List of large language models

Last updated

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

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.

NameRelease date [a] DeveloperNumber of parameters (billion) [b] Corpus sizeTraining cost (petaFLOP-day)License [c] Notes
GPT-1 June 11, 2018 OpenAI 0.117Unknown1 [1] MIT [2] First GPT model, decoder-only transformer. Trained for 30 days on 8 P600 GPUs. [3]
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 words330 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-NeoMarch 2021 EleutherAI 2.7 [24] 825 GiB [25] Unknown 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 NLGOctober 2021 [29] Microsoft and Nvidia 530 [30] 338.6 billion tokens [30] 38000 [31] UnreleasedTrained for 3 months on over 2000 A100 GPUs on the NVIDIA Selene Supercomputer, for over 3 million GPU-hours [31]
Ernie 3.0 TitanDecember 2021 Baidu 260 [32] 4TBUnknown Proprietary Chinese-language LLM. Ernie Bot is based on this model.
Claude [33] December 2021 Anthropic 52 [34] 400 billion tokens [34] Unknown Proprietary 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.
GopherDecember 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-NeoXFebruary 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]
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 100BJune 2022 Yandex 100 [48] 1.7TB [48] Unknown Apache 2.0 English-Russian model based on Microsoft's Megatron-LM
MinervaJune 2022 Google 540 [49] 38.5B tokens from webpages filtered for mathematical content and from papers submitted to the arXiv preprint server [49] Unknown 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 2022Large collaboration led by Hugging Face 175 [51] 350 billion tokens (1.6TB) [52] UnknownResponsible AIEssentially GPT-3 but trained on a multi-lingual corpus (30% English excluding programming languages)
GalacticaNovember 2022 Meta 120106 billion tokens [53] UnknownCC-BY-NC-4.0Trained on scientific text and modalities.
AlexaTM (Teacher Models)November 2022 Amazon 20 [54] 1.3 trillion [55] Unknown Proprietary [56] Bidirectional sequence-to-sequence architecture
Llama 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] 		UnknownUnknown,
estimated 230,000		 Proprietary Available for all ChatGPT users now and used in several products.
Cerebras-GPTMarch 2023 Cerebras 13 [61] 270 [28] Apache 2.0 Trained 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 [58] 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] UnknownUnreleasedTrained on financial data from proprietary sources, for financial tasks
PanGu-Σ March 2023 Huawei 1085329 billion tokens [67] Unknown Proprietary
OpenAssistant [68] March 2023 LAION 171.5 trillion tokensUnknown Apache 2.0 Trained on crowdsourced open data
Jurassic-2 [69] March 2023 AI21 Labs UnknownUnknownUnknown Proprietary Multilingual [70]
PaLM 2 (Pathways Language Model 2)May 2023 Google 340 [71] 3.6 trillion tokens [71] 85,000 [58] Proprietary Was used in Bard chatbot. [72]
YandexGPT May 17, 2023 Yandex UnknownUnknownUnknown Proprietary Used in Alice chatbot.
Llama 2 July 2023 Meta AI 70 [73] 2 trillion tokens [73] 21,000 Llama 2 license 1.7 million A100-hours. [74]
Claude 2 July 2023 Anthropic UnknownUnknownUnknown Proprietary Used in Claude chatbot. [75]
Granite 13b July 2023 IBM UnknownUnknownUnknown Proprietary Used in IBM Watsonx. [76]
Mistral 7B September 2023 Mistral AI 7.3 [77] UnknownUnknown Apache 2.0
YandexGPT 2 September 7, 2023 Yandex UnknownUnknownUnknown Proprietary Used in Alice chatbot.
Claude 2.1 November 2023 Anthropic UnknownUnknownUnknown Proprietary Used in Claude chatbot. Has a context window of 200,000 tokens, or ~500 pages. [78]
