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List of large language models

From Wikipedia, the free encyclopedia

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.

List

For the training cost column, 1 petaFLOP-day equals 1 petaFLOP/sec × 1 day, or 8.64×1019 FLOP (floating point operations). Only the cost of the largest model is shown. The number of parameters is measured in billions,[a] and the training cost is measured in petaFLOP-days.

2018

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size Training cost License[c] Notes
GPT-1 Jun 11 OpenAI 0.117B Unknown 1[1] MIT[2]
First GPT model, decoder-only transformer. Trained for 30 days on 8 P600 GPUs.[3]
BERT Oct 2018 Google 0.340B[4] 3.3B 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.[4]
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2019

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size Training cost License[c] Notes
T5 Oct 2019 Google 11B[9] 34B tokens[9] Unknown Apache 2.0[10]
Base model for Google projects like Imagen.[11]
XLNet Jun 2019 Google 0.340B[12] 33B words 330 Apache 2.0[13]
An alternative to BERT; designed as encoder-only. Trained on 512 TPU v3 chips for 5.5 days.[14]
GPT-2 Feb 2019 OpenAI 1.5B[15] 40GB[16] (~10B tokens)[17] 28[18] MIT[19]
Trained on 32 TPUv3 chips for 1 week.[18]
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2020

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size Training cost License[c] Notes
GPT-3 May 2020 OpenAI 175B[20] 300B tokens[17] 3640[21] Proprietary
A fine-tuned variant of GPT-3, termed GPT-3.5, was made available to the public through ChatGPT in 2022.[22]
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2021

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size Training cost License[c] Notes
GPT-Neo Mar 2021 EleutherAI 2.7B[23] 825 GiB[24] Unknown 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]
HyperCLOVA May 2021[26] Naver 204B[27]
82B[28]		 560B Tokens[27] Unknown Unreleased
Its technical report says the model is the "first discovery on near 100B-scale non-English LM" by "constructing a large Korean-centric corpus of 560B tokens."[28]
GPT-J Jun 2021 EleutherAI 6B[29] 825 GiB[24] 200[30] Apache 2.0
Megatron-Turing NLG Oct 2021[31] Microsoft and Nvidia 530B[32] 338.6B tokens[32] 38000[33] Unreleased
Trained for 3 months on over 2000 A100 GPUs on the NVIDIA Selene Supercomputer, for over 3 million GPU-hours.[33]
Ernie 3.0 Titan Dec 2021 Baidu 260B[34] 4TB Unknown Proprietary
Claude[35] Dec 2021 Anthropic 52B[36] 400B tokens[36] Unknown
Fine-tuned for desirable behavior in conversations.[37]
GLaM (Generalist Language Model) Dec 2021 Google 1162B[38] 1.6T tokens[38] 5600[38]
Gopher Dec 2021 Google DeepMind 280B[39] 300B tokens[40] 5833[41]
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2022

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size Training cost License[c] Notes
LaMDA (Language Models for Dialog Applications) Jan 2022 Google 137B[42] 1.56T words,[42] 168B tokens[40] 4110[43] Proprietary
GPT-NeoX Feb 2022 EleutherAI 20B[44] 825 GiB[24] 740[30] Apache 2.0
Chinchilla Mar 2022 Google DeepMind 70B[45] 1.4T tokens[45][40] 6805[41] Proprietary
PaLM (Pathways Language Model) Apr 2022 Google 540B[46] 768B tokens[45] 29,250[41]
Trained for ~60 days on ~6000 TPU v4 chips.[41]
OPT (Open Pretrained Transformer) May 2022 Meta 175B[47] 180B tokens[48] 310[30] Non-commercial research[d]
GPT-3 architecture with some adaptations from Megatron. The training logbook written by the team was published.[49]
YaLM 100B Jun 2022 Yandex 100B[50] 1.7TB[50] Unknown Apache 2.0
Minerva Jun 2022 Google 540B[51] 38.5B tokens from webpages filtered for math content and from arXiv[51] Proprietary
For solving "mathematical and scientific questions using step-by-step reasoning".[52]
BLOOM Jul 2022 Large collaboration led by Hugging Face 175B[53] 350B tokens (1.6TB)[54] Responsible AI
Galactica Nov 2022 Meta 120B 106B tokens[55] CC-BY-NC-4.0
AlexaTM (Teacher Models) Nov 2022 Amazon 20B[56] 1.3T[57] Proprietary[58]
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2023

