2.5 × 10⁸ | 2019 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 413 | ALBERT-xxlarge (Toyota Technological Institute at Chicago, 2020) | ~2.4 × 10⁸ | 2020 | estimate | |
| 414 | SRU++ Large (ASAPP, 2021) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2021 | architecture not stated |
| 415 | GR-2 (ByteDance, 2024) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2024 | architecture not stated |
| 416 | MaskGIT (Google Research, 2022) | 2.3 × 10⁸ | 2.3 × 10⁸ | 2022 | architecture not stated |
| 417 | MetNet (Google, 2020) | ~2.3 × 10⁸ | 2020 | estimate | |
| 418 | FFN SwiGLU (Google, 2020) | 2.2 × 10⁸ | 2.2 × 10⁸ | 2020 | dense |
| 419 | CodeT5-base (Salesforce, 2021) | ~2.2 × 10⁸ | 2021 | estimate | |
| 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 | estimate | |
| 422 | EfficientNetV2-XL (Google, 2021) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2021 | architecture not stated |
| 423 | Word2Vec (Google, 2013) | ~2.1 × 10⁸ | 2013 | estimate | |
| 424 | Multilingual DNN (Google, 2013) | 2.1 × 10⁸ | 2.1 × 10⁸ | 2013 | dense |
| 425 | DreamerV3 (DeepMind, 2023) | ~2 × 10⁸ | 2023 | estimate | |
| 426 | DL scaling speech (Baidu, 2017) | ~1.9 × 10⁸ | 2017 | estimate | |
| 427 | ResNeXt-101 Billion-scale (Facebook AI, 2019) | ~1.9 × 10⁸ | 2019 | estimate | |
| 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 | estimate | |
| 431 | EXAONE Path 2.0 (LG AI Research, 2025) | 1.8 × 10⁸ | 1.8 × 10⁸ | 2025 | architecture not stated |
| 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 | estimate | |
| 435 | TransformerXL + spectrum control (University of California Los Angeles (UCLA), 2020) | ~1.5 × 10⁸ | 2020 | estimate | |
| 436 | AlphaGeometry (Google DeepMind, 2024) | 1.5 × 10⁸ | 1.5 × 10⁸ | 2024 | architecture not stated |
| 437 | PermuteFormer (Peking University, 2021) | ~1.5 × 10⁸ | 2021 | estimate | |
| 438 | Fragment embedding (Stanford University, 2014) | ~1.4 × 10⁸ | 2014 | estimate | |
| 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 | estimate | |
| 441 | LRCN (UT Austin, 2014) | ~1.4 × 10⁸ | 2014 | estimate | |
| 442 | Transformer-XL DeFINE (University of Washington, 2019) | ~1.4 × 10⁸ | 2019 | estimate | |
| 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 | estimate | |
| 448 | Template Adaptation (University of Oxford, 2016) | ~1.4 × 10⁸ | 2016 | estimate | |
| 449 | LRR-4X (UC Irvine, 2016) | ~1.4 × 10⁸ | 2016 | estimate | |
| 450 | CMS-RCNN (IEEE, 2016) | ~1.4 × 10⁸ | 2016 | estimate | |
| 451 | QRNN (Salesforce Research, 2018) | ~1.4 × 10⁸ | 2018 | estimate | |
| 452 | Multiresolution CNN (Google, 2014) | ~1.3 × 10⁸ | 2014 | estimate | |
| 453 | FTW (DeepMind, 2018) | ~1.3 × 10⁸ | 2018 | estimate | |
| 454 | Feedback Transformer (LORIA, 2020) | ~1.3 × 10⁸ | 2020 | estimate | |
| 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 | estimate | |
| 458 | Conformer (Google, 2020) | 1.2 × 10⁸ | 1.2 × 10⁸ | 2020 | dense |
| 459 | GPT-1 (OpenAI, 2018) | ~1.2 × 10⁸ | 2018 | estimate | |
| 460 | ContextNet (Google, 2020) | ~1.1 × 10⁸ | 2020 | estimate | |
| 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 | architecture not stated |
| 463 | Contriever (Meta AI, 2021) | ~1.1 × 10⁸ | 2021 | estimate | |
| 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 | architecture not stated |
| 466 | LSTM LM (RWTH Aachen University, 2012) | ~1 × 10⁸ | 2012 | estimate | |
| 467 | ReLU-Speech (Google, 2013) | ~1 × 10⁸ | 2013 | estimate | |
| 468 | STORM-B/8 (University of Southern California, 2024) | 1 × 10⁸ | 1 × 10⁸ | 2024 | architecture not stated |
| 469 | Support Vector Machines (AT&T, 1995) | ~1 × 10⁸ | 1995 | estimate | |
| 470 | GPU DBNs (Stanford University, 2009) | 1 × 10⁸ | 1 × 10⁸ | 2009 | dense |
| 471 | MSA Transformer (Facebook AI Research, 2021) | ~1 × 10⁸ | 2021 | estimate | |
| 472 | YOLOX-X (Megvii Inc, 2021) | ~9.9 × 10⁷ | 2021 | estimate | |
| 473 | DeLighT (University of Washington, 2020) | ~9.9 × 10⁷ | 2020 | estimate | |
| 474 | ELMo (University of Washington, 2018) | ~9.4 × 10⁷ | 2018 | estimate | |
| 475 | AlphaFold 2 (DeepMind, 2020) | ~9.3 × 10⁷ | 2020 | estimate | |
| 476 | AlphaMissense (Google DeepMind, 2023) | ~9.3 × 10⁷ | 2023 | estimate | |
| 477 | Octo-Base (University of California (UC) Berkeley, 2024) | 9.3 × 10⁷ | 9.3 × 10⁷ | 2024 | architecture not stated |
| 478 | PolyNet (Chinese University of Hong Kong (CUHK), 2016) | ~9.2 × 10⁷ | 2016 | estimate | |
| 479 | Transformer local-attention (Google Cloud, 2021) | 9 × 10⁷ | 9 × 10⁷ | 2021 | architecture not stated |
| 480 | Transformer + Simple Recurrent Unit (ASAPP, 2018) | 9 × 10⁷ | 9 × 10⁷ | 2018 | dense |
| 481 | NASNet-A (Google Brain, 2017) | ~8.9 × 10⁷ | 2017 | estimate | |
| 482 | CLIP (OpenAI, 2021) | ~8.9 × 10⁷ | 2021 | estimate | |
| 483 | Detic (Meta AI, 2022) | ~8.8 × 10⁷ | 2022 | estimate | |
| 484 | MSRA (Microsoft Research, 2015) | 8.7 × 10⁷ | 8.7 × 10⁷ | 2015 | dense |
| 485 | BigBiGAN (Google, 2019) | ~8.6 × 10⁷ | 2019 | estimate | |
| 486 | ViT-Base/32 (Google Brain, 2020) | ~8.6 × 10⁷ | 2020 | estimate | |
| 487 | DeiT-B (Meta AI, 2021) | 8.6 × 10⁷ | 8.6 × 10⁷ | 2021 | architecture not stated |
| 488 | CaLM (University of Oxford, 2022) | ~8.6 × 10⁷ | 2022 | estimate | |
| 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 | estimate | |
| 492 | Routing Transformer (Google Research, 2020) | 8 × 10⁷ | 8 × 10⁷ | 2020 | dense |
| 493 | Big-Little Net (IBM, 2018) | ~7.7 × 10⁷ | 2018 | estimate | |
| 494 | EfficientDet (Google Brain, 2020) | ~7.7 × 10⁷ | 2020 | estimate | |
| 495 | CODEFUSION (Microsoft, 2023) | 7.5 × 10⁷ | 7.5 × 10⁷ | 2023 | architecture not stated |
| 496 | RNN LM (Johns Hopkins University, 2010) | ~7 × 10⁷ | 2010 | estimate | |
