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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.

List

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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

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Name Release date[b] Developer Number of parameters 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]

2019

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Name Release date[b] Developer Number of parameters 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]

2020

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Name Release date[b] Developer Number of parameters 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]

2021

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Name Release date[b] Developer Number of parameters 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]
GPT-J Jun 2021 EleutherAI 6B[26] 825 GiB[24] 200[27] Apache 2.0
Megatron-Turing NLG Oct 2021[28] Microsoft and Nvidia 530B[29] 338.6B tokens[29] 38000[30] Unreleased
Trained for 3 months on over 2000 A100 GPUs on the NVIDIA Selene Supercomputer, for over 3 million GPU-hours.[30]
Ernie 3.0 Titan Dec 2021 Baidu 260B[31] 4TB Unknown Proprietary
Claude[32] Dec 2021 Anthropic 52B[33] 400B tokens[33] Unknown Proprietary
Fine-tuned for desirable behavior in conversations.[34]
GLaM (Generalist Language Model) Dec 2021 Google 1200B[35] 1.6T tokens[35] 5600[35] Proprietary
Gopher Dec 2021 Google DeepMind 280B[36] 300B tokens[37] 5833[38] Proprietary

2022

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Name Release date[b] Developer Number of parameters Corpus size Training cost License[c] Notes
LaMDA (Language Models for Dialog Applications) Jan 2022 Google 137B[39] 1.56T words,[39] 168B tokens[37] 4110[40] Proprietary
GPT-NeoX Feb 2022 EleutherAI 20B[41] 825 GiB[24] 740[27] Apache 2.0
Chinchilla Mar 2022 Google DeepMind 70B[42] 1.4T tokens[42][37] 6805[38] Proprietary
PaLM (Pathways Language Model) Apr 2022 Google 540B[43] 768B tokens[42] 29,250[38] Proprietary
Trained for ~60 days on ~6000 TPU v4 chips.[38]
OPT (Open Pretrained Transformer) May 2022 Meta 175B[44] 180B tokens[45] 310[27] Non-commercial research[d]
GPT-3 architecture with some adaptations from Megatron. The training logbook written by the team was published.[46]
YaLM 100B Jun 2022 Yandex 100B[47] 1.7TB[47] Unknown Apache 2.0
Minerva Jun 2022 Google 540B[48] 38.5B tokens from webpages filtered for math content and from arXiv[48] Unknown Proprietary
For solving "mathematical and scientific questions using step-by-step reasoning".[49]
BLOOM Jul 2022 Large collaboration led by Hugging Face 175B[50] 350B tokens (1.6TB)[51] Unknown Responsible AI
Galactica Nov 2022 Meta 120B 106B tokens[52] Unknown CC-BY-NC-4.0
AlexaTM (Teacher Models) Nov 2022 Amazon 20B[53] 1.3T[54] Unknown Proprietary[55]

2023

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Name Release date[b] Developer Number of parameters Corpus size Training cost License[c] Notes
Llama Feb 2023 Meta AI 65B[56] 1.4T[56] 6300[57] Non-commercial research[e]
GPT-4 Mar 2023 OpenAI Unknown[f]
(According to rumors: 1760)[59] 		Unknown Unknown,
estimated 230,000 		Proprietary
Cerebras-GPT Mar 2023 Cerebras 13B[60] 270[27] Apache 2.0
Falcon Mar 2023 Technology Innovation Institute 40B[61] 1T tokens, from RefinedWeb (filtered web text corpus)[62] plus some "curated corpora".[63] 2800[57] Apache 2.0[64]
BloombergGPT Mar 2023 Bloomberg L.P. 50B 363B tokens from Bloomberg's proprietary data sources, plus 345B tokens from general purpose datasets[65] Unknown Unreleased
Designed for financial tasks.[65]
PanGu-Σ Mar 2023 Huawei 1085B 329B tokens[66] Unknown Proprietary
OpenAssistant[67] Mar 2023 LAION 17B 1.5T tokens Unknown Apache 2.0
Jurassic-2[68][69] Mar 2023 AI21 Labs Unknown Unknown Unknown Proprietary
PaLM 2 (Pathways Language Model 2) May 2023 Google 340B[70] 3.6T tokens[70] 85,000[57] Proprietary
Used in the Bard chatbot.[71]
YandexGPT May 17, 2023 Yandex Unknown Unknown Unknown Proprietary
Phi-1 Jun 21, 2023 Microsoft 1.3B[72] 7B tokens[72] Unknown MIT
Trained for 4 days on 8 A100s.[72]
Llama 2 Jul 2023 Meta AI 70B[73] 2T tokens[73] 21,000 Llama 2
Trained over 3.3 million GPU (A100) hours.[74]
Claude 2 Jul 2023 Anthropic Unknown Unknown Unknown Proprietary
Used in the Claude chatbot.[75]
Granite 13b Jul 2023 IBM Unknown Unknown Unknown Proprietary
Used in IBM Watsonx.[76]
Mistral 7B Sep 2023 Mistral AI 7.3B[77] Unknown Unknown Apache 2.0
YandexGPT 2 Sep 7, 2023 Yandex Unknown Unknown Unknown Proprietary
Claude 2.1 Nov 2023 Anthropic Unknown Unknown Unknown Proprietary
Used in the Claude chatbot. Has a context window of 200,000 tokens, or ~500 pages.[78]
Grok-1[79] Nov 2023 xAI 314B Unknown Unknown Apache 2.0
Used in the Grok chatbot. Grok 1 has a context length of 8,192 tokens and has access to X (Twitter).[80]
Gemini 1.0 Dec 2023 Google DeepMind Unknown Unknown Unknown Proprietary
Multimodal model, comes in three sizes. Used in the chatbot of the same name.[81]
Mixtral 8x7B Dec 2023 Mistral AI 46.7B Unknown Unknown 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 Nov 29, 2023 DeepSeek 67B 2T tokens[84]: table 2  12,000 DeepSeek
Trained on English and Chinese text. Used 1024 training FLOPs for 67B model, 10b FLOPs for 7B.[84]: figure 5 
Phi-2 Dec 2023 Microsoft 2.7B 1.4T tokens 419[85] MIT
Trained on real and synthetic "textbook-quality" data over 14 days on 96 A100 GPUs.[85]