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size Training cost License[c] Notes
Llama Feb 2023 Meta AI 65B[59] 1.4T[59] 6300[60] Non-commercial research[e]
GPT-4 Mar 2023 OpenAI Unknown[f] Unknown Unknown Proprietary
Cerebras-GPT Mar 2023 Cerebras 13B[62] 270[30] Apache 2.0
Falcon Mar 2023 Technology Innovation Institute 40B[63] 1T tokens, from RefinedWeb (filtered web text corpus)[64] plus some "curated corpora".[65] 2800[60] Apache 2.0[66]
BloombergGPT Mar 2023 Bloomberg L.P. 50B 363B tokens from Bloomberg's proprietary data sources, plus 345B tokens from general purpose datasets[67] Unknown Unreleased
Designed for financial tasks.[67]
PanGu-Σ Mar 2023 Huawei 1085B 329B tokens[68] Proprietary
OpenAssistant[69] Mar 2023 LAION 17B 1.5T tokens Apache 2.0
Jurassic-2[70][71] Mar 2023 AI21 Labs Unknown Unknown Proprietary
PaLM 2 (Pathways Language Model 2) May 2023 Google 340B[72] 3.6T tokens[72] 85,000[60]
Used in the Bard chatbot.[73]
YandexGPT May 17, 2023 Yandex Unknown Unknown Unknown
Phi-1 Jun 21, 2023 Microsoft 1.3B[74] 7B tokens[74] Unknown MIT
Trained for 4 days on 8 A100s.[74]
Llama 2 Jul 2023 Meta AI 70B[75] 2T tokens[75] 21,000 Llama 2
Trained over 3.3 million GPU (A100) hours.[76]
Claude 2 Jul 2023 Anthropic Unknown Unknown Unknown Proprietary
Used in the Claude chatbot.[77]
Granite 13b Jul 2023 IBM Unknown
Used in IBM Watsonx.[78]
Mistral 7B Sep 2023 Mistral AI 7.3B[79] Apache 2.0
YandexGPT 2 Sep 7, 2023 Yandex Unknown Proprietary
Claude 2.1 Nov 2023 Anthropic Unknown
Used in the Claude chatbot. Has a context window of 200,000 tokens, or ~500 pages.[80]
Grok-1[81] Nov 2023 xAI 314B Apache 2.0
Used in the Grok chatbot. Grok 1 has a context length of 8,192 tokens and has access to X (Twitter).[82]
Gemini 1.0 Dec 2023 Google DeepMind Unknown Proprietary
Multimodal model, comes in three sizes. Used in the chatbot of the same name.[83]
Mixtral 8x7B Dec 2023 Mistral AI 46.7B Apache 2.0
Outperforms GPT-3.5 and Llama 2 70B on many benchmarks.[84] Mixture of experts model, with 12.9 billion parameters activated per token.[85]
DeepSeek-LLM Nov 29, 2023 DeepSeek 67B 2T tokens[86]: table 2  12,000 DeepSeek
Trained on English and Chinese text. Used 1024 training FLOPs for 67B model, 10b FLOPs for 7B.[86]: figure 5 
Phi-2 Dec 2023 Microsoft 2.7B 1.4T tokens 419[87] MIT
Trained on real and synthetic "textbook-quality" data over 14 days on 96 A100 GPUs.[87]
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2024