| 497 | R-CNN (University of California (UC) Berkeley, 2013) | ~6.9 × 10⁷ | 2013 | estimate | |
| 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 | estimate | |
| 505 | Inception-ResNet-V2 (Google, 2016) | ~5.6 × 10⁷ | 2016 | estimate | |
| 506 | Transformer ELMo (Allen Institute for AI, 2019) | 5.6 × 10⁷ | 5.6 × 10⁷ | 2019 | dense |
| 507 | Fruit fly brain | 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 | estimate | |
| 514 | Dropout-LSTM+Noise (Columbia University, 2018) | ~5.1 × 10⁷ | 2018 | estimate | |
| 515 | LSTM + dynamic eval (University of Edinburgh, 2017) | ~5 × 10⁷ | 2017 | estimate | |
| 516 | Dropout (University of Toronto, 2012) | ~4.9 × 10⁷ | 2012 | estimate | |
| 517 | Dropout: SVHN (University of Toronto, 2014) | 4.8 × 10⁷ | 4.8 × 10⁷ | 2014 | dense |
| 518 | DistBelief Speech (Google, 2012) | ~4.7 × 10⁷ | 2012 | estimate | |
| 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 | estimate | |
| 529 | Inceptionv4 (Google, 2016) | ~4.3 × 10⁷ | 2016 | estimate | |
| 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 | estimate | |
| 533 | MuZero (DeepMind, 2019) | ~3.7 × 10⁷ | 2019 | estimate | |
| 534 | AWD-LSTM-MoS + dynamic evaluation (Carnegie Mellon University (CMU), 2017) | ~3.5 × 10⁷ | 2017 | estimate | |
| 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 | architecture not stated |
| 540 | RT-1 (Google, 2022) | 3.5 × 10⁷ | 3.5 × 10⁷ | 2022 | architecture not stated |
| 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 | estimate | |
| 543 | Fraternal dropout + AWD-LSTM 3-layer (Jagiellonian University, 2017) | ~3.4 × 10⁷ | 2017 | estimate | |
| 544 | AWD-LSTM-DRILL + dynamic evaluation† (IDIAP, 2019) | ~3.4 × 10⁷ | 2019 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 552 | SENet (Chinese Academy of Sciences, 2017) | ~2.8 × 10⁷ | 2017 | estimate | |
| 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 | estimate | |
| 555 | Hierarchical Reasoning Model (Sapient Intelligence, 2025) | 2.7 × 10⁷ | 2.7 × 10⁷ | 2025 | architecture not stated |
| 556 | RNN-WER (DeepMind, 2014) | ~2.6 × 10⁷ | 2014 | estimate | |
| 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 | estimate | |
| 559 | ResNeXt-50 (University of California San Diego, 2016) | ~2.5 × 10⁷ | 2016 | estimate | |
| 560 | AWD-LSTM (DeepMind, 2017) | 2.4 × 10⁷ | 2.4 × 10⁷ | 2017 | dense |
| 561 | Inception v3 (Google, 2015) | ~2.4 × 10⁷ | 2015 | estimate | |
| 562 | Mamba-24M (Carnegie Mellon University (CMU), 2023) | 2.3 × 10⁷ | 2.3 × 10⁷ | 2023 | architecture not stated |
| 563 | WeNet (Amazon, 2019) | ~2.3 × 10⁷ | 2019 | estimate | |
| 564 | EN^2AS with performance reward (Beijing Institute of Technology, 2019) | ~2.3 × 10⁷ | 2019 | estimate | |
| 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 | estimate | |
| 568 | MMLSTM (Beijing University of Posts and Telecommunications, 2019) | ~2.1 × 10⁷ | 2019 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 580 | GNoME for crystal discovery (Google DeepMind, 2023) | ~1.6 × 10⁷ | 2023 | estimate | |
| 581 | ReLU (University of Toronto, 2010) | ~1.6 × 10⁷ | 2010 | estimate | |
| 582 | ProteinBERT (Hebrew University of Jerusalem, 2022) | 1.6 × 10⁷ | 1.6 × 10⁷ | 2022 | architecture not stated |
| 583 | Eve (Harvard Medical School, 2021) | ~1.5 × 10⁷ | 2021 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 591 | RNN+LDA+KN5+cache (Microsoft, 2012) | ~9 × 10⁶ | 2012 | estimate | |
| 592 | Gated HORNN (York University, 2016) | ~9 × 10⁶ | 2016 | estimate | |
| 593 | AlphaGo Fan (DeepMind, 2015) | ~8.2 × 10⁶ | 2015 | estimate | |
| 594 | genCNN + dyn eval (Chinese Academy of Sciences, 2015) | ~8 × 10⁶ | 2015 | estimate | |
| 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 | estimate | |
| 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 | architecture not stated |
| 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 | estimate | |
| 609 | SPN-4+KN5 (Singapore University of Technology & Design, 2014) | ~5 × 10⁶ | 2014 | estimate | |
| 610 | 6-Act Tether (Facebook AI Research, 2021) | 5 × 10⁶ | 5 × 10⁶ | 2021 | architecture not stated |
| 611 | High Performance CNN (IDSIA, 2011) | ~4.9 × 10⁶ | 2011 | estimate | |
| 612 | MobileNet (Google, 2017) | ~4.2 × 10⁶ | 2017 | estimate | |
| 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 | estimate | |
| 617 | GOAT (DeepMind, 2021) | ~3.5 × 10⁶ | 2021 | estimate | |
| 618 | MobileNetV2 (Google, 2018) | ~3.4 × 10⁶ | 2018 | estimate | |
| 619 | Big-Little Net (IBM, 2018) | ~3.3 × 10⁶ | 2018 | estimate | |
| 620 | DLWP (University of Washington, 2021) | 2.7 × 10⁶ | 2.7 × 10⁶ | 2021 | architecture not stated |
| 621 | BIDAF (University of Washington, 2016) | 2.6 × 10⁶ | 2.6 × 10⁶ | 2016 | dense |
| 622 | DeepStack (University of Alberta, 2017) | ~2.5 × 10⁶ | 2017 | estimate | |
| 623 | KataGo (Jane Street, 2019) | ~2.5 × 10⁶ | 2019 | estimate | |
| 624 | ShuffleNet v1 (Megvii Inc, 2017) | ~2.4 × 10⁶ | 2017 | estimate | |
| 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 | architecture not stated |
| 627 | LSTM+NeuralCache (KU Leuven, 2018) | ~2.1 × 10⁶ | 2018 | estimate | |
| 628 | HLBL (University of Toronto, 2008) | 1.8 × 10⁶ | 1.8 × 10⁶ | 2008 | dense |
| 629 | ResNet-110 (Microsoft, 2015) | ~1.7 × 10⁶ | 2015 | estimate | |
| 630 | Dueling DQN (Google DeepMind, 2016) | 1.7 × 10⁶ | 1.7 × 10⁶ | 2016 | dense |
| 631 | DQN-2015 (Google, 2015) | ~1.7 × 10⁶ | 2015 | estimate | |
| 632 | Rational DQN Average (TU Darmstadt, 2021) | ~1.7 × 10⁶ | 2021 | estimate | |
| 633 | IBM-5 (IBM, 1993) | ~1.7 × 10⁶ | 1993 | estimate | |
| 634 | Deep Belief Nets (University of Toronto, 2006) | ~1.6 × 10⁶ | 2006 | estimate | |
