List of large language models

A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.

This page lists notable large language models.

List

For the training cost column, 1 petaFLOP-day = 1 petaFLOP/sec × 1 day = 8.64E19 FLOP. Also, only the largest model's cost is written.

Name Release date[a] Developer Number of parameters (billion) [b] Corpus size Training cost (petaFLOP-day) License[c] Notes
Attention Is All You Need June 2017 Vaswani et al at Google 0.213 36 million English-French sentence pairs 0.09[1] Trained for 0.3M steps on 8 NVIDIA P100 GPUs.
GPT-1 June 2018 OpenAI 0.117 1[2] MIT[3] First GPT model, decoder-only transformer. Trained for 30 days on 8 P600 GPUs.
BERT October 2018 Google 0.340[4] 3.3 billion words[4] 9[5] Apache 2.0[6] An early and influential language model.[7]Encoder-only and thus not built to be prompted or generative.[8] Training took 4 days on 64 TPUv2 chips.[9]
T5 October 2019 Google 11[10] 34 billion tokens[10] Apache 2.0[11] Base model for many Google projects, such as Imagen.[12]
XLNet June 2019 Google 0.340[13] 33 billion words 330 Apache 2.0[14] An alternative to BERT; designed as encoder-only. Trained on 512 TPU v3 chips for 5.5 days.[15]
GPT-2 February 2019 OpenAI 1.5[16] 40GB[17] (~10 billion tokens)[18] 28[19] MIT[20] Trained on 32 TPUv3 chips for 1 week.[19]
GPT-3 May 2020 OpenAI 175[21] 300 billion tokens[18] 3640[22] Proprietary A fine-tuned variant of GPT-3, termed GPT-3.5, was made available to the public through a web interface called ChatGPT in 2022.[23]
GPT-Neo March 2021 EleutherAI 2.7[24] 825 GiB[25] MIT[26] The first of a series of free GPT-3 alternatives released by EleutherAI. GPT-Neo outperformed an equivalent-size GPT-3 model on some benchmarks, but was significantly worse than the largest GPT-3.[26]
GPT-J June 2021 EleutherAI 6[27] 825 GiB[25] 200[28] Apache 2.0 GPT-3-style language model
Megatron-Turing NLG October 2021 [29] Microsoft and Nvidia 530[30] 338.6 billion tokens[30] 38000[31] Restricted web access Trained for 3 months on over 2000 A100 GPUs on the NVIDIA Selene Supercomputer, for over 3 million GPU-hours.[31]
Ernie 3.0 Titan December 2021 Baidu 260[32] 4 Tb Proprietary Chinese-language LLM. Ernie Bot is based on this model.
Claude[33] December 2021 Anthropic 52[34] 400 billion tokens[34] beta Fine-tuned for desirable behavior in conversations.[35]
GLaM (Generalist Language Model) December 2021 Google 1200[36] 1.6 trillion tokens[36] 5600[36] Proprietary Sparse mixture of experts model, making it more expensive to train but cheaper to run inference compared to GPT-3.
Gopher December 2021 DeepMind 280[37] 300 billion tokens[38] 5833[39] Proprietary Later developed into the Chinchilla model.
LaMDA (Language Models for Dialog Applications) January 2022 Google 137[40] 1.56T words,[40] 168 billion tokens[38] 4110[41] Proprietary Specialized for response generation in conversations.
GPT-NeoX February 2022 EleutherAI 20[42] 825 GiB[25] 740[28] Apache 2.0 based on the Megatron architecture
Chinchilla March 2022 DeepMind 70[43] 1.4 trillion tokens[43][38] 6805[39] Proprietary Reduced-parameter model trained on more data. Used in the Sparrow bot. Often cited for its neural scaling law.
PaLM (Pathways Language Model) April 2022 Google 540[44] 768 billion tokens[43] 29,250[39] Proprietary Trained for ~60 days on ~6000 TPU v4 chips.[39] As of October 2024, it is the largest dense Transformer published.
OPT (Open Pretrained Transformer) May 2022 Meta 175[45] 180 billion tokens[46] 310[28] Non-commercial research[d] GPT-3 architecture with some adaptations from Megatron. Uniquely, the training logbook written by the team was published.[47]
YaLM 100B June 2022 Yandex 100[48] 1.7TB[48] Apache 2.0 English-Russian model based on Microsoft's Megatron-LM.