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size Training cost License[c] Notes
Gemini 1.5 Feb 2024 Google DeepMind Unknown Unknown Unknown Proprietary
Multimodal model based on a MoE architecture. Context window above 1 million tokens.[88]
Gemini Ultra Feb 2024
Gemma Feb 2024 7B 6T tokens Gemma Terms of Use[89]
OLMo Feb 2024 Allen Institute for AI 7B[90] 2T tokens[91] Apache 2.0
Claude 3 Mar 2024 Anthropic Unknown Unknown Proprietary
Includes three models: Haiku, Sonnet, and Opus.[92]
DBRX Mar 2024 Databricks and Mosaic ML 136B 12T tokens Databricks Open Model[93][94]
YandexGPT 3 Pro Mar 28, 2024 Yandex Unknown Unknown Proprietary
Fugaku-LLM[95] May 2024 Fujitsu, Tokyo Institute of Technology, Tohoku University, RIKEN, etc. 13B 380B tokens Fugaku-LLM Terms of Use[96]
The largest model ever trained on CPU-only, on the Fugaku supercomputer; the model was trained from scratch on 380 billion tokens using 13,824 Fugaku nodes.[95][97]
Chameleon May 2024 Meta AI 34B[98] 4.4T Non-commercial research[99]
Mixtral 8x22B[100] Apr 17, 2024 Mistral AI 141B Unknown Apache 2.0
Phi-3 Apr 23, 2024 Microsoft 14B[101] 4.8T tokens[102] MIT
Marketed by Microsoft as a "small language model".[101]
Granite Code Models May 2024 IBM Unknown Unknown Apache 2.0
YandexGPT 3 Lite May 28, 2024 Yandex Unknown Unknown Proprietary
Qwen2 Jun 2024 Alibaba Cloud 72B[103] 3T tokens Various
DeepSeek-V2 Jun 2024 DeepSeek 236B 8.1T tokens 28,000 DeepSeek
1.4M hours on H800.[104]
Nemotron-4 Jun 2024 Nvidia 340B 9T tokens 200,000 NVIDIA Open Model[105][106]
Trained for 1 epoch. Trained on 6144 H100 GPUs between December 2023 and May 2024.[107][108]
Claude 3.5 Jun 2024 Anthropic Unknown Unknown Unknown Proprietary
Initially, only one model, Sonnet, was released.[109] In October 2024, Sonnet 3.5 was upgraded, and Haiku 3.5 became available.[110]
Llama 3.1 Jul 2024 Meta AI 405B 15.6T tokens 440,000 Llama 3
405B version took 31 million hours on H100-80GB, at 3.8E25 FLOPs.[111][112]
Grok-2 Aug 14, 2024 xAI Unknown Unknown Unknown xAI Community License Agreement[113][114]
Originally closed-source, then re-released as "Grok 2.5" under a source-available license in August 2025.[115][116]
OpenAI o1 Sep 12, 2024 OpenAI Unknown Unknown Proprietary
First LLM described as a "reasoning model".[117][118][better source needed]
Sarvam-1 Oct 24, 2024 Sarvam AI 2B ~2T tokens Sarvam AI Research
Supports 10 Indic languages and English[119][120]
YandexGPT 4 Lite and Pro Oct 24, 2024 Yandex Unknown Unknown Proprietary
Mistral Large Nov 2024 Mistral AI 123B Unknown Mistral Research
Upgraded over time. The latest version is 24.11.[121]
Pixtral
Multimodal. There is also a 12B version which is under Apache 2 license.[121]
OLMo 2 Nov 2024 Allen Institute for AI 32B[122][123] 6.6T tokens[123] 15,000[123] Apache 2.0
Phi-4 Dec 12, 2024 Microsoft 14B[124] 9.8T tokens Unknown MIT
Marketed by Microsoft as a "small language model".[125]
DeepSeek-V3 Dec 2024 DeepSeek 671B 14.8T tokens 56,000
Used 2.788M training hours on H800 GPUs.[126] Originally released under the DeepSeek License, then re-released under the MIT License as "DeepSeek-V3-0324" in March 2025.[127]
Amazon Nova Dec 2024 Amazon Unknown Unknown Unknown Proprietary
Includes three models: Nova Micro, Nova Lite, and Nova Pro.[128]
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2025