| 635 | IMPALA (DeepMind, 2018) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2018 | dense |
| 636 | StyleGAN3-R (Nvidia, 2021) | 1.6 × 10⁶ | 1.6 × 10⁶ | 2021 | architecture not stated |
| 637 | Deep Multitask NLP Network (NEC Laboratories, 2008) | ~1.5 × 10⁶ | 2008 | estimate | |
| 638 | Double DQN (Google DeepMind, 2016) | 1.5 × 10⁶ | 1.5 × 10⁶ | 2016 | dense |
| 639 | CNN committee (IDSIA, 2011) | ~1.4 × 10⁶ | 2011 | estimate | |
| 640 | SqueezeNet (DeepScale, 2016) | ~1.2 × 10⁶ | 2016 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 648 | Robot Parkour (Shanghai Qi Zhi institute, 2023) | 5 × 10⁵ | 5 × 10⁵ | 2023 | architecture not stated |
| 649 | LISSOM (University of Texas at Austin, 1995) | ~4.3 × 10⁵ | 1995 | estimate | |
| 650 | SPIDER2 (Griffith University, 2016) | ~4.1 × 10⁵ | 2016 | estimate | |
| 651 | Mixture of linear models (, 1994) | ~3.8 × 10⁵ | 1994 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 661 | CNN Committee (IDSIA, 2011) | ~1.3 × 10⁵ | 2011 | estimate | |
| 662 | CNN Committee (IDSIA, 2011) | ~1.2 × 10⁵ | 2011 | estimate | |
| 663 | RECONTRA-uncategorized (, 1999) | ~1.1 × 10⁵ | 1999 | estimate | |
| 664 | BLSTM for handwriting (University of Bern, 2007) | ~1 × 10⁵ | 2007 | estimate | |
| 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 | estimate | |
| 669 | RECONTRA-categorized (, 1999) | ~6.7 × 10⁴ | 1999 | estimate | |
| 670 | LeNet-5 (AT&T, 1998) | 6 × 10⁴ | 6 × 10⁴ | 1998 | dense |
| 671 | Swift (Intel Labs, 2023) | ~5.7 × 10⁴ | 2023 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 683 | Decision tree (Mitsubishi Electric Research Labs, 2001) | ~1.2 × 10⁴ | 2001 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 689 | Weight Decay (, 1991) | 8.4 × 10³ | 8.4 × 10³ | 1991 | dense |
| 690 | Deep Blue (IBM, 1997) | ~8 × 10³ | 1997 | estimate | |
| 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 | estimate | |
| 697 | Kohonen network (Helsinki University of Technology, 1981) | ~4.1 × 10³ | 1981 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 704 | Ceramic-MLP (Sapienza Università di Roma, 1994) | ~1.9 × 10³ | 1994 | estimate | |
| 705 | SexNet classification (, 1990) | ~1.6 × 10³ | 1990 | estimate | |
| 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 | estimate | |
| 708 | Perceptron Mark I (Cornell Aeronautical Laboratory, 1957) | ~1 × 10³ | 1957 | estimate | |
| 709 | Perceptron (Cornell Aeronautical Laboratory, 1960) | ~1 × 10³ | 1960 | estimate | |
| 710 | Translation-invariant MLP (Carnegie Mellon University (CMU), 1987) | 8.2 × 10² | 8.2 × 10² | 1987 | mixture of experts (active count unpublished) |
| 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 | estimate | |
| 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 | estimate | |
| 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 | estimate | |
| 718 | LSTM with forget gates (IDSIA, 1999) | ~2.8 × 10² | 1999 | estimate | |
| 719 | Self Organizing System (Massachusetts Institute of Technology (MIT), 1955) | ~2.3 × 10² | 1955 | estimate | |
| 720 | Theseus (Bell Laboratories, 1950) | 4 × 10¹ | 4 × 10¹ | 1950 | dense |
| 721 | SNARC (Harvard University, 1952) | ~4 × 10¹ | 1952 | estimate | |
| 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 | estimate |
Collectives
These charts rank animal societies, religions, states, parties and corporations two ways. One is by members. The other is by neuron-equivalents, which weight each member by the share of their waking time the group’s instructions direct. Religious codes, laws, party rules and job descriptions all count the same way. They're all sets of instructions, so we measure them by the same rule. How directed time is estimated.
- Chimpanzee community▲17.4 × 10¹¹
- Leafcutter ant colony▼16.3 × 10¹¹
- Bonobo community▲24.4 × 10¹¹
- Hadza camp (humans, for scale)4.1 × 10¹¹
- Bigg’s orca group▼22.6 × 10¹¹
- Resident orca matriline2.4 × 10¹¹
- Baboon troop8.6 × 10¹⁰
- Bottlenose dolphin alliance▲17.6 × 10¹⁰
- Sperm whale social unit▲16.4 × 10¹⁰
- Honeybee colony▼25.8 × 10¹⁰
- Elephant family5.6 × 10¹⁰
- Pinyon jay flock5.3 × 10¹⁰
- Spotted hyena clan2.2 × 10¹⁰
- African wild dog pack7.2 × 10⁹
- Wolf pack4.2 × 10⁹
- Meerkat group1.7 × 10⁹
- Naked mole-rat colony4.1 × 10⁸
108109101010111012neurons
Show the numbers
| # | Group | Neurons in the group | In group activity | Members | Share directed | Call types |
|---|---|---|---|---|---|---|
| 1 | Chimpanzee community | 7.4 × 10¹¹ | 2.5 × 10¹¹ | 100 | 34% | — |
| 2 | Leafcutter ant colony | 6.3 × 10¹¹ | 5 × 10¹¹ | 2.5 million | 80% | — |
| 3 | Bonobo community | 4.4 × 10¹¹ | 1.9 × 10¹¹ | 59 | 43% | — |
| 4 | Hadza camp (humans, for scale) | 4.1 × 10¹¹ | 2.1 × 10¹¹ | 25 | 53% | — |
| 5 | Bigg’s orca group | 2.6 × 10¹¹ | 2.4 × 10¹¹ | 6 | 91% | — |
| 6 | Resident orca matriline | 2.4 × 10¹¹ | 1.7 × 10¹¹ | 6 | 70% | 11 |
| 7 | Baboon troop | 8.6 × 10¹⁰ | 5.2 × 10¹⁰ | 30 | 60% | 9 |
| 8 | Bottlenose dolphin alliance | 7.6 × 10¹⁰ | 4.2 × 10¹⁰ | 6 | 55% | — |
| 9 | Sperm whale social unit | 6.4 × 10¹⁰ | 4.1 × 10¹⁰ | 8 | 65% | — |
| 10 | Honeybee colony | 5.8 × 10¹⁰ | 4.9 × 10¹⁰ | 60,000 | 85% | — |
| 11 | Elephant family | 5.6 × 10¹⁰ | 3.2 × 10¹⁰ | 10 | 57% | — |
| 12 | Pinyon jay flock | 5.3 × 10¹⁰ | 3.2 × 10¹⁰ | 100 | 60% | — |
| 13 | Spotted hyena clan | 2.2 × 10¹⁰ | 6.7 × 10⁹ | 45 | 30% | 11 |
| 14 | African wild dog pack | 7.2 × 10⁹ | 4.8 × 10⁹ | 14 | 67% | 11 |
| 15 | Wolf pack | 4.2 × 10⁹ | 2.6 × 10⁹ | 8 | 63% | — |
| 16 | Meerkat group | 1.7 × 10⁹ | 1 × 10⁹ | 15 | 57% | 12 |
| 17 | Naked mole-rat colony | 4.1 × 10⁸ | 2.9 × 10⁸ | 78 | 70% | — |
- Christianity▲12.27 billion
- Islam▼12.02 billion
- Hinduism1.18 billion
- Folk religions400 million
- Buddhism324 million