Grok 1 [79] November 2023 xAI 314UnknownUnknown Apache 2.0 Used 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 UnknownUnknownUnknown Proprietary Multimodal model, comes in three sizes. Used in the chatbot of the same name. [81]
Mixtral 8x7B December 2023 Mistral AI 46.7UnknownUnknown Apache 2.0 Outperforms GPT-3.5 and Llama 2 70B on many benchmarks. [82] Mixture of experts model, with 12.9 billion parameters activated per token. [83]
DeepSeek-LLM November 29, 2023 DeepSeek 672T tokens [84] : table 2 12,000DeepSeek LicenseTrained on English and Chinese text. 1e24 FLOPs for 67B. 1e23 FLOPs for 7B [84] : figure 5 
Phi-2 December 2023 Microsoft 2.71.4T tokens419 [85] MIT Trained on real and synthetic "textbook-quality" data, for 14 days on 96 A100 GPUs. [85]
Gemini 1.5 February 2024 Google DeepMind UnknownUnknownUnknown Proprietary Multimodal model, based on a Mixture-of-Experts (MoE) architecture. Context window above 1 million tokens. [86]
Gemini Ultra February 2024 Google DeepMind UnknownUnknownUnknown Proprietary
GemmaFebruary 2024 Google DeepMind 76T tokensUnknownGemma Terms of Use [87]
Claude 3 March 2024 Anthropic UnknownUnknownUnknown Proprietary Includes three models, Haiku, Sonnet, and Opus. [88]
DBRX March 2024 Databricks and Mosaic ML 13612T tokensUnknownDatabricks Open Model License [89] [90] Training cost 10 million USD
YandexGPT 3 Pro March 28, 2024 Yandex UnknownUnknownUnknown Proprietary Used in Alice chatbot.
Fugaku-LLMMay 2024 Fujitsu, Tokyo Institute of Technology, etc.13380B tokensUnknownFugaku-LLM Terms of Use [91] The largest model ever trained on CPU-only, on the Fugaku [92]
ChameleonMay 2024 Meta AI 34 [93] 4.4 trillionUnknownNon-commercial research [94]
Mixtral 8x22B April 17, 2024 Mistral AI 141UnknownUnknown Apache 2.0 [95]
Phi-3 April 23, 2024 Microsoft 14 [96] 4.8T tokensUnknown MIT Microsoft markets them as "small language model". [97]
Granite Code Models May 2024 IBM UnknownUnknownUnknown Apache 2.0
YandexGPT 3 Lite May 28, 2024 Yandex UnknownUnknownUnknown Proprietary Used in Alice chatbot.
Qwen2 June 2024 Alibaba Cloud 72 [98] 3T tokensUnknownQwen LicenseMultiple sizes, the smallest being 0.5B.
DeepSeek-V2 June 2024 DeepSeek 2368.1T tokens28,000DeepSeek License1.4M hours on H800. [99]
Nemotron-4June 2024 Nvidia 3409T tokens200,000NVIDIA Open Model License [100] [101] Trained for 1 epoch. Trained on 6144 H100 GPUs between December 2023 and May 2024. [102] [103]
Claude 3.5 June 2024 Anthropic UnknownUnknownUnknown Proprietary Initially, only one model, Sonnet, was released. [104] In October 2024, Sonnet 3.5 was upgraded, and Haiku 3.5 became available. [105]
Llama 3.1 July 2024 Meta AI 40515.6T tokens440,000 Llama 3 license 405B version took 31 million hours on H100-80GB, at 3.8E25 FLOPs. [106] [107]
Grok-2 August 14, 2024 xAI UnknownUnknownUnknownxAI Community License Agreement [108] [109] Originally closed-source, then re-released as "Grok 2.5" under a source-available license in August 2025. [110] [111]
OpenAI o1 September 12, 2024 OpenAI UnknownUnknownUnknown Proprietary Reasoning model. [112]
YandexGPT 4 Lite and Pro October 24, 2024 Yandex UnknownUnknownUnknown Proprietary Used in Alice chatbot.
Mistral Large November 2024 Mistral AI 123UnknownUnknownMistral Research LicenseUpgraded over time. The latest version is 24.11. [113]
Pixtral November 2024 Mistral AI 123UnknownUnknownMistral Research LicenseMultimodal. There is also a 12B version which is under Apache 2 license. [113]
Phi-4 December 12, 2024 Microsoft 14 [114] 9.8T tokensUnknown MIT Microsoft markets them as "small language model". [115]