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size[g] License[c] Notes
DeepSeek-R1 Jan 20 DeepSeek 671B Unknown MIT
No pretraining; reinforcement-learned upon V3-Base.[129][130]
Qwen2.5 Jan 26 Alibaba 72B 18T Various
7 dense models with parameter counts from 0.5B to 72B. Alibaba also released 2 MoE variants.[131]
MiniMax-Text-01 Jan 14 Minimax 456B 4.7T Minimax Model
[132][133]
Gemini 2.0 Feb 5 Google DeepMind Unknown Unknown Proprietary
Three models released: Flash, Flash-Lite and Pro.[134][135][136]
Grok 3 Feb 19 xAI
Training cost claimed to be "10x the compute of previous state-of-the-art models".[137]
Claude 3.7 Feb 24 Anthropic
One model, Sonnet 3.7.[138]
YandexGPT 5 Lite Pretrain and Pro Feb 25 Yandex
GPT-4.5 Feb 27 OpenAI
OpenAI's largest non-reasoning model at the time.[139]
Gemini 2.5 Mar 25 Google DeepMind
Three models released: Flash, Flash-Lite and Pro.[140]
YandexGPT 5 Lite Instruct Mar 31 Yandex
Llama 4 Apr 5 Meta AI 400B 40T Llama 4
[141][142]
OpenAI o3 and o4-mini Apr 16 OpenAI Unknown Unknown Proprietary
Reasoning models.[143]
Qwen3 Apr 28 Alibaba Cloud 235B 36T Apache 2.0
Multiple sizes, the smallest being 0.6B.[144]
Claude 4 May 22 Anthropic Unknown Unknown Proprietary
Includes two models, Sonnet and Opus.[145]
Sarvam-M May 23 Sarvam AI 24B Unknown Apache 2.0
Hybrid reasoning model fine-tuned on Mistral Small base; optimized for math, programming, and Indian languages.[146][147]
Grok 4 Jul 9 xAI Unknown Unknown Proprietary
[148]
Param-1 Jul 21 BharatGen 2.9B 5T Apache 2.0
Corpus is "focus[ed] on India’s linguistic landscape".[149][150]
GLM-4.5 Jul 29 Z.ai 355B 22T MIT
Released in 355B and 106B sizes.[151] Corpus size was calculated by combining the 15 trillion tokens and the 7 trillion tokens pre-training mix.[152]
GPT-OSS Aug 5 OpenAI 117B Unknown Apache 2.0
Released in 20B and 120B sizes.[153]
Claude 4.1 Aug 5 Anthropic Unknown Unknown Proprietary
Includes one model, Opus.[154]
GPT-5 Aug 7 OpenAI Unknown Unknown
Includes three models: GPT-5, GPT-5 mini, and GPT-5 nano. GPT-5 is available in ChatGPT and API. It includes reasoning abilities.[155][156]
DeepSeek-V3.1 Aug 21 DeepSeek 671B 15.639T MIT
Corpus size based on DeepSeek V3 (trained on 14.8T tokens); further trained on 839B tokens from the extension phases (630B + 209B).[157] A hybrid model that can switch between thinking and non-thinking modes.[158]
YandexGPT 5.1 Pro Aug 28 Yandex Unknown Unknown Proprietary
Apertus Sep 2 ETH Zurich and EPF Lausanne 70B 15T Apache 2.0
The first LLM to be compliant with the Artificial Intelligence Act of the European Union.[159][160]
Claude Sonnet 4.5 Sep 29 Anthropic Unknown Unknown Proprietary
[161]
GLM-4.6 Sep 30 Z.ai 357B Apache 2.0
[162][163][164]
Alice AI LLM 1.0 Oct 28 Yandex Unknown Proprietary
Gemini 3 Nov 18 Google DeepMind Unknown
Models released: Deep Think and Pro.[165]
Olmo 3[166] Nov 20 Allen Institute for AI 32B 5.9T Apache 2.0
Includes 7B and 32B parameter versions, alongside reasoning and instruction-following models.[167]
Claude Opus 4.5 Nov 24 Anthropic Unknown Unknown Proprietary
Largest model in the Claude family.[168]
DeepSeek-V3.2 Dec 1 DeepSeek 685B MIT
Uses a custom DeepSeek Sparse Attention (DSA) mechanism[169][170][171]
GPT 5.2 Dec 11 OpenAI Unknown Proprietary
It was able to solve an open problem in statistical learning theory that had previously remained unresolved by human researchers.[172]
GLM-4.7 Dec 22 Z.ai 355B Apache 2.0
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2026