- African traditional religions▲198.7 million
- Shinto▲187.9 million
- Sikhism▲125 million
- Judaism▼314.8 million
- Taoism▲18 million
- Baháʼí Faith▲18 million
- Falun Gong▲17 million
- Jainism▼34.45 million
- Spiritism3.8 million
- Cao Đài2.5 million
- Tenrikyo1.12 million
- Brahma Kumaris1 million
1051061071081091010members
Show the numbers
| # | Religion | Members | Following its instructions | Share directed | Hours a week |
|---|---|---|---|---|---|
| 1 | Christianity | 2.27 billion | 1.7 × 10¹⁸ | 0.88% | 0.9 h |
| 2 | Islam | 2.02 billion | 8.9 × 10¹⁸ | 5.1% | 5.4 h |
| 3 | Hinduism | 1.18 billion | 1.6 × 10¹⁸ | 1.6% | 1.6 h |
| 4 | Folk religions | 400 million | 2 × 10¹⁷ | 0.59% (proxy) | 0.6 h |
| 5 | Buddhism | 324 million | 7.5 × 10¹⁶ | 0.27% | 0.3 h |
| 6 | African traditional religions | 98.7 million | 5 × 10¹⁶ | 0.59% (proxy) | 0.6 h |
| 7 | Shinto | 87.9 million | 4.5 × 10¹⁶ | 0.59% (proxy) | 0.6 h |
| 8 | Sikhism | 25 million | 3.7 × 10¹⁶ | 1.7% | 1.8 h |
| 9 | Judaism | 14.8 million | 5.3 × 10¹⁶ | 4.2% | 4.4 h |
| 10 | Taoism | 8 million | 4.1 × 10¹⁵ | 0.59% (proxy) | 0.6 h |
| 11 | Baháʼí Faith | 8 million | 4.1 × 10¹⁵ | 0.59% (proxy) | 0.6 h |
| 12 | Falun Gong | 7 million | 3.6 × 10¹⁵ | 0.59% (proxy) | 0.6 h |
| 13 | Jainism | 4.45 million | 1.8 × 10¹⁶ | 4.6% | 4.8 h |
| 14 | Spiritism | 3.8 million | 1.9 × 10¹⁵ | 0.59% (proxy) | 0.6 h |
| 15 | Cao Đài | 2.5 million | 1.3 × 10¹⁵ | 0.59% (proxy) | 0.6 h |
| 16 | Tenrikyo | 1.12 million | 5.7 × 10¹⁴ | 0.59% (proxy) | 0.6 h |
| 17 | Brahma Kumaris | 1 million | 5.1 × 10¹⁴ | 0.59% (proxy) | 0.6 h |
- India▲11.46 billion
- China▼11.41 billion
- United States342 million
- Indonesia286 million
- Pakistan▲5255 million
- Nigeria▲1238 million
- Brazil▼2213 million
- Bangladesh▲5176 million
- Russia▼3144 million
- Ethiopia▲7135 million
- Mexico▼2132 million
- Japan▲11123 million
- Egypt▼2118 million
- Philippines117 million
- DR Congo▼3113 million
1041061081010residents
Show the numbers
| # | State | Residents | Following its instructions | Share directed | Hours a week |
|---|---|---|---|---|---|
| 1 | India | 1.46 billion | 2.5 × 10¹⁸ | 2% | 2.1 h |
| 2 | China | 1.41 billion | 5 × 10¹⁸ | 4.1% | 4.3 h |
| 3 | United States | 342 million | 1.6 × 10¹⁸ | 5.6% | 5.8 h |
| 4 | Indonesia | 286 million | 1 × 10¹⁸ | 4.2% | 4.4 h |
| 5 | Pakistan | 255 million | 5.7 × 10¹⁷ | 2.6% | 2.7 h |
| 6 | Nigeria | 238 million | 6.6 × 10¹⁷ | 3.2% | 3.4 h |
| 7 | Brazil | 213 million | 8.7 × 10¹⁷ | 4.8% | 5 h |
| 8 | Bangladesh | 176 million | 4.6 × 10¹⁷ | 3% | 3.1 h |
| 9 | Russia | 144 million | 7.5 × 10¹⁷ | 6.1% | 6.4 h |
| 10 | Ethiopia | 135 million | 3.9 × 10¹⁷ | 3.4% | 3.5 h |
| 11 | Mexico | 132 million | 5.9 × 10¹⁷ | 5.2% | 5.5 h |
| 12 | Japan | 123 million | 2.8 × 10¹⁷ | 2.7% | 2.8 h |
| 13 | Egypt | 118 million | 5.5 × 10¹⁷ | 5.4% | 5.7 h |
| 14 | Philippines | 117 million | 4.4 × 10¹⁷ | 4.4% | 4.6 h |
| 15 | DR Congo | 113 million | 4.7 × 10¹⁷ | 4.8% | 5.1 h |
| 16 | Vietnam | 102 million | 4.2 × 10¹⁷ | 4.8% | 5 h |
| 17 | Iran | 92.4 million | 3.5 × 10¹⁷ | 4.3% | 4.6 h |
| 18 | Turkey | 85.9 million | 4.3 × 10¹⁷ | 5.8% | 6 h |
| 19 | Germany | 83.5 million | 2.9 × 10¹⁷ | 4% | 4.2 h |
| 20 | Thailand | 71.6 million | 2.4 × 10¹⁷ | 3.9% | 4.1 h |
| 21 | Tanzania | 70.5 million | 2.2 × 10¹⁷ | 3.6% | 3.8 h |
| 22 | United Kingdom | 69.5 million | 2.8 × 10¹⁷ | 4.6% | 4.8 h |
| 23 | France | 68.7 million | 3.1 × 10¹⁷ | 5.3% | 5.5 h |
| 24 | South Africa | 64.7 million | 2.9 × 10¹⁷ | 5.1% | 5.4 h |
| 25 | Italy | 58.9 million | 2.1 × 10¹⁷ | 4.2% | 4.4 h |
| 26 | Kenya | 57.5 million | 2.3 × 10¹⁷ | 4.7% | 5 h |
| 27 | Myanmar | 54.9 million | 1.9 × 10¹⁷ | 4% | 4.2 h |
| 28 | Colombia | 53.4 million | 1.7 × 10¹⁷ | 3.7% | 3.9 h |
| 29 | South Korea | 51.7 million | 1.4 × 10¹⁷ | 3% | 3.2 h |
| 30 | Sudan | 51.7 million | 1.5 × 10¹⁷ | 3.4% | 3.6 h |
| 31 | Uganda | 51.4 million | 1.5 × 10¹⁷ | 3.4% | 3.6 h |
| 32 | Spain | 49.4 million | 1.8 × 10¹⁷ | 4.2% | 4.3 h |
| 33 | Algeria | 47.4 million | 3.2 × 10¹⁷ | 7.9% | 8.3 h |
| 34 | Iraq | 47 million | 1.1 × 10¹⁷ | 2.8% | 2.9 h |
| 35 | Argentina | 45.9 million | 1.7 × 10¹⁷ | 4.4% | 4.6 h |
| 36 | Afghanistan | 43.8 million | 1.8 × 10¹⁷ | 4.8% | 5 h |
| 37 | Yemen | 41.8 million | 1.3 × 10¹⁷ | 3.7% | 3.9 h |
| 38 | Canada | 41.7 million | 2 × 10¹⁷ | 5.5% | 5.8 h |
| 39 | Angola | 39 million | 1.4 × 10¹⁷ | 4.2% | 4.4 h |
| 40 | Ukraine | 39 million | 2.1 × 10¹⁷ | 6.2% | 6.5 h |
| 41 | Morocco | 38.4 million | 1.5 × 10¹⁷ | 4.5% | 4.7 h |
| 42 | Uzbekistan | 37.1 million | 2 × 10¹⁷ | 6.3% | 6.6 h |
| 43 | Saudi Arabia | 37 million | 1.4 × 10¹⁷ | 4.3% | 4.6 h |
| 44 | Poland | 36.4 million | 2.1 × 10¹⁷ | 6.7% | 7 h |
| 45 | Malaysia | 36 million | 1.8 × 10¹⁷ | 5.8% | 6.1 h |
| 46 | Mozambique | 35.6 million | 1.5 × 10¹⁷ | 4.8% | 5.1 h |
| 47 | Ghana | 35.1 million | 1.2 × 10¹⁷ | 3.9% | 4.1 h |
| 48 | Peru | 34.6 million | 1.4 × 10¹⁷ | 4.8% | 5 h |
| 49 | Madagascar | 32.7 million | 9.4 × 10¹⁶ | 3.3% | 3.5 h |
| 50 | Côte d’Ivoire | 32.7 million | 9.5 × 10¹⁶ | 3.4% | 3.5 h |
| 51 | Cameroon | 29.9 million | 9.8 × 10¹⁶ | 3.8% | 4 h |
| 52 | Nepal | 29.6 million | 1.1 × 10¹⁷ | 4.2% | 4.4 h |
| 53 | Venezuela | 28.5 million | 1.5 × 10¹⁷ | 6.1% | 6.4 h |
| 54 | Niger | 27.9 million | 6.4 × 10¹⁶ | 2.6% | 2.8 h |
| 55 | Australia | 27.6 million | 1.1 × 10¹⁷ | 4.7% | 5 h |
| 56 | North Korea | 26.6 million | 6.6 × 10¹⁷ | 29% | 30.1 h |
| 57 | Syria | 25.6 million | 9.8 × 10¹⁶ | 4.4% | 4.6 h |
| 58 | Mali | 25.2 million | 4.9 × 10¹⁶ | 2.3% | 2.4 h |
| 59 | Burkina Faso | 24.1 million | 5.7 × 10¹⁶ | 2.8% | 2.9 h |
| 60 | Taiwan | 23.3 million | 1 × 10¹⁷ | 5% | 5.3 h |
| 61 | Malawi | 22.2 million | 1 × 10¹⁷ | 5.4% | 5.6 h |
