List of large language models

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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.

Contents

This page lists notable large language models.

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.

NameRelease date [a] DeveloperNumber of parameters (billion) [b] Corpus sizeTraining cost (petaFLOP-day)License [c] Notes
GPT-1 June 2018 OpenAI 0.1171 [1] MIT [2] First GPT model, decoder-only transformer. Trained for 30 days on 8 P600 GPUs.
BERT October 2018 Google 0.340 [3] 3.3 billion words [3] 9 [4] Apache 2.0 [5] An early and influential language model. [6] Encoder-only and thus not built to be prompted or generative. [7] Training took 4 days on 64 TPUv2 chips. [8]
T5 October 2019Google11 [9] 34 billion tokens [9] Apache 2.0 [10] Base model for many Google projects, such as Imagen. [11]
XLNet June 2019 Google 0.340 [12] 33 billion words330Apache 2.0 [13] An alternative to BERT; designed as encoder-only. Trained on 512 TPU v3 chips for 5.5 days. [14]
GPT-2 February 2019 OpenAI 1.5 [15] 40GB [16] (~10 billion tokens) [17] 28 [18] MIT [19] Trained on 32 TPUv3 chips for 1 week. [18]
GPT-3 May 2020OpenAI175 [20] 300 billion tokens [17] 3640 [21] ProprietaryA fine-tuned variant of GPT-3, termed GPT-3.5, was made available to the public through a web interface called ChatGPT in 2022. [22]
GPT-NeoMarch 2021 EleutherAI 2.7 [23] 825 GiB [24] MIT [25] 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. [25]
GPT-J June 2021 EleutherAI 6 [26] 825 GiB [24] 200 [27] Apache 2.0GPT-3-style language model
Megatron-Turing NLGOctober 2021 [28] Microsoft and Nvidia 530 [29] 338.6 billion tokens [29] 38000 [30] Restricted web accessTrained for 3 months on over 2000 A100 GPUs on the NVIDIA Selene Supercomputer, for over 3 million GPU-hours. [30]
Ernie 3.0 TitanDecember 2021 Baidu 260 [31] 4 TbProprietaryChinese-language LLM. Ernie Bot is based on this model.
Claude [32] December 2021 Anthropic 52 [33] 400 billion tokens [33] betaFine-tuned for desirable behavior in conversations. [34]
GLaM (Generalist Language Model)December 2021Google1200 [35] 1.6 trillion tokens [35] 5600 [35] ProprietarySparse mixture of experts model, making it more expensive to train but cheaper to run inference compared to GPT-3.
GopherDecember 2021 DeepMind 280 [36] 300 billion tokens [37] 5833 [38] ProprietaryLater developed into the Chinchilla model.
LaMDA (Language Models for Dialog Applications)January 2022Google137 [39] 1.56T words, [39] 168 billion tokens [37] 4110 [40] ProprietarySpecialized for response generation in conversations.
GPT-NeoXFebruary 2022 EleutherAI 20 [41] 825 GiB [24] 740 [27] Apache 2.0based on the Megatron architecture
Chinchilla March 2022 DeepMind 70 [42] 1.4 trillion tokens [42] [37] 6805 [38] ProprietaryReduced-parameter model trained on more data. Used in the Sparrow bot. Often cited for its neural scaling law.
PaLM (Pathways Language Model)April 2022Google540 [43] 768 billion tokens [42] 29,250 [38] ProprietaryTrained for ~60 days on ~6000 TPU v4 chips. [38] As of October 2024, it is the largest dense Transformer published.
OPT (Open Pretrained Transformer)May 2022 Meta 175 [44] 180 billion tokens [45] 310 [27] Non-commercial research [d] GPT-3 architecture with some adaptations from Megatron. Uniquely, the training logbook written by the team was published. [46]