2024

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Name Release date[b] Developer Number of parameters 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.[86]
Gemini Ultra Feb 2024 Google DeepMind Unknown Unknown Unknown Proprietary
Gemma Feb 2024 Google DeepMind 7B 6T tokens Unknown Gemma Terms of Use[87]
OLMo Feb 2024 Allen Institute for AI 7B[88] 2T tokens[89] Unknown Apache 2.0
Claude 3 Mar 2024 Anthropic Unknown Unknown Unknown Proprietary
Includes three models: Haiku, Sonnet, and Opus.[90]
DBRX Mar 2024 Databricks and Mosaic ML 136B 12T tokens Unknown Databricks Open Model[91][92]
YandexGPT 3 Pro Mar 28, 2024 Yandex Unknown Unknown Unknown Proprietary
Fugaku-LLM[93] May 2024 Fujitsu, Tokyo Institute of Technology, Tohoku University, RIKEN, etc. 13B 380B tokens Unknown Fugaku-LLM Terms of Use[94]
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.[93][95]
Chameleon May 2024 Meta AI 34B[96] 4.4T Unknown Non-commercial research[97]
Mixtral 8x22B[98] Apr 17, 2024 Mistral AI 141B Unknown Unknown Apache 2.0
Phi-3 Apr 23, 2024 Microsoft 14B[99] 4.8T tokens[100] Unknown MIT
Marketed by Microsoft as a "small language model".[99]
Granite Code Models May 2024 IBM Unknown Unknown Unknown Apache 2.0
YandexGPT 3 Lite May 28, 2024 Yandex Unknown Unknown Unknown Proprietary
Qwen2 Jun 2024 Alibaba Cloud 72B[101] 3T tokens Unknown Various
DeepSeek-V2 Jun 2024 DeepSeek 236B 8.1T tokens 28,000 DeepSeek
1.4M hours on H800.[102]
Nemotron-4 Jun 2024 Nvidia 340B 9T tokens 200,000 NVIDIA Open Model[103][104]
Trained for 1 epoch. Trained on 6144 H100 GPUs between December 2023 and May 2024.[105][106]
Claude 3.5 Jun 2024 Anthropic Unknown Unknown Unknown Proprietary
Initially, only one model, Sonnet, was released.[107] In October 2024, Sonnet 3.5 was upgraded, and Haiku 3.5 became available.[108]
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.[109][110]
Grok-2 Aug 14, 2024 xAI Unknown Unknown Unknown xAI Community License Agreement[111][112]
Originally closed-source, then re-released as "Grok 2.5" under a source-available license in August 2025.[113][114]
OpenAI o1 Sep 12, 2024 OpenAI Unknown Unknown Unknown Proprietary
First LLM described as a "reasoning model".[115][116][better source needed]
Sarvam-1 Oct 24, 2024 Sarvam AI 2B ~2T tokens Unknown Sarvam AI Research
Supports 10 Indic languages and English[117][118]
YandexGPT 4 Lite and Pro Oct 24, 2024 Yandex Unknown Unknown Unknown Proprietary
Mistral Large Nov 2024 Mistral AI 123B Unknown Unknown Mistral Research
Upgraded over time. The latest version is 24.11.[119]
Pixtral Nov 2024 Mistral AI 123B Unknown Unknown Mistral Research
Multimodal. There is also a 12B version which is under Apache 2 license.[119]
OLMo 2 Nov 2024 Allen Institute for AI 32B[120][121] 6.6T tokens[121] 15,000[121] Apache 2.0
Phi-4 Dec 12, 2024 Microsoft 14B[122] 9.8T tokens Unknown MIT
Marketed by Microsoft as a "small language model".[123]
DeepSeek-V3 Dec 2024 DeepSeek 671B 14.8T tokens 56,000 MIT
Used 2.788M training hours on H800 GPUs.[124] Originally released under the DeepSeek License, then re-released under the MIT License as "DeepSeek-V3-0324" in March 2025.[125]
Amazon Nova Dec 2024 Amazon Unknown Unknown Unknown Proprietary
Includes three models: Nova Micro, Nova Lite, and Nova Pro.[126]