More information Name, Release date ...
Name Release date[b] Developer # of params Corpus size[g] License[c] Notes
Qwen3-Max-Thinking Jan 26 Alibaba Cloud Unknown Unknown Proprietary
Proprietary reasoning model with adaptive tool-use, test-time scaling, and iterative self-reflection.[173]
Kimi K2.5 Jan 27 Moonshot AI 1T 15T Modified MIT[174]
Multimodal MoE with 32B active parameters, derived from Kimi K2.[175] Can use "Agent Swarm" technology to coordinate up to 100 parallel sub-agents.[176][177]
Step 3.5 Flash Feb 12 StepFun 196B Unknown Apache 2.0
MoE model with 11B active parameters out of 196B total[178][179][180]
Claude Opus 4.6 Feb 5 Anthropic Unknown Proprietary
GPT-5.3-Codex Feb 5 OpenAI Unknown
GLM-5 Feb 12 Z.ai 744B 28.5T MIT
40B active parameters[181][182]
Qwen3.5 Feb 15 Alibaba Cloud 397B Unknown Apache 2.0
17B active parameters[183][184][185]
Qwen3.5-Plus Unknown Proprietary
[183][184]
Claude Sonnet 4.6 Feb 17 Anthropic
Param-2 Feb 17 BharatGen 17B 22T BharatGen Research[186]
Mixture-of-experts model, successor of Param-1; many more Indic languages are supported. Trained on H100 GPUs for 24 days.[187]
Sarvam-105B Feb 18[h] Sarvam AI 105B 12T Apache 2.0
India's first independently-trained foundation model; has 105B and 30B versions. Based on mixture-of-experts model, using only 10.3B active parameters at a time.[189] Interprets Indic languages and Hinglish.[190][191][192]
Sarvam-30B 30B 16T
GPT-5.4 Mar 5 OpenAI Unknown Unknown Proprietary
Mistral Small 4 Mar 17 Mistral AI 119B Apache 2.0
MoE model with 6B active parameters out of 119B total[193][194]
MiMo-V2-Pro Mar 18 Xiaomi 1T Proprietary
Mixture-of-experts (MoE) model with more than 1 trillion parameters (43 billion active). Designed for agentic scenarios. Initially available on OpenRouter under the codename "Hunter Alpha" before official release.[195][196]
Qwen3.6-Plus Apr 1 Alibaba Cloud Unknown
[197][198]
Gemma 4 Apr 2 Google DeepMind 31B Apache 2.0
Released in 31B, 26B A4B (3.8B active parameters), E4B (4B effective parameters), and E2B variants[199][200]
GLM-5.1 Apr 7 Z.ai 744B 28.5T MIT
40B active parameters[201][202][182]
Muse Spark Apr 8 Meta Unknown Unknown Proprietary
[203]
Qwen3.6 Apr 15 Alibaba Cloud 35B Apache 2.0
MoE model with 3B active parameters out of 35B total[204][205]
Claude Opus 4.7 Apr 16 Anthropic Unknown Proprietary
Kimi K2.6 Apr 20 Moonshot AI 1T Modified MIT[206]
32B active parameters[207][208][209]
GPT-5.5 Apr 23 OpenAI Unknown Proprietary
DeepSeek-V4-Flash Apr 24 DeepSeek 284B 32T MIT
Preview release on 24 April.[210] Beta of V4-Flash released on 31 July.[211]
DeepSeek-V4-Pro 1.6T
MiMo-V2.5-Pro Apr 27 Xiaomi 1.02T 48T
MoE model designed for agentic coding and long-horizon software engineering tasks.[212][213]
MiMo-V2.5 310B 27T
Omni-modal MoE model with agentic capabilities and 1M-token context.[214]
Mistral Medium 3.5 Apr 28 Mistral AI 128B Unknown Modified MIT
Mistral AI's flagship model that merges instruction-following, reasoning, and coding into a single 128B dense model[215]
Composer 2.5 May 18 Cursor 1T Proprietary