YaLM 100BJune 2022 Yandex 100 [47] 1.7TB [47] Apache 2.0English-Russian model based on Microsoft's Megatron-LM.
MinervaJune 2022Google540 [48] 38.5B tokens from webpages filtered for mathematical content and from papers submitted to the arXiv preprint server [48] ProprietaryFor solving "mathematical and scientific questions using step-by-step reasoning". [49] Initialized from PaLM models, then finetuned on mathematical and scientific data.
BLOOM July 2022Large collaboration led by Hugging Face 175 [50] 350 billion tokens (1.6TB) [51] Responsible AIEssentially GPT-3 but trained on a multi-lingual corpus (30% English excluding programming languages)
GalacticaNovember 2022 Meta 120106 billion tokens [52] unknownCC-BY-NC-4.0Trained on scientific text and modalities.
AlexaTM (Teacher Models)November 2022 Amazon 20 [53] 1.3 trillion [54] proprietary [55] bidirectional sequence-to-sequence architecture
LLaMA (Large Language Model Meta AI)February 2023 Meta AI 65 [56] 1.4 trillion [56] 6300 [57] Non-commercial research [e] Corpus has 20 languages. "Overtrained" (compared to Chinchilla scaling law) for better performance with fewer parameters. [56]
GPT-4 March 2023OpenAIUnknown [f] (According to rumors: 1760) [59] UnknownUnknownproprietaryAvailable for ChatGPT Plus users and used in several products.
ChameleonJune 2024 Meta AI 34 [60] 4.4 trillion
Cerebras-GPTMarch 2023 Cerebras 13 [61] 270 [27] Apache 2.0Trained with Chinchilla formula.
FalconMarch 2023 Technology Innovation Institute 40 [62] 1 trillion tokens, from RefinedWeb (filtered web text corpus) [63] plus some "curated corpora". [64] 2800 [57] Apache 2.0 [65]
BloombergGPTMarch 2023 Bloomberg L.P. 50363 billion token dataset based on Bloomberg's data sources, plus 345 billion tokens from general purpose datasets [66] ProprietaryTrained on financial data from proprietary sources, for financial tasks.
PanGu-Σ March 2023 Huawei 1085329 billion tokens [67] Proprietary
OpenAssistant [68] March 2023 LAION 171.5 trillion tokensApache 2.0Trained on crowdsourced open data
Jurassic-2 [69] March 2023 AI21 Labs UnknownUnknownProprietaryMultilingual [70]
PaLM 2 (Pathways Language Model 2)May 2023Google340 [71] 3.6 trillion tokens [71] 85,000 [57] ProprietaryWas used in Bard chatbot. [72]
Llama 2July 2023Meta AI70 [73] 2 trillion tokens [73] 21,000Llama 2 license1.7 million A100-hours. [74]
Claude 2 July 2023AnthropicUnknownUnknownUnknownProprietaryUsed in Claude chatbot. [75]
Granite 13b July 2023 IBM UnknownUnknownUnknownProprietaryUsed in IBM Watsonx. [76]
Mistral 7BSeptember 2023 Mistral AI 7.3 [77] UnknownApache 2.0
Claude 2.1 November 2023AnthropicUnknownUnknownUnknownProprietaryUsed in Claude chatbot. Has a context window of 200,000 tokens, or ~500 pages. [78]
Grok-1 [79] November 2023 xAI 314UnknownUnknownApache 2.0Used in Grok chatbot. Grok-1 has a context length of 8,192 tokens and has access to X (Twitter). [80]
Gemini 1.0 December 2023 Google DeepMind UnknownUnknownUnknownProprietaryMultimodal model, comes in three sizes. Used in the chatbot of the same name. [81]
Mixtral 8x7BDecember 2023 Mistral AI 46.7UnknownUnknownApache 2.0Outperforms GPT-3.5 and Llama 2 70B on many benchmarks. [82] Mixture of experts model, with 12.9 billion parameters activated per token. [83]
Mixtral 8x22BApril 2024 Mistral AI 141UnknownUnknownApache 2.0 [84]