DeepSeek-V3 December 2024 DeepSeek 67114.8T tokens56,000 MIT 2.788M hours on H800 GPUs. [116] Originally released under the DeepSeek License, then re-released under the MIT License as "DeepSeek-V3-0324" in March 2025. [117]
Amazon NovaDecember 2024 Amazon UnknownUnknownUnknown Proprietary Includes three models, Nova Micro, Nova Lite, and Nova Pro [118]
DeepSeek-R1 January 2025 DeepSeek 671Not applicableUnknown MIT No pretraining. Reinforcement-learned upon V3-Base. [119] [120]
Qwen2.5 January 2025 Alibaba 7218T tokensUnknownQwen License7 dense models, with parameter count from 0.5B to 72B. They also released 2 MoE variants. [121]
MiniMax-Text-01January 2025 Minimax 4564.7T tokens [122] UnknownMinimax Model license [123] [122]
Gemini 2.0 February 2025 Google DeepMind UnknownUnknownUnknown Proprietary Three models released: Flash, Flash-Lite and Pro [124] [125] [126]
Claude 3.7 February 24, 2025 Anthropic UnknownUnknownUnknown Proprietary One model, Sonnet 3.7. [127]
YandexGPT 5 Lite Pretrain and Pro February 25, 2025 Yandex UnknownUnknownUnknown Proprietary Used in Alice Neural Network chatbot.
GPT-4.5 February 27, 2025 OpenAI UnknownUnknownUnknown Proprietary Largest non-reasoning model. [128]
Grok 3 February 2025 xAI UnknownUnknownUnknown,
estimated 5,800,000		 Proprietary Training cost claimed "10x the compute of previous state-of-the-art models". [129]
Gemini 2.5 March 25, 2025 Google DeepMind UnknownUnknownUnknown Proprietary Three models released: Flash, Flash-Lite and Pro [130]
YandexGPT 5 Lite Instruct March 31, 2025 Yandex UnknownUnknownUnknown Proprietary Used in Alice Neural Network chatbot.
Llama 4 April 5, 2025 Meta AI 40040T tokensUnknown Llama 4 license [131] [132]
OpenAI o3 and o4-miniApril 16, 2025 OpenAI UnknownUnknownUnknown Proprietary Reasoning models. [133]
Qwen3 April 2025 Alibaba Cloud 23536T tokensUnknown Apache 2.0 Multiple sizes, the smallest being 0.6B. [134]
Claude 4 May 22, 2025 Anthropic UnknownUnknownUnknown Proprietary Includes two models, Sonnet and Opus. [135]
Grok 4 July 9, 2025 xAI UnknownUnknownUnknown Proprietary
GLM-4.5July 29, 2025 Zhipu AI 35522T tokensUnknown MIT Released in 335B and 106B sizes. [136] Corpus size was calculated by combining the 15 trillion tokens and the 7 trillion tokens pre-training mix. [137]
GPT-OSS August 5, 2025 OpenAI 117UnknownUnknown Apache 2.0 Released in 20B and 120B sizes. [138]
Claude 4.1 August 5, 2025 Anthropic UnknownUnknownUnknown Proprietary Includes one model, Opus. [139]
GPT-5 August 7, 2025 OpenAI UnknownUnknownUnknown Proprietary Includes three models, GPT-5, GPT-5 mini, and GPT-5 nano. GPT-5 is available in ChatGPT and API. It includes thinking abilities. [140] [141]
DeepSeek-V3.1August 21, 2025 DeepSeek 67115.639T MIT Training size: 14.8T tokens, of DeepSeek V3 plus 839B tokens from the extension phases (630B + 209B) [142] It is a hybrid model that can switch between thinking and non-thinking modes. [143]
YandexGPT 5.1 Pro August 28, 2025 Yandex UnknownUnknownUnknown Proprietary Used in Alice Neural Network chatbot.
Apertus September 2, 2025 ETH Zurich and EPF Lausanne 7015 trillion [144] Unknown Apache 2.0 It's said to be the first LLM to be compliant with EU's Artificial Intelligence Act. [145]
Claude 4.5 September 29, 2025 Anthropic UnknownUnknownUnknown Proprietary Only one variant is available, Sonnet. [146]
DeepSeek-V3.2-ExpSeptember 29, 2025 DeepSeek 685 MIT This experimental model built upon v3.1-Terminus uses a custom efficient mechanism tagged DeepSeek Sparse Attention (DSA). [147] [148] [149]
GLM-4.6September 30, 2025 Zhipu AI 357 Apache 2.0 [150] [151] [152]
Alice AI LLM 1.0 October 28, 2025 Yandex UnknownUnknownUnknown Proprietary Available in Alice AI chatbot.

