List of large language models

This is a dynamic list and may never be able to satisfy particular standards for completeness. You can help by editing the page to add missing items, with references to reliable sources.

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

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]	3.3 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]

2019

Name	Release date [b]	Developer	Number of parameters	Corpus size	Training cost	License [c]	Notes
T5	Oct 2019	Google	11B [10]	34 billion tokens [10]	Unknown	Apache 2.0 [11]	Base model for Google projects like Imagen. [12]
XLNet	Jun 2019	Google	0.340B [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	Feb 2019	OpenAI	1.5B [16]	40GB [17] (~10 billion tokens) [18]	28 [19]	MIT [20]	Trained on 32 TPUv3 chips for 1 week. [19]

2020

Name	Release date [b]	Developer	Number of parameters	Corpus size	Training cost	License [c]	Notes
GPT-3	May 2020	OpenAI	175B [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 ChatGPT in 2022. [23]

2021

Name	Release date [b]	Developer	Number of parameters	Corpus size	Training cost	License [c]	Notes
GPT-Neo	Mar 2021	EleutherAI	2.7B [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	Jun 2021	EleutherAI	6B [27]	825 GiB [25]	200 [28]	Apache 2.0
Megatron-Turing NLG	Oct 2021 [29]	Microsoft and Nvidia	530B [30]	338.6 billion tokens [30]	38000 [31]	Unreleased	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	Dec 2021	Baidu	260B [32]	4TB	Unknown	Proprietary
Claude [33]	Dec 2021	Anthropic	52B [34]	400 billion tokens [34]	Unknown	Proprietary	Fine-tuned for desirable behavior in conversations. [35]
GLaM (Generalist Language Model)	Dec 2021	Google	1200B [36]	1.6 trillion tokens [36]	5600 [36]	Proprietary
Gopher	Dec 2021	Google DeepMind	280B [37]	300 billion tokens [38]	5833 [39]	Proprietary

2022

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 [40]	1.56T words, [40] 168 billion tokens [38]	4110 [41]	Proprietary
GPT-NeoX	Feb 2022	EleutherAI	20B [42]	825 GiB [25]	740 [28]	Apache 2.0
Chinchilla	Mar 2022	Google DeepMind	70B [43]	1.4 trillion tokens [43] [38]	6805 [39]	Proprietary
PaLM (Pathways Language Model)	Apr 2022	Google	540B [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	175B [45]	180 billion tokens [46]	310 [28]	Non-commercial research [d]	GPT-3 architecture with some adaptations from Megatron. The training logbook written by the team was published. [47]
YaLM 100B	Jun 2022	Yandex	100B [48]	1.7TB [48]	Unknown	Apache 2.0
Minerva	Jun 2022	Google	540B [49]	38.5B tokens from webpages filtered for math content and from arXiv [49]	Unknown	Proprietary	For solving "mathematical and scientific questions using step-by-step reasoning". [50]
BLOOM	Jul 2022	Large collaboration led by Hugging Face	175B [51]	350 billion tokens (1.6TB) [52]	Unknown	Responsible AI
Galactica	Nov 2022	Meta	120B	106 billion tokens [53]	Unknown	CC-BY-NC-4.0
AlexaTM (Teacher Models)	Nov 2022	Amazon	20B [54]	1.3 trillion [55]	Unknown	Proprietary [56]

2023

Name	Release date [b]	Developer	Number of parameters	Corpus size	Training cost	License [c]	Notes
Llama	Feb 2023	Meta AI	65B [57]	1.4 trillion [57]	6300 [58]	Non-commercial research [e]
GPT-4	Mar 2023	OpenAI	Unknown [f] (According to rumors: 1760) [60]	Unknown	Unknown, estimated 230,000	Proprietary
Cerebras-GPT	Mar 2023	Cerebras	13B [61]		270 [28]	Apache 2.0
Falcon	Mar 2023	Technology Innovation Institute	40B [62]	1 trillion tokens, from RefinedWeb (filtered web text corpus) [63] plus some "curated corpora". [64]	2800 [58]	Apache 2.0 [65]
BloombergGPT	Mar 2023	Bloomberg L.P.	50B	363 billion tokens from Bloomberg's proprietary data sources, plus 345 billion tokens from general purpose datasets [66]	Unknown	Unreleased	Designed for financial tasks. [66]
PanGu-Σ	Mar 2023	Huawei	1085B	329 billion tokens [67]	Unknown	Proprietary
OpenAssistant [68]	Mar 2023	LAION	17B	1.5 trillion tokens	Unknown	Apache 2.0
Jurassic-2 [69] [70]	Mar 2023	AI21 Labs	Unknown	Unknown	Unknown	Proprietary
PaLM 2 (Pathways Language Model 2)	May 2023	Google	340B [71]	3.6 trillion tokens [71]	85,000 [58]	Proprietary	Used in the Bard chatbot. [72]
YandexGPT	May 17, 2023	Yandex	Unknown	Unknown	Unknown	Proprietary
Llama 2	Jul 2023	Meta AI	70B [73]	2 trillion 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