DeepSeek-LLM November 29, 2023DeepSeek672T tokens [85] : table 2 12,000DeepSeek LicenseTrained on English and Chinese text. 1e24 FLOPs for 67B. 1e23 FLOPs for 7B [85] : figure 5 
Phi-2 December 2023Microsoft2.71.4T tokens419 [86] MITTrained on real and synthetic "textbook-quality" data, for 14 days on 96 A100 GPUs. [86]
Gemini 1.5 February 2024 Google DeepMind UnknownUnknownUnknownProprietaryMultimodal model, based on a Mixture-of-Experts (MoE) architecture. Context window above 1 million tokens. [87]
Gemini Ultra February 2024 Google DeepMind UnknownUnknownUnknown
GemmaFebruary 2024 Google DeepMind 76T tokensUnknownGemma Terms of Use [88]
Claude 3 March 2024AnthropicUnknownUnknownUnknownProprietaryIncludes three models, Haiku, Sonnet, and Opus. [89]
Nova October 2024 Rubik's AI UnknownUnknownUnknownProprietaryIncludes three models, Nova-Instant, Nova-Air, and Nova-Pro.
DBRX March 2024 Databricks and Mosaic ML 13612T TokensDatabricks Open Model LicenseTraining cost 10 million USD.
Fugaku-LLMMay 2024 Fujitsu, Tokyo Institute of Technology, etc.13380B TokensThe largest model ever trained on CPU-only, on the Fugaku. [90]
Phi-3 April 2024Microsoft14 [91] 4.8T TokensMITMicrosoft markets them as "small language model". [92]
Granite Code Models May 2024 IBM UnknownUnknownUnknownApache 2.0
Qwen2June 2024 Alibaba Cloud 72 [93] 3T TokensUnknownQwen LicenseMultiple sizes, the smallest being 0.5B.
DeepSeek-V2 June 2024DeepSeek2368.1T tokens28,000DeepSeek License1.4M hours on H800. [94]
Nemotron-4June 2024 Nvidia 3409T Tokens200,000NVIDIA Open Model LicenseTrained for 1 epoch. Trained on 6144 H100 GPUs between December 2023 and May 2024. [95] [96]
Llama 3.1July 2024Meta AI40515.6T tokens440,000Llama 3 license405B version took 31 million hours on H100-80GB, at 3.8E25 FLOPs. [97] [98]
DeepSeek-V3 December 2024 DeepSeek 67114.8T tokens56,000DeepSeek License2.788M hours on H800 GPUs. [99]
Amazon NovaDecember 2024 Amazon UnknownUnknownUnknownProprietaryIncludes three models, Nova Micro, Nova Lite, and Nova Pro [100]
DeepSeek-R1 January 2025DeepSeek671Not applicableUnknownMITNo pretraining. Reinforcement-learned upon V3-Base. [101] [102]
Qwen2.5January 2025Alibaba7218T tokensUnknownQwen License7 dense models, with parameter count from 0.5B to 72B. They also released 2 MoE variants. [103]
MiniMax-Text-01January 2025 Minimax 4564.7T tokens [104] UnknownMinimax Model license [105] [104]
Gemini 2.0 February 2025 Google DeepMind UnknownUnknownUnknownProprietaryThree models released: Flash, Flash-Lite and Pro [106] [107] [108]
Mistral Large 24.02February 2024 Mistral AI UnknownUnknownUnknownProprietary [109]
Mistral Large 2 24.07July 2024 Mistral AI 123UnknownUnknownMistral Research License [109]
Mistral Large 2 24.11November 2024 Mistral AI 123UnknownUnknownMistral Research License [109]
Mistral Small 3 25.01January 2025 Mistral AI 32UnknownUnknownApache 2 [109]
Ministral 3B 24.10October 2024 Mistral AI 3UnknownUnknownProprietary [109]
Ministral 8B 24.10October 2024 Mistral AI 8UnknownUnknownMistral Research License [109]
Pixtral 24.09September 2024 Mistral AI 12UnknownUnknownApache 2 [109]
Pixtral Large 24.11November 2024 Mistral AI 123UnknownUnknownMistral Research License [109]

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 ..." [58]

References

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