2025

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Name Release date[b] Developer Number of parameters Corpus size License[c] Notes
DeepSeek-R1 Jan 20 DeepSeek 671B Not applicable MIT
No pretraining; reinforcement-learned upon V3-Base.[127][128]
Qwen2.5 Jan 26 Alibaba 72B 18T tokens Various
7 dense models with parameter counts from 0.5B to 72B. Alibaba also released 2 MoE variants.[129]
MiniMax-Text-01 Jan 14 Minimax 456B 4.7T tokens[130] Minimax Model
[131][130]
Gemini 2.0 Feb 5 Google DeepMind Unknown Unknown Proprietary
Three models released: Flash, Flash-Lite and Pro.[132][133][134]
Grok 3 Feb 19 xAI Unknown Unknown Proprietary
Training cost claimed to be "10x the compute of previous state-of-the-art models".[135]
Claude 3.7 Feb 24 Anthropic Unknown Unknown Proprietary
One model, Sonnet 3.7.[136]
YandexGPT 5 Lite Pretrain and Pro Feb 25 Yandex Unknown Unknown Proprietary
GPT-4.5 Feb 27 OpenAI Unknown Unknown Proprietary
OpenAI's largest non-reasoning model at the time.[137]
Gemini 2.5 Mar 25 Google DeepMind Unknown Unknown Proprietary
Three models released: Flash, Flash-Lite and Pro.[138]
YandexGPT 5 Lite Instruct Mar 31 Yandex Unknown Unknown Proprietary
Llama 4 Apr 5 Meta AI 400B 40T tokens Llama 4
[139][140]
OpenAI o3 and o4-mini Apr 16 OpenAI Unknown Unknown Proprietary
Reasoning models.[141]
Qwen3 Apr 28 Alibaba Cloud 235B 36T tokens Apache 2.0
Multiple sizes, the smallest being 0.6B.[142]
Claude 4 May 22 Anthropic Unknown Unknown Proprietary
Includes two models, Sonnet and Opus.[143]
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.[144][145]
Grok 4 Jul 9 xAI Unknown Unknown Proprietary
[146]
Param-1 Jul 21 BharatGen 2.9B[147] 5T tokens[g][147] Apache 2.0
[148]
GLM-4.5 Jul 29 Z.ai 355B 22T tokens[149][h] MIT
Released in 355B and 106B sizes.[150]
GPT-OSS Aug 5 OpenAI 117B Unknown Apache 2.0
Released in 20B and 120B sizes.[151]
Claude 4.1 Aug 5 Anthropic Unknown Unknown Proprietary
Includes one model, Opus.[152]
GPT-5 Aug 7 OpenAI Unknown Unknown Proprietary
Includes three models: GPT-5, GPT-5 mini, and GPT-5 nano. GPT-5 is available in ChatGPT and API. It includes reasoning abilities. [153][154]
DeepSeek-V3.1 Aug 21 DeepSeek 671B 15.639T MIT
Based on DeepSeek V3 (trained on 14.8T tokens); further trained on 839B tokens from the extension phases (630B + 209B).[155] A hybrid model that can switch between thinking and non-thinking modes.[156]
YandexGPT 5.1 Pro Aug 28 Yandex Unknown Unknown Proprietary
Apertus Sep 2 ETH Zurich and EPF Lausanne 70B 15T[157] Apache 2.0
The first LLM to be compliant with the Artificial Intelligence Act of the European Union.[158]
Claude Sonnet 4.5 Sep 29 Anthropic Unknown Unknown Proprietary
[159]
GLM-4.6 Sep 30 Z.ai 357B Unknown Apache 2.0
[160][161][162]
Alice AI LLM 1.0 Oct 28 Yandex Unknown Unknown Proprietary
Gemini 3 Nov 18 Google DeepMind Unknown Unknown Proprietary
Models released: Deep Think and Pro.[163]
Olmo 3[164] Nov 20 Allen Institute for AI 32B 5.9T tokens[165] Apache 2.0
Includes 7B and 32B parameter versions, alongside reasoning and instruction-following models.[165]
Claude Opus 4.5 Nov 24 Anthropic Unknown Unknown Proprietary
Largest model in the Claude family.[166]
DeepSeek-V3.2 Dec 1 DeepSeek 685B Unknown MIT
Uses a custom DeepSeek Sparse Attention (DSA) mechanism[167][168][169]
GPT 5.2 Dec 11 OpenAI Unknown Unknown Proprietary
It was able to solve an open problem in statistical learning theory that had previously remained unresolved by human researchers.[170]
GLM-4.7 Dec 22 Z.ai 355B Unknown Apache 2.0