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.	13	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]	4.4 trillion	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[ citation needed ]	Unknown	MIT	Marketed by Microsoft as a "small language model". [100]
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	32 [120] [121]	6.6T tokens [121]	15,000 [121]	Apache 2.0
Phi-4	Dec 12, 2024	Microsoft	14 [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

Name	Release date [b]	Developer	Number of parameters	Corpus size	Training cost	License [c]	Notes
DeepSeek-R1	Jan 20	DeepSeek	671B	Not applicable	Unknown	MIT	No pretraining; reinforcement-learned upon V3-Base. [127] [128]
Qwen2.5	Jan 26	Alibaba	72B	18T tokens	Unknown	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]	Unknown	Minimax Model	[131] [130]
Gemini 2.0	Feb 5	Google DeepMind	Unknown	Unknown	Unknown	Proprietary	Three models released: Flash, Flash-Lite and Pro. [132] [133] [134]
Grok 3	Feb 19	xAI	Unknown	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	Unknown	Proprietary	One model, Sonnet 3.7. [136]
YandexGPT 5 Lite Pretrain and Pro	Feb 25	Yandex	Unknown	Unknown	Unknown	Proprietary
GPT-4.5	Feb 27	OpenAI	Unknown	Unknown	Unknown	Proprietary	OpenAI's largest non-reasoning model at the time. [137]
Gemini 2.5	Mar 25	Google DeepMind	Unknown	Unknown	Unknown	Proprietary	Three models released: Flash, Flash-Lite and Pro. [138]
YandexGPT 5 Lite Instruct	Mar 31	Yandex	Unknown	Unknown	Unknown	Proprietary
Llama 4	Apr 5	Meta AI	400B	40T tokens	Unknown	Llama 4	[139] [140]
OpenAI o3 and o4-mini	Apr 16	OpenAI	Unknown	Unknown	Unknown	Proprietary	Reasoning models. [141]
Qwen3	Apr 28	Alibaba Cloud	235B	36T tokens	Unknown	Apache 2.0	Multiple sizes, the smallest being 0.6B. [142]
Claude 4	May 22	Anthropic	Unknown	Unknown	Unknown	Proprietary	Includes two models, Sonnet and Opus. [143]
Sarvam-M	May 23	Sarvam AI	24B	Unknown	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	Unknown	Proprietary	[ citation needed ]
Param-1	Jul 21	BharatGen	2.9B [146]	5T tokens "focus[ed] on India’s linguistic landscape" [146]	Unknown	Apache 2.0	[147]
GLM-4.5	Jul 29	Z.ai	355B	22T tokens [148] [g]	Unknown	MIT	Released in 335B and 106B sizes. [149]
GPT-OSS	Aug 5	OpenAI	117B	Unknown	Unknown	Apache 2.0	Released in 20B and 120B sizes. [150]
Claude 4.1	Aug 5	Anthropic	Unknown	Unknown	Unknown	Proprietary	Includes one model, Opus. [151]
GPT-5	Aug 7	OpenAI	Unknown	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. [152] [153]
DeepSeek-V3.1	Aug 21	DeepSeek	671B	15.639T	Unknown	MIT	Based on DeepSeek V3 (trained on 14.8T tokens); further trained on 839B tokens from the extension phases (630B + 209B). [154] A hybrid model that can switch between thinking and non-thinking modes. [155]
YandexGPT 5.1 Pro	Aug 28	Yandex	Unknown	Unknown	Unknown	Proprietary
Apertus	Sep 2	ETH Zurich and EPF Lausanne	70B	15 trillion [156]	Unknown	Apache 2.0	The first LLM to be compliant with the Artificial Intelligence Act of the European Union. [157]
Claude Sonnet 4.5	Sep 29	Anthropic	Unknown	Unknown	Unknown	Proprietary	[158]
GLM-4.6	Sep 30	Z.ai	357B	Unknown	Unknown	Apache 2.0	[159] [160] [161]
Alice AI LLM 1.0	Oct 28	Yandex	Unknown	Unknown	Unknown	Proprietary
Gemini 3	Nov 18	Google DeepMind	Unknown	Unknown	Unknown	Proprietary	Models released: Deep Think and Pro. [162]
Olmo 3 [163]	Nov 20	Allen Institute for AI	32	5.9T tokens [164]	Unknown	Apache 2.0	Includes 7B and 32B parameter versions, alongside reasoning and instruction-following models. [164]
Claude Opus 4.5	Nov 24	Anthropic	Unknown	Unknown	Unknown	Proprietary	Largest model in the Claude family. [165]
DeepSeek-V3.2	Dec 1	DeepSeek	685B	Unknown	Unknown	MIT	Uses a custom DeepSeek Sparse Attention (DSA) mechanism [166] [167] [168]
GPT 5.2	Dec 11	OpenAI	Unknown	Unknown	Unknown	Proprietary	It was able to solve an open problem in statistical learning theory that had previously remained unresolved by human researchers. [169]
GLM-4.7	Dec 22	Z.ai	355B	Unknown	Unknown	Apache 2.0