| 62 | Zambia | 21.9 million | 10 × 10¹⁶ | 5.3% | 5.5 h |
| 63 | Sri Lanka | 21.8 million | 1.1 × 10¹⁷ | 6% | 6.3 h |
| 64 | Chad | 21 million | 4.9 × 10¹⁶ | 2.7% | 2.9 h |
| 65 | Kazakhstan | 20.8 million | 1.4 × 10¹⁷ | 7.6% | 7.9 h |
| 66 | Chile | 19.9 million | 6.2 × 10¹⁶ | 3.6% | 3.8 h |
| 67 | Somalia | 19.7 million | 1.5 × 10¹⁶ | 0.88% | 0.9 h |
| 68 | Romania | 19 million | 7.5 × 10¹⁶ | 4.6% | 4.8 h |
| 69 | Senegal | 18.9 million | 5.9 × 10¹⁶ | 3.6% | 3.8 h |
| 70 | Guatemala | 18.7 million | 6 × 10¹⁶ | 3.7% | 3.9 h |
| 71 | Ecuador | 18.3 million | 6.6 × 10¹⁶ | 4.2% | 4.4 h |
| 72 | Netherlands | 18.1 million | 7.6 × 10¹⁶ | 4.9% | 5.1 h |
| 73 | Cambodia | 17.8 million | 7.4 × 10¹⁶ | 4.8% | 5.1 h |
| 74 | Zimbabwe | 17 million | 6.1 × 10¹⁶ | 4.2% | 4.4 h |
| 75 | Guinea | 15.1 million | 3.1 × 10¹⁶ | 2.4% | 2.5 h |
| 76 | Benin | 14.8 million | 4.4 × 10¹⁶ | 3.4% | 3.6 h |
| 77 | Rwanda | 14.6 million | 7 × 10¹⁶ | 5.6% | 5.8 h |
| 78 | Burundi | 14.4 million | 5.2 × 10¹⁶ | 4.2% | 4.4 h |
| 79 | Bolivia | 12.6 million | 5.8 × 10¹⁶ | 5.4% | 5.6 h |
| 80 | Tunisia | 12.3 million | 6.4 × 10¹⁶ | 6% | 6.3 h |
| 81 | South Sudan | 12.2 million | 4.4 × 10¹⁶ | 4.2% | 4.4 h |
| 82 | Belgium | 11.9 million | 4.5 × 10¹⁶ | 4.3% | 4.5 h |
| 83 | Haiti | 11.9 million | 1.9 × 10¹⁶ | 1.8% | 1.9 h |
| 84 | Jordan | 11.5 million | 4.9 × 10¹⁶ | 5% | 5.2 h |
| 85 | Dominican Republic | 11.5 million | 4.9 × 10¹⁶ | 4.9% | 5.2 h |
| 86 | United Arab Emirates | 11.5 million | 3.1 × 10¹⁶ | 3.1% | 3.2 h |
| 87 | Honduras | 11 million | 3.2 × 10¹⁶ | 3.3% | 3.5 h |
| 88 | Cuba | 10.9 million | 1.6 × 10¹⁷ | 17% | 17.4 h |
| 89 | Czechia | 10.9 million | 5.8 × 10¹⁶ | 6.2% | 6.5 h |
| 90 | Portugal | 10.8 million | 5.1 × 10¹⁶ | 5.5% | 5.7 h |
| 91 | Tajikistan | 10.8 million | 5.3 × 10¹⁶ | 5.7% | 6 h |
| 92 | Papua New Guinea | 10.8 million | 4.3 × 10¹⁶ | 4.6% | 4.8 h |
| 93 | Sweden | 10.6 million | 6.8 × 10¹⁶ | 7.5% | 7.8 h |
| 94 | Greece | 10.4 million | 4.9 × 10¹⁶ | 5.4% | 5.7 h |
| 95 | Azerbaijan | 10.2 million | 6.2 × 10¹⁶ | 7% | 7.4 h |
| 96 | Israel | 10.1 million | 6.4 × 10¹⁶ | 7.3% | 7.7 h |
| 97 | Hungary | 9.51 million | 5.7 × 10¹⁶ | 6.9% | 7.3 h |
| 98 | Austria | 9.21 million | 2.5 × 10¹⁶ | 3.2% | 3.4 h |
| 99 | Switzerland | 9.09 million | 3.9 × 10¹⁶ | 5% | 5.3 h |
| 100 | Belarus | 9.09 million | 7.9 × 10¹⁶ | 10% | 10.6 h |
| 101 | Sierra Leone | 8.82 million | 4.9 × 10¹⁶ | 6.5% | 6.8 h |
| 102 | Togo | 8.59 million | 3 × 10¹⁶ | 4% | 4.2 h |
| 103 | Laos | 7.87 million | 3.2 × 10¹⁶ | 4.8% | 5 h |
| 104 | Turkmenistan | 7.62 million | 4.2 × 10¹⁶ | 6.5% | 6.8 h |
| 105 | Libya | 7.46 million | 3.9 × 10¹⁶ | 6.1% | 6.4 h |
| 106 | Kyrgyzstan | 7.34 million | 4.1 × 10¹⁶ | 6.5% | 6.8 h |
| 107 | Paraguay | 7.01 million | 2.9 × 10¹⁶ | 4.8% | 5.1 h |
| 108 | Nicaragua | 7.01 million | 2.8 × 10¹⁶ | 4.6% | 4.8 h |
| 109 | Serbia | 6.55 million | 4.1 × 10¹⁶ | 7.2% | 7.5 h |
| 110 | Republic of the Congo | 6.48 million | 1.7 × 10¹⁶ | 3% | 3.1 h |
| 111 | Bulgaria | 6.43 million | 3.3 × 10¹⁶ | 5.9% | 6.2 h |
| 112 | El Salvador | 6.37 million | 3.2 × 10¹⁶ | 5.8% | 6.1 h |
| 113 | Singapore | 6.11 million | 1.9 × 10¹⁶ | 3.6% | 3.7 h |
| 114 | Denmark | 6.01 million | 3.9 × 10¹⁶ | 7.6% | 8 h |
| 115 | Lebanon | 5.85 million | 1.5 × 10¹⁶ | 3% | 3.1 h |
| 116 | Liberia | 5.73 million | 1.5 × 10¹⁶ | 3.1% | 3.3 h |
| 117 | Finland | 5.65 million | 3.2 × 10¹⁶ | 6.6% | 6.9 h |
| 118 | Norway | 5.61 million | 3.7 × 10¹⁶ | 7.6% | 8 h |
| 119 | Central African Republic | 5.51 million | 1.7 × 10¹⁶ | 3.5% | 3.7 h |
| 120 | Oman | 5.49 million | 2.9 × 10¹⁶ | 6.2% | 6.5 h |
| 121 | Ireland | 5.48 million | 3.4 × 10¹⁶ | 7.2% | 7.6 h |
| 122 | Slovakia | 5.41 million | 3.1 × 10¹⁶ | 6.7% | 7 h |
| 123 | Palestine | 5.41 million | 2 × 10¹⁶ | 4.3% | 4.5 h |
| 124 | New Zealand | 5.32 million | 2.7 × 10¹⁶ | 5.9% | 6.2 h |
| 125 | Mauritania | 5.32 million | 2 × 10¹⁶ | 4.3% | 4.5 h |
| 126 | Costa Rica | 5.15 million | 2.6 × 10¹⁶ | 5.9% | 6.2 h |
| 127 | Kuwait | 4.87 million | 2.7 × 10¹⁶ | 6.5% | 6.8 h |
| 128 | Panama | 4.57 million | 2.3 × 10¹⁶ | 5.9% | 6.2 h |
| 129 | Georgia | 3.94 million | 2.1 × 10¹⁶ | 6.1% | 6.4 h |
| 130 | Croatia | 3.88 million | 2.2 × 10¹⁶ | 6.7% | 7 h |
| 131 | Eritrea | 3.61 million | 1.5 × 10¹⁶ | 5% | 5.2 h |
| 132 | Mongolia | 3.57 million | 2.2 × 10¹⁶ | 7% | 7.3 h |
| 133 | Uruguay | 3.38 million | 1.7 × 10¹⁶ | 5.9% | 6.2 h |
| 134 | Bosnia and Herzegovina | 3.14 million | 1.6 × 10¹⁶ | 5.9% | 6.2 h |
| 135 | Namibia | 3.09 million | 1.8 × 10¹⁶ | 6.9% | 7.2 h |
| 136 | Armenia | 3.09 million | 1.7 × 10¹⁶ | 6.5% | 6.8 h |
| 137 | Qatar | 2.97 million | 9.6 × 10¹⁵ | 3.8% | 3.9 h |
| 138 | Lithuania | 2.89 million | 1.8 × 10¹⁶ | 7.4% | 7.7 h |
| 139 | Jamaica | 2.84 million | 1.2 × 10¹⁶ | 5% | 5.2 h |
| 140 | Gambia | 2.82 million | 1.1 × 10¹⁶ | 4.5% | 4.7 h |
| 141 | Gabon | 2.59 million | 9.9 × 10¹⁵ | 4.4% | 4.6 h |
| 142 | Botswana | 2.56 million | 1.3 × 10¹⁶ | 5.7% | 6 h |
| 143 | Lesotho | 2.36 million | 9.5 × 10¹⁵ | 4.7% | 4.9 h |
| 144 | Moldova | 2.36 million | 1.5 × 10¹⁶ | 7.3% | 7.6 h |
| 145 | Albania | 2.35 million | 1.2 × 10¹⁶ | 5.7% | 6 h |
| 146 | Guinea-Bissau | 2.25 million | 5.7 × 10¹⁵ | 2.9% | 3.1 h |
| 147 | Slovenia | 2.13 million | 1.2 × 10¹⁶ | 6.6% | 6.9 h |
| 148 | Equatorial Guinea | 1.94 million | 4 × 10¹⁵ | 2.4% | 2.5 h |
| 149 | Latvia | 1.85 million | 1.2 × 10¹⁶ | 7.6% | 7.9 h |
| 150 | North Macedonia | 1.82 million | 9.2 × 10¹⁵ | 5.9% | 6.2 h |
| 151 | Bahrain | 1.6 million | 5.9 × 10¹⁵ | 4.3% | 4.5 h |
| 152 | Kosovo | 1.58 million | 8 × 10¹⁵ | 5.9% | 6.2 h |
| 153 | Timor-Leste | 1.42 million | 8 × 10¹⁵ | 6.6% | 6.9 h |
| 154 | Cyprus | 1.37 million | 5.5 × 10¹⁵ | 4.7% | 4.9 h |