2026

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Name Release date[b] Developer Number of parameters Corpus size 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.[171]
Kimi K2.5 Jan 27 Moonshot AI 1040B 15T tokens Modified MIT
Multimodal MoE with 32B active parameters, derived from Kimi K2.[172] Can use "Agent Swarm" technology to coordinate up to 100 parallel sub-agents.[173][174]
Step-3.5-Flash Feb 12 StepFun 196B Unknown Apache 2.0
MoE model with 11B active parameters out of 196B total[175][176][177]
Claude Opus 4.6 Feb 5 Anthropic Unknown Unknown Proprietary
GPT-5.3-Codex Feb 5 OpenAI Unknown Unknown Proprietary
GLM-5 Feb 12 Z.ai 754B Unknown MIT
Claude Sonnet 4.6 Feb 17 Anthropic Unknown Unknown Proprietary
Param-2 Feb 17 BharatGen 17B ~22T tokens BharatGen Research[178]
Mixture-of-experts model, successor of Param-1; many more Indic languages are supported. Trained on H100 GPUs for 24 days.[179]
Sarvam-105B Feb 18[i] Sarvam AI 105B[181] 12T tokens[181] 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.[182] Interprets Indic languages and Hinglish.[183][184]
Sarvam-30B 30B[181] 16T tokens[181]
GPT-5.4 Mar 5 OpenAI Unknown Unknown Proprietary
Mistral Small 4 Mar 17 Mistral AI 119B Unknown Apache 2.0
MoE model with 6B active parameters out of 119B total[185][186]
MiMo-V2-Pro Mar 18 Xiaomi 1000B[187] Unknown 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.[188]
Gemma 4 Apr 2 Google DeepMind 31B Unknown Apache 2.0
Released in 31B, 26B A4B (3.8 billion active parameters), E4B (4 billion effective parameters), and E2B variants[189][190]
GLM-5.1 Apr 7 Z.ai 754B Unknown MIT
MoE model designed for agentic coding[191][192]
Muse Spark Apr 8 Meta Superintelligence Labs Unknown Unknown Proprietary
[193]
Qwen3.6 (Qwen3.6-35B-A3B) Apr 15 Alibaba Cloud 35B Unknown Apache 2.0
MoE model with 3B active parameters out of 35B total[194][195]
Claude Opus 4.7 Apr 16 Anthropic Unknown Unknown Proprietary
GPT-5.5 Apr 23 OpenAI Unknown Unknown Proprietary
DeepSeek-V4-Flash Apr 24 DeepSeek 284B 32T MIT
Preview release[196]
DeepSeek-V4-Pro 1.6T
MiMo-V2.5-Pro Apr 27 Xiaomi 1.02T 48T MIT
MoE model designed for agentic coding and long-horizon software engineering tasks.[197][198]
MiMo-V2.5 310B 27T
Omni-modal MoE model with agentic capabilities and 1M-token context.[199]
Gemini 3.5 Flash May 19 Google DeepMind Unknown Unknown Proprietary
[200]
Claude Opus 4.8 May 28 Anthropic Unknown Unknown Proprietary
[201]
Step 3.7 Flash May 29 StepFun 198B[j] Unknown Apache 2.0
[202]

See also

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Notes

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  1. ^ 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. ^ a b c d e f g h i This is the date that documentation describing the model's architecture was first released.
  3. ^ a b c d e f g h i 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 ..."[58]
  7. ^ "focus[ed] on India’s linguistic landscape"
  8. ^ Corpus size was calculated by combining the 15 trillion tokens and the 7 trillion tokens pre-training mix.
  9. ^ An early checkpoint of the model was released in January.[180]
  10. ^ 196B + 1.8B (ViT)

References

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