Agentic coding model available in the Cursor IDE. Built on Kimi K2.5 with additional reinforcement learning; reported 1T total / 32B active parameters.[216]
Gemini 3.5 Flash May 19 Google DeepMind Unknown
[217]
Qwen3.7-Max May 20 Alibaba Cloud Unknown
[218][219]
Claude Opus 4.8 May 28 Anthropic Unknown
[220]
Step 3.7 Flash May 29 StepFun 198B Apache 2.0
Parameter count is 196B (base) + 1.8B (ViT).[221]
MiniMax-M3 Jun 1 MiniMax 428B MiniMax Community[222]
23B active parameters[223][224]
Nemotron 3 Ultra Jun 4 Nvidia 550B 20T OpenMDW 1.1[225]
55B active parameters[226][227]
Claude Fable 5 Jun 9 Anthropic Unknown Unknown Proprietary
[228]
Kimi K2.7 Code Jun 16 Moonshot AI 1T Modified MIT[229]
32B active parameters[230][231]
GLM-5.2 Jun 16 Z.ai 744B 28.5T MIT
40B active parameters[232][233][234]
LongCat-2.0 Jun 30 Meituan 1.6T 35T
48B active parameters. Preview version previously released as "Owl Alpha" on OpenRouter.[235][236]
Hy3 Jul 6 Tencent 295B Unknown Apache 2.0
21B active parameters[237]
Grok 4.5 Jul 8 SpaceXAI Unknown Proprietary
Frontier model for coding, agentic tasks, and knowledge work; trained alongside Cursor. Public availability followed on 9 July 2026.[238][239]
SWE-1.7 Jul 8 Cognition AI Unknown
Coding model for the Devin agent; trained from a Kimi K2.7 base with Cognition's reinforcement-learning pipeline. Available via Devin (not as open weights).[240]
Muse Spark 1.1 Jul 9 Meta Unknown
[241]
GPT-5.6 Jul 9 OpenAI Unknown
Inkling Jul 15 Thinking Machines Lab 975B 45T Apache 2.0
41B active parameters[242][243]
Kimi K3 Jul 16 Moonshot AI 2.8T Unknown Kimi K3[244]
104B active parameters. Weights released on 27 July.[245][246]
Qwen3.8-Max Jul 19 Alibaba Cloud 2.4T Proprietary

Preview release. Alibaba announced that weights will be released at a later date.[247]

Gemini 3.6 Flash Jul 21 Google Deepmind Unknown
[248]
Laguna S 2.1 Jul 21 Poolside AI 118B OpenMDW 1.1[225]
8B active parameters[249][250]
Fugu-Ultra v1.1 Jul 24 Sakana AI Unknown Proprietary
Upgraded Fugu-Ultra with stronger coding, agentic, and reasoning benchmarks vs v1.0.[251]
Claude Opus 5 Jul 24 Anthropic Unknown
[252]
Inkling Small Jul 31 Thinking Machines Lab 276B Apache 2.0
12B active parameters[253]
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See also

Notes

  1. [a]
    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.
  2. [b]
    This is the date that documentation describing the model's architecture was first released.
  3. [c]
    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. [d]
    The smaller models including 66B are publicly available, while the 175B model is available on request.
  5. [e]
    Facebook's license and distribution scheme restricted access to approved researchers, but the model weights were leaked and became widely available.
  6. [f]
    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 ..."[61]
  7. [g]
    Expressed in number of tokens, unless otherwise specified
  8. [h]
    An early checkpoint of the model was released in January.[188]

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