Minerva June 2022 Google 540[49] 38.5B tokens from webpages filtered for mathematical content and from papers submitted to the arXiv preprint server[49] Proprietary For solving "mathematical and scientific questions using step-by-step reasoning".[50] Initialized from PaLM models, then finetuned on mathematical and scientific data.
BLOOM July 2022 Large collaboration led by Hugging Face 175[51] 350 billion tokens (1.6TB)[52] Responsible AI Essentially GPT-3 but trained on a multi-lingual corpus (30% English excluding programming languages)
Galactica November 2022 Meta 120 106 billion tokens[53] Un­known CC-BY-NC-4.0 Trained on scientific text and modalities.
AlexaTM (Teacher Models) November 2022 Amazon 20[54] 1.3 trillion[55] Proprietary[56] bidirectional sequence-to-sequence architecture
LLaMA (Large Language Model Meta AI) February 2023 Meta AI 65[57] 1.4 trillion[57] 6300[58] Non-commercial research[e] Corpus has 20 languages. "Overtrained" (compared to Chinchilla scaling law) for better performance with fewer parameters.[57]
GPT-4 March 2023 OpenAI Un­known[f]
(According to rumors: 1760)[60] 		Un­known Un­known,
estimated 230,000.		 Proprietary Available for ChatGPT Plus users and used in several products.
Chameleon June 2024 Meta AI 34[61] 4.4 trillion
Cerebras-GPT March 2023 Cerebras 13[62] 270[28] Apache 2.0 Trained with Chinchilla formula.
Falcon March 2023 Technology Innovation Institute 40[63] 1 trillion tokens, from RefinedWeb (filtered web text corpus)[64] plus some "curated corpora".[65] 2800[58] Apache 2.0[66]
BloombergGPT March 2023 Bloomberg L.P. 50 363 billion token dataset based on Bloomberg's data sources, plus 345 billion tokens from general purpose datasets[67] Proprietary Trained on financial data from proprietary sources, for financial tasks.
PanGu-Σ March 2023 Huawei 1085 329 billion tokens[68] Proprietary
OpenAssistant[69] March 2023 LAION 17 1.5 trillion tokens Apache 2.0 Trained on crowdsourced open data
Jurassic-2[70] March 2023 AI21 Labs Un­known Un­known Proprietary Multilingual[71]
PaLM 2 (Pathways Language Model 2) May 2023 Google 340[72] 3.6 trillion tokens[72] 85,000[58] Proprietary Was used in Bard chatbot.[73]
Llama 2 July 2023 Meta AI 70[74] 2 trillion tokens[74] 21,000 Llama 2 license 1.7 million A100-hours.[75]
Claude 2 July 2023 Anthropic Un­known Un­known Un­known Proprietary Used in Claude chatbot.[76]
Granite 13b July 2023 IBM Un­known Un­known Un­known Proprietary Used in IBM Watsonx.[77]
Mistral 7B September 2023 Mistral AI 7.3[78] Un­known Apache 2.0
Claude 2.1 November 2023 Anthropic Un­known Un­known Un­known Proprietary Used in Claude chatbot. Has a context window of 200,000 tokens, or ~500 pages.[79]
Grok 1[80] November 2023 xAI 314 Un­known Un­known Apache 2.0 Used in Grok chatbot. Grok 1 has a context length of 8,192 tokens and has access to X (Twitter).[81]
Gemini 1.0 December 2023 Google DeepMind Un­known Un­known Un­known Proprietary Multimodal model, comes in three sizes. Used in the chatbot of the same name.[82]
Mixtral 8x7B December 2023 Mistral AI 46.7 Un­known Un­known Apache 2.0 Outperforms GPT-3.5 and Llama 2 70B on many benchmarks.[83] Mixture of experts model, with 12.9 billion parameters activated per token.[84]
Mixtral 8x22B April 2024 Mistral AI 141 Un­known Un­known Apache 2.0 [85]
DeepSeek-LLM November 29, 2023 DeepSeek 67 2T tokens[86]: table 2  12,000 DeepSeek License Trained on English and Chinese text. 1e24 FLOPs for 67B. 1e23 FLOPs for 7B[86]: figure 5 
Phi-2 December 2023 Microsoft 2.7 1.4T tokens 419[87] MIT Trained on real and synthetic "textbook-quality" data, for 14 days on 96 A100 GPUs.[87]
Gemini 1.5 February 2024 Google DeepMind Unknown Un­known Un­known Proprietary Multimodal model, based on a Mixture-of-Experts (MoE) architecture. Context window above 1 million tokens.[88]
Gemini Ultra February 2024 Google DeepMind Unknown Un­known Un­known
Gemma February 2024 Google DeepMind 7 6T tokens Unknown Gemma Terms of Use[89]