| 155 | Trinidad and Tobago | 1.37 million | 9.3 × 10¹⁵ | 7.9% | 8.2 h |
| 156 | Estonia | 1.37 million | 8.3 × 10¹⁵ | 7% | 7.4 h |
| 157 | Eswatini | 1.26 million | 7.5 × 10¹⁵ | 6.9% | 7.2 h |
| 158 | Mauritius | 1.24 million | 5.1 × 10¹⁵ | 4.8% | 5 h |
| 159 | Djibouti | 1.18 million | 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 | Solomon Islands | 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 and 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 and the Grenadines | 99,924 | 4.9 × 10¹⁴ | 5.7% | 5.9 h |
| 186 | Antigua and 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 and 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 | Marshall Islands | 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 (India)▲1140 million
- Chinese Communist Party▼1101 million
- Democratic Party (US)▲1344.9 million
- Republican Party (US)▲1638 million
- Indian National Congress▼226 million
- AIADMK (India)▼220 million
- DMK (India)▼220 million
- Prosperity Party (Ethiopia)▼115.7 million
- TVK (India)▼115 million
- Chama Cha Mapinduzi (Tanzania)▼113 million
- All Progressives Congress (Nigeria)▼112.9 million
- AK Party (Turkey)▼111.7 million
- Morena (Mexico)▼111.6 million
- Telugu Desam Party (India)▼110.1 million
- Cambodian People’s Party▼17 million
104105106107108109members
Show the numbers
| # | Party | Members | Following its instructions | Share directed | Hours a week |
|---|---|---|---|---|---|
| 1 | Bharatiya Janata Party (India) | 140 million | 6.3 × 10¹⁶ | 0.52% (proxy) | 0.6 h |
| 2 | Chinese Communist Party | 101 million | 8.2 × 10¹⁶ | 0.94% | 1 h |
| 3 | Democratic Party (US) | 44.9 million | 2.3 × 10¹⁵ | 0.06% | 0.1 h |
| 4 | Republican Party (US) | 38 million | 2 × 10¹⁵ | 0.06% | 0.1 h |
| 5 | Indian National Congress | 26 million | 1.2 × 10¹⁶ | 0.52% (proxy) | 0.6 h |
| 6 | AIADMK (India) | 20 million | 9 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 7 | DMK (India) | 20 million | 9 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 8 | Prosperity Party (Ethiopia) | 15.7 million | 7 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 9 | TVK (India) | 15 million | 6.7 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 10 | Chama Cha Mapinduzi (Tanzania) | 13 million | 5.8 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 11 | All Progressives Congress (Nigeria) | 12.9 million | 5.8 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 12 | AK Party (Turkey) | 11.7 million | 5.2 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 13 | Morena (Mexico) | 11.6 million | 5.2 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 14 | Telugu Desam Party (India) | 10.1 million | 4.5 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 15 | Cambodian People’s Party | 7 million | 3.1 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 16 | Workers’ Party of Korea | 6.17 million | 7.6 × 10¹⁵ | 1.4% | 1.5 h |
| 17 | FRELIMO (Mozambique) | 6 million | 2.7 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 18 | Communist Party of Vietnam | 5.6 million | 2.1 × 10¹⁵ | 0.44% | 0.5 h |
| 19 | Democratic Party of Korea | 5 million | 2.2 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 20 | People Power Party (South Korea) | 4.44 million | 2 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 21 | PSUV (Venezuela) | 4.24 million | 1.9 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 22 | ZANU-PF (Zimbabwe) | 4.06 million | 1.8 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 23 | UMNO (Malaysia) | 3.25 million | 1.5 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 24 | Justicialist Party (Argentina) | 3.1 million | 1.4 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 25 | Colorado Party (Paraguay) | 3 million | 1.3 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 26 | United Russia | 2.6 million | 1.2 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 27 | UzLiDeP (Uzbekistan) | 2.31 million | 1 × 10¹⁵ | 0.52% (proxy) | 0.6 h |
| 28 | PLD (Dominican Republic) | 2.18 million | 9.7 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 29 | MDB (Brazil) | 2.02 million | 9 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 30 | CHP (Turkey) | 1.88 million | 8.4 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 31 | Radical Civic Union (Argentina) | 1.79 million | 8 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 32 | Workers’ Party, PT (Brazil) | 1.67 million | 7.5 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 33 | PRM (Dominican Republic) | 1.55 million | 6.9 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 34 | Authentic Radical Liberal Party (Paraguay) | 1.55 million | 6.9 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 35 | Partido Federal ng Pilipinas | 1.5 million | 6.7 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 36 | PAS (Malaysia) | 1.3 million | 5.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 37 | PRD (Brazil) | 1.29 million | 5.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 38 | Progressistas (Brazil) | 1.29 million | 5.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 39 | PSDB (Brazil) | 1.26 million | 5.6 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 40 | PKR (Malaysia) | 1.15 million | 5.2 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 41 | Economic Freedom Fighters (South Africa) | 1.09 million | 4.9 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 42 | PDT (Brazil) | 1.07 million | 4.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 43 | União Brasil | 1.07 million | 4.8 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 44 | CPI(M) (India) | 1.02 million | 4.6 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 45 | Liberal Democratic Party (Japan) | 1 million | 4.5 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 46 | FLN (Algeria) | 1 million | 4.5 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 47 | Liberal Party, PL (Brazil) | 952,881 | 4.3 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 48 | Communist Party of Cuba | 700,000 | 2.7 × 10¹⁴ | 0.44% | 0.5 h |