Timeline

Timeline of major LLM releases (2024–present)
Command R+Command RGrok-2Grok-1.5Mistral LargeLlama 3.1Llama 3Claude 3.5 SonnetClaude 3Gemini 2.0Gemini 1.5GPT-5GPT-4oList of large language models


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. LLMs may be licensed differently from the chatbots that use them; for the licenses of chatbots, see List of chatbots.
  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]

References

  1. ↑ "Improving language understanding with unsupervised learning". openai.com. June 11, 2018. Archived from the original on 2023-03-18. Retrieved 2023-03-18.
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  3. ↑ Radford, Alec (11 June 2018). "Improving language understanding with unsupervised learning". OpenAI . Retrieved 18 November 2025.
  4. 1 2 Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina (11 October 2018). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". arXiv: 1810.04805v2 [cs.CL].
  5. ↑ Prickett, Nicole Hemsoth (2021-08-24). "Cerebras Shifts Architecture To Meet Massive AI/ML Models". The Next Platform. Archived from the original on 2023-06-20. Retrieved 2023-06-20.
  6. ↑ "BERT". March 13, 2023. Archived from the original on January 13, 2021. Retrieved March 13, 2023– via GitHub.
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  31. 1 2 Rajbhandari, Samyam; Li, Conglong; Yao, Zhewei; Zhang, Minjia; Aminabadi, Reza Yazdani; Awan, Ammar Ahmad; Rasley, Jeff; He, Yuxiong (2022-07-21), DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale, arXiv: 2201.05596
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  38. 1 2 3 Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; et al. (29 March 2022). "Training Compute-Optimal Large Language Models". arXiv: 2203.15556 [cs.CL].
  39. 1 2 3 4 Table 20 and page 66 of PaLM: Scaling Language Modeling with Pathways Archived 2023-06-10 at the Wayback Machine
  40. 1 2 Cheng, Heng-Tze; Thoppilan, Romal (January 21, 2022). "LaMDA: Towards Safe, Grounded, and High-Quality Dialog Models for Everything". ai.googleblog.com. Archived from the original on 2022-03-25. Retrieved 2023-03-09.
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  45. ↑ Susan Zhang; Mona Diab; Luke Zettlemoyer. "Democratizing access to large-scale language models with OPT-175B". ai.facebook.com. Archived from the original on 2023-03-12. Retrieved 2023-03-12.
  46. ↑ Zhang, Susan; Roller, Stephen; Goyal, Naman; Artetxe, Mikel; Chen, Moya; Chen, Shuohui; Dewan, Christopher; Diab, Mona; Li, Xian; Lin, Xi Victoria; Mihaylov, Todor; Ott, Myle; Shleifer, Sam; Shuster, Kurt; Simig, Daniel; Koura, Punit Singh; Sridhar, Anjali; Wang, Tianlu; Zettlemoyer, Luke (21 June 2022). "OPT: Open Pre-trained Transformer Language Models". arXiv: 2205.01068 [cs.CL].
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