2026

Name	Release date [b]	Developer	Number of parameters	Corpus size	Training cost	License [c]	Notes
Qwen3-Max-Thinking	Jan 26	Alibaba Cloud	Unknown	Unknown	Unknown	Proprietary	Proprietary reasoning model with adaptive tool-use, test-time scaling, and iterative self-reflection. [170]
Kimi K2.5	Jan 27	Moonshot AI	1040B	15T tokens	Unknown	Modified MIT	Multimodal MoE with 32B active parameters, derived from Kimi K2. [171] Can use "Agent Swarm" technology to coordinate up to 100 parallel sub-agents. [172] [173]
Step-3.5-Flash	Feb 12	StepFun	196B	Unknown	Unknown	Apache 2.0	MoE model with 11B active parameters out of 196B total [174] [175] [176]
Claude Opus 4.6	Feb 5	Anthropic	Unknown	Unknown	Unknown	Proprietary
GPT-5.3-Codex	Feb 5	OpenAI	Unknown	Unknown	Unknown	Proprietary
GLM-5	Feb 12	Z.ai	754B	Unknown	Unknown	MIT
Claude Sonnet 4.6	Feb 17	Anthropic	Unknown	Unknown	Unknown	Proprietary
Param-2	Feb 17	BharatGen	17B	~22T tokens	Unknown	BharatGen Research [177]	Mixture-of-experts model, successor of Param-1; many more Indic languages are supported. Trained on H100 GPUs for 24 days. [178]
Sarvam-105B	Feb 18 [h]	Sarvam AI	105B	Unknown	Unknown	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. [180] Interprets Indic languages and Hinglish. [181] [182]
Sarvam-30B				~16T tokens
GPT-5.4	Mar 5	OpenAI	Unknown	Unknown	Unknown	Proprietary
Mistral Small 4	Mar 17	Mistral AI	119B	Unknown	Unknown	Apache 2.0	MoE model with 6B active parameters out of 119B total [183] [184]
MiMo-V2-Pro	Mar 18	Xiaomi	1000B [185]	Unknown	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. [186]
Gemma 4	Apr 2	Google DeepMind	31B	Unknown	Unknown	Apache 2.0	Released in 31B, 26B A4B (3.8 billion active parameters), E4B (4 billion effective parameters), and E2B variants [187] [188]
GLM-5.1	Apr 7	Z.ai	754B	Unknown	Unknown	MIT	MoE model designed for agentic coding [189] [190]
Qwen3.6 (Qwen3.6-35B-A3B)	Apr 15	Alibaba Cloud	35B	Unknown	Unknown	Apache 2.0	MoE model with 3B active parameters out of 35B total [191] [192]
Claude Opus 4.7	Apr 16	Anthropic	Unknown	Unknown	Unknown	Proprietary
GPT-5.5	Apr 23	OpenAI	Unknown	Unknown	Unknown	Proprietary
DeepSeek-V4-Flash	Apr 24	DeepSeek	284B	32T	Unknown	MIT	Preview release [193]
DeepSeek-V4-Pro			1.6T

See also

• Comparison of deep learning software
• Comparison of machine learning software
• List of chatbots
• List of language model benchmarks

Notes

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. This is the date that documentation describing the model's architecture was first released.
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]
7. Corpus size was calculated by combining the 15 trillion tokens and the 7 trillion tokens pre-training mix.
8. An early checkpoint of the model was released in January. ^[179]

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