| 49 | African National Congress | 688,454 | 3.1 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 50 | Conservative Party of Canada | 678,708 | 2.7 × 10¹⁴ | 0.46% | 0.5 h |
| 51 | CDU (Germany) | 364,202 | 1.6 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 52 | SPD (Germany) | 357,116 | 1.6 × 10¹⁴ | 0.52% (proxy) | 0.6 h |
| 53 | Labour Party (UK) | 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 (Germany) | 155,296 | 7 × 10¹³ | 0.52% (proxy) | 0.6 h |
| 56 | CSU (Germany) | 125,153 | 5.6 × 10¹³ | 0.52% (proxy) | 0.6 h |
| 57 | Conservative Party (UK) | 123,000 | 3.7 × 10¹³ | 0.35% | 0.4 h |
| 58 | New Democratic Party (Canada) | 100,542 | 4 × 10¹³ | 0.46% | 0.5 h |
| 59 | AfD (Germany) | 51,335 | 2.3 × 10¹³ | 0.52% (proxy) | 0.6 h |
- Walmart2.1 million
- Amazon▲11.58 million
- State Grid▼11.36 million
- China National Petroleum1.03 million
- 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
Show the numbers
| # | Corporation | Employees | Following its instructions | Hours a week | Share directed |
|---|---|---|---|---|---|
| 1 | Walmart | 2.1 million | 6.3 × 10¹⁶ | 36.3 h (United States) | 35% |
| 2 | Amazon | 1.58 million | 4.7 × 10¹⁶ | 36.3 h (United States) | 35% |
| 3 | State Grid | 1.36 million | 5.5 × 10¹⁶ | 49 h (China) | 47% |
| 4 | China National Petroleum | 1.03 million | 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 (India) | 55% |
| 8 | Allied Universal | 770,000 | 2.3 × 10¹⁶ | 36.52 h (United States) | 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 (Russia) | 38% |
| 11 | Volkswagen Group | 662,942 | 2 × 10¹⁶ | 35.95 h (Germany) | 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 (Germany) | 30% |
| 14 | Compass Group | 590,000 | 1.5 × 10¹⁶ | 31.7 h (United States) | 30% |
| 15 | Tata Consultancy Services | 584,510 | 2.8 × 10¹⁶ | 58.23 h (India) | 56% |
| 16 | DHL Group | 583,998 | 1.7 × 10¹⁶ | 35.81 h (Germany) | 34% |
| 17 | Sinopec | 513,434 | 2.1 × 10¹⁶ | 49 h (China) | 47% |
| 18 | Gazprom | 501,000 | 1.7 × 10¹⁶ | 40.47 h (Russia) | 39% |
| 19 | FedEx | 474,200 | 1.6 × 10¹⁶ | 39.98 h (United States) | 38% |
| 20 | Home Depot | 470,000 | 1.4 × 10¹⁶ | 36.3 h (United States) | 35% |
| 21 | China Mobile | 468,496 | 1.9 × 10¹⁶ | 49 h (China) | 47% |
| 22 | UPS | 460,000 | 1.5 × 10¹⁶ | 39.98 h (United States) | 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 (Philippines) | 45% |
| 25 | Teleperformance | 446,716 | 2.2 × 10¹⁶ | 58.89 h (India) | 56% |
| 26 | Jardine Matheson | 443,000 | 1.5 × 10¹⁶ | 41.93 h (Indonesia) | 40% |
| 27 | Sodexo | 426,464 | 1.1 × 10¹⁶ | 31.7 h (United States) | 30% |
| 28 | Capgemini | 423,400 | 2 × 10¹⁶ | 58.23 h (India) | 56% |
| 29 | Reliance Industries | 419,911 | 2.1 × 10¹⁶ | 59.57 h (India) | 57% |
| 30 | Edeka | 417,500 | 1.1 × 10¹⁶ | 31.49 h (Germany) | 30% |
| 31 | Luxshare Precision | 416,197 | 1.7 × 10¹⁶ | 49 h (China) | 47% |
| 32 | Target | 415,000 | 1.2 × 10¹⁶ | 36.3 h (United States) | 35% |
| 33 | Marriott International | 414,000 | 1.1 × 10¹⁶ | 31.7 h (United States) | 30% |
| 34 | Bosch | 412,400 | 1.2 × 10¹⁶ | 35.95 h (Germany) | 34% |
| 35 | ICBC | 409,758 | 1.6 × 10¹⁶ | 49 h (China) | 47% |
| 36 | Kroger | 403,000 | 1.2 × 10¹⁶ | 36.3 h (United States) | 35% |
| 37 | China Resources | 397,398 | 1.6 × 10¹⁶ | 49 h (China) | 47% |
| 38 | Magnit | 394,000 | 1.3 × 10¹⁶ | 39.69 h (Russia) | 38% |
| 39 | Toyota | 390,927 | 1.3 × 10¹⁶ | 39.6 h (Japan) | 38% |
| 40 | UnitedHealth Group | 390,000 | 1.2 × 10¹⁶ | 36.87 h (United States) | 35% |
| 41 | X5 Group | 389,000 | 1.3 × 10¹⁶ | 39.69 h (Russia) | 38% |
| 42 | Berkshire Hathaway | 387,800 | 1.2 × 10¹⁶ | 37.75 h (United States) | 36% |
| 43 | AVIC | 384,000 | 1.5 × 10¹⁶ | 49 h (China) | 47% |
| 44 | Ahold Delhaize | 384,000 | 1.1 × 10¹⁶ | 36.3 h (United States) | 35% |
| 45 | China Telecom | 381,436 | 1.5 × 10¹⁶ | 49 h (China) | 47% |
| 46 | Starbucks | 381,000 | 9.9 × 10¹⁵ | 31.7 h (United States) | 30% |
| 47 | Rewe Group | 380,000 | 9.8 × 10¹⁵ | 31.49 h (Germany) | 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 (United States) | 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 (Mexico) | 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 (United Kingdom) | 30% |
| 54 | Cognizant | 351,600 | 1.7 × 10¹⁶ | 58.23 h (India) | 56% |
| 55 | NTT | 344,196 | 1.1 × 10¹⁶ | 40.2 h (Japan) | 38% |
| 56 | Costco | 341,000 | 1 × 10¹⁶ | 36.3 h (United States) | 35% |
| 57 | Infosys | 328,594 | 1.6 × 10¹⁶ | 58.23 h (India) | 56% |
| 58 | ISS | 325,000 | 1.6 × 10¹⁶ | 58.89 h (India) | 56% |
| 59 | Securitas | 322,000 | 9.7 × 10¹⁵ | 36.52 h (United States) | 35% |
| 60 | HCA Healthcare | 320,000 | 9.7 × 10¹⁵ | 36.87 h (United States) | 35% |
| 61 | Rosneft | 320,000 | 1.1 × 10¹⁶ | 40.47 h (Russia) | 39% |
| 62 | JPMorgan Chase | 318,512 | 1.1 × 10¹⁶ | 40.14 h (United States) | 38% |
| 63 | Siemens | 318,000 | 9.4 × 10¹⁵ | 35.95 h (Germany) | 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 (United States) | 35% |
| 66 | PepsiCo | 306,000 | 1 × 10¹⁶ | 40.7 h (United States) | 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 (United Kingdom) | 33% |
| 71 | CVS Health | 300,000 | 8.9 × 10¹⁵ | 36.3 h (United States) | 35% |
Animals and other organisms
From single cells with no neurons to the human brain. Neuron counts are measured where they exist.