Claude 3 March 2024 Anthropic Un­known Un­known Un­known Proprietary Includes three models, Haiku, Sonnet, and Opus.[90]
Nova October 2024 Rubik's AI Un­known Un­known Un­known Proprietary Previous three models, Nova-Instant, Nova-Air, and Nova-Pro. Company shifted to Sonus AI.
Sonus[91] January 2025 Rubik's AI Un­known Un­known Un­known Proprietary
DBRX March 2024 Databricks and Mosaic ML 136 12T Tokens Databricks Open Model License Training cost 10 million USD.
Fugaku-LLM May 2024 Fujitsu, Tokyo Institute of Technology, etc. 13 380B Tokens The largest model ever trained on CPU-only, on the Fugaku.[92]
Phi-3 April 2024 Microsoft 14[93] 4.8T Tokens MIT Microsoft markets them as "small language model".[94]
Granite Code Models May 2024 IBM Unknown Un­known Un­known Apache 2.0
Qwen2 June 2024 Alibaba Cloud 72[95] 3T Tokens Un­known Qwen License Multiple sizes, the smallest being 0.5B.
DeepSeek-V2 June 2024 DeepSeek 236 8.1T tokens 28,000 DeepSeek License 1.4M hours on H800.[96]
Nemotron-4 June 2024 Nvidia 340 9T Tokens 200,000 NVIDIA Open Model License Trained for 1 epoch. Trained on 6144 H100 GPUs between December 2023 and May 2024.[97][98]
Llama 3.1 July 2024 Meta AI 405 15.6T tokens 440,000 Llama 3 license 405B version took 31 million hours on H100-80GB, at 3.8E25 FLOPs.[99][100]
DeepSeek-V3 December 2024 DeepSeek 671 14.8T tokens 56,000 MIT 2.788M hours on H800 GPUs.[101] Originally released under the DeepSeek License, then re-released under the MIT License as "DeepSeek-V3-0324" in March 2025.[102]
Amazon Nova December 2024 Amazon Un­known Un­known Un­known Proprietary Includes three models, Nova Micro, Nova Lite, and Nova Pro[103]
DeepSeek-R1 January 2025 DeepSeek 671 Not applicable Un­known MIT No pretraining. Reinforcement-learned upon V3-Base.[104][105]
Qwen2.5 January 2025 Alibaba 72 18T tokens Un­known Qwen License 7 dense models, with parameter count from 0.5B to 72B. They also released 2 MoE variants.[106]
MiniMax-Text-01 January 2025 Minimax 456 4.7T tokens[107] Un­known Minimax Model license [108][107]
Gemini 2.0 February 2025 Google DeepMind Unknown Un­known Un­known Proprietary Three models released: Flash, Flash-Lite and Pro[109][110][111]
Mistral Large November 2024 Mistral AI 123 Un­known Un­known Mistral Research License Upgraded over time. The latest version is 24.11.[112]
Pixtral November 2024 Mistral AI 123 Un­known Un­known Mistral Research License Multimodal. There is also a 12B version which is under Apache 2 license.[112]
Grok 3 February 2025 xAI Un­known Un­known Un­known,
estimated 5,800,000. 		Proprietary Training cost claimed "10x the compute of previous state-of-the-art models".[113]
Llama 4 April 5, 2025 Meta AI 400 40T tokens Llama 4 license [114][115]
Qwen3 April 2025 Alibaba Cloud 235 36T tokens Un­known Apache 2.0 Multiple sizes, the smallest being 0.6B.[116]

See also

Notes

  1. ^ This is the date that documentation describing the model's architecture was first released.
  2. ^ In many cases, researchers release or report on multiple versions of a model having different sizes. In these cases, the size of the largest model is listed here.
  3. ^ This is the license of the pre-trained model weights. In almost all cases the training code itself is open-source or can be easily replicated.
  4. ^ The smaller models including 66B are publicly available, while the 175B model is available on request.
  5. ^ Facebook's license and distribution scheme restricted access to approved researchers, but the model weights were leaked and became widely available.
  6. ^ As stated in Technical report: "Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method ..."[59]

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Prefix: a b c d e f g h i j k l m n o p q r s t u v w x y z 0 1 2 3 4 5 6 7 8 9

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