No neurons
- Escherichia coliEscherichia coli0 measuredA single cell with no neurons. It steers toward food and has a chemical memory that lasts a few seconds.
- Slime mouldPhysarum polycephalum0 measuredA single giant cell with no neurons. It solves mazes and grows efficient transport networks.
- Sensitive plantMimosa pudica0 measuredA plant whose leaves fold when touched. One study reported that it learns to ignore harmless touches. A later re-analysis disputed that.
- SpongePorifera (e.g. Amphimedon queenslandica)0 measuredAn animal with no neurons or synapses that still contracts its whole body in a coordinated way.
Nerve nets, no brain
- HydraHydra vulgarisabout 3,000 to 5,000 estimateA small freshwater cnidarian. It's controlled by a diffuse nerve net and has no brain.
- Box jellyfishTripedalia cystophoraNo published countA box jellyfish has no central brain, yet it showed associative learning.
Invertebrates with brains
- RoundwormCaenorhabditis elegans302 measuredA worm about a millimetre long with exactly 302 neurons. It was the first animal to have its complete wiring diagram mapped.
- Fruit flyDrosophila melanogaster139,255 measuredThe largest complete brain wiring diagram mapped so far, covering about 139,000 neurons.
- HoneybeeApis melliferaabout 1 million estimateAbout a million neurons, and bees can learn abstract concepts such as same and different.
- Ant (Camponotus / Formica)Camponotus spp.No published countA small brain that handles long-distance navigation by counting steps (path integration) and remembering landmarks.
- Portia jumping spiderPortia fimbriata / labiataNo published countA jumping spider that plans detours. It takes routes that lead away from its prey and out of sight.
- Shore crab / crayfishCarcinus maenas; Procambarus / Astacus spp.No published countCrabs and crayfish. A 2021 government review found strong evidence that true crabs are sentient.
- Common octopusOctopus vulgaris500 million estimateMost of its neurons are in its arms. A 2021 review found very strong evidence that it's sentient.
Fish and amphibians
- ZebrafishDanio rerioNo published countA small fish widely used in neuroscience. Its neurons haven't been fully counted. The adult brain has about 10 million cells.
- Cleaner wrasseLabroides dimidiatusNo published countA reef fish that passed a version of the mirror mark test. People disagree about what that shows.
- FrogAnura (e.g. Xenopus / Rana)No published countThe 2024 New York Declaration says there's a realistic possibility of conscious experience in amphibians.
Birds
- Rock pigeonColumba livia310 million measuredHundreds of millions of neurons in a small brain. Pigeons can sort complex images into categories.
- Common ravenCorvus corax2.17 billion measuredOver two billion neurons, densely packed. Ravens remember individual competitors.
- New Caledonian crowCorvus moneduloidesNo published countIt makes hooked tools from twigs and wire. Its neurons haven't been counted.
- African grey parrotPsittacus erithacus1.57 billion measuredAbout 1.6 billion neurons. Decades of laboratory study of numerosity and categorisation.
Mammals
- MouseMus musculus70.9 million measuredAbout 71 million neurons. Mice show facial expressions of pain that can be measured.
- RatRattus norvegicus200 million measuredAbout 200 million neurons. Rats release trapped cagemates.
- Domestic catFelis catus1.22 billion measuredAbout 250 million neurons in the cerebral cortex.
- Domestic dogCanis familiaris1.8 to 2.6 billion, depending on the dog measuredUp to about 600 million cortical neurons, depending on the dog. That's more than cats.
- Domestic pigSus scrofa domesticus2.22 billion measuredPigs learn what a mirror shows and use it to find food.
- Bottlenose dolphinTursiops truncatusNo published countPasses mirror self-recognition tests. There's no measured neuron count for the bottlenose dolphin.
- African elephantLoxodonta africana257 billion measured257 billion neurons, three times as many as a human, almost all in the cerebellum.
- ChimpanzeePan troglodytesabout 28 billion (predicted from scaling rules, not counted) estimateOne of our two closest living relatives (along with the bonobo). It passes the mirror test.
- HumanHomo sapiens86.1 billion measured86 billion neurons, 16 billion of them in the cerebral cortex. We're the only species whose members can give verbal reports of their experience.
Collective systems
Groups that make decisions no single member makes alone. They're studied as collective intelligence, not as minds.
- Ant coloniesColonies pick the better nest because individual ants follow a quorum threshold. No ant compares the sites overall.
- Honeybee swarmsSwarms pick the best cavity through competition between scouts and a quorum. Stop signals provide cross-inhibition, which breaks deadlocks.
- Starling flocksEach bird tracks a fixed number of neighbours (about 6-7), not every bird within a fixed distance. This is called topological, not metric, interaction.
- Fish shoalsShoals use quorum rules to decide. Large groups can sense environmental gradients that no single fish can.
Artificial systems
Kinds of machine intelligence, sorted by how they work. No current system is considered a strong candidate for consciousness.
- Rule-based expert systemsPrograms that encode a domain expert's knowledge as explicit if-then rules. An inference engine applies the rules in sequence to reach conclusions, usually with an explanation of its reasoning.
- Game-tree search engines (chess)Programs that enumerate possible move sequences to a fixed depth and score the resulting positions with a hand-built evaluation function. They prune branches that can't change the choice (minimax with alpha-beta pruning).
- Deep reinforcement learning agentsNeural networks trained by trial and error to maximise a reward signal. They learn a policy or value function by interacting with an environment, often a simulated one such as a game.
- Large language modelsTransformer neural networks trained to predict the next token in very large text corpora, then often tuned on human feedback.
- Embodied robotsMachines with sensors and actuators that act in the physical world. They often use layered reactive control instead of a central world model, so behaviour emerges from the loop between body and environment.
- Neuromorphic hardwareChips whose circuits imitate spiking neurons and synapses. They are event-driven and use little power, unlike a conventional processor-and-memory design.
Hypothetical minds
Minds proposed in the scientific and philosophical literature. Nobody has observed one.
- Whole-brain emulationunobservedScanning a brain in fine detail and running a software model of it.
- Simulated minds in ancestor simulationsunobservedBostrom's trilemma: either almost no civilisations reach a posthuman stage, or almost none run ancestor simulations, or we almost certainly live in one.
- Boltzmann brainsunobservedBrain-like observers that appear out of rare thermal or quantum fluctuations.
- Mind uploading via gradual replacementunobservedReplacing neurons one by one with functionally identical artificial parts.
- SuperintelligenceunobservedA hypothetical intellect greatly exceeding human performance in virtually all domains. Bostrom's book analyses the routes to it (whole-brain emulation, biological cognition, AI), its forms and the control problems.
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