List of large language models

A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.

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.

Name	Release date[a]	Developer	Number of parameters (billion) [b]	Corpus size	Training cost (petaFLOP-day)	License[c]	Notes
GPT-1	June 2018	OpenAI	0.117		1[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 2019	Google	11[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 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	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 2020	OpenAI	175[20]	300 billion tokens[17]	3640[21]	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.[22]
GPT-Neo	March 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 2.0	GPT-3-style language model
Megatron-Turing NLG	October 2021[28]	Microsoft and Nvidia	530[29]	338.6 billion tokens[29]	38000[30]	Restricted web access	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	December 2021	Baidu	260[31]	4 Tb		Proprietary	Chinese-language LLM. Ernie Bot is based on this model.
Claude[32]	December 2021	Anthropic	52[33]	400 billion tokens[33]		beta	Fine-tuned for desirable behavior in conversations.[34]
GLaM (Generalist Language Model)	December 2021	Google	1200[35]	1.6 trillion tokens[35]	5600[35]	Proprietary	Sparse mixture of experts model, making it more expensive to train but cheaper to run inference compared to GPT-3.
Gopher	December 2021	DeepMind	280[36]	300 billion tokens[37]	5833[38]	Proprietary	Later developed into the Chinchilla model.
LaMDA (Language Models for Dialog Applications)	January 2022	Google	137[39]	1.56T words,[39] 168 billion tokens[37]	4110[40]	Proprietary	Specialized for response generation in conversations.
GPT-NeoX	February 2022	EleutherAI	20[41]	825 GiB[24]	740[27]	Apache 2.0	based on the Megatron architecture
Chinchilla	March 2022	DeepMind	70[42]	1.4 trillion tokens[42][37]	6805[38]	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[43]	768 billion tokens[42]	29,250[38]	Proprietary	Trained for ~60 days on ~6000 TPU v4 chips.[38] As of October 2024[update], 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 100B	June 2022	Yandex	100[47]	1.7TB[47]		Apache 2.0	English-Russian model based on Microsoft's Megatron-LM.
Minerva	June 2022	Google	540[48]	38.5B tokens from webpages filtered for mathematical content and from papers submitted to the arXiv preprint server[48]		Proprietary	For solving "mathematical and scientific questions using step-by-step reasoning".[49] Initialized from PaLM models, then finetuned on mathematical and scientific data.
BLOOM	July 2022	Large collaboration led by Hugging Face	175[50]	350 billion tokens (1.6TB)[51]		Responsible AI	Essentially GPT-3 but trained on a multi-lingual corpus (30% English excluding programming languages)
Galactica	November 2022	Meta	120	106 billion tokens[52]	unknown	CC-BY-NC-4.0	Trained on scientific text and modalities.
AlexaTM (Teacher Models)	November 2022	Amazon	20[53]	1.3 trillion[54]		proprietary[55]	bidirectional sequence-to-sequence architecture
Neuro-sama	December 2022	Independent	Unknown	Unknown		privately-owned	A language model designed for live-streaming on Twitch.
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 2023	OpenAI	Unknown[f] (According to rumors: 1760)[59]	Unknown	Unknown	proprietary	Available for ChatGPT Plus users and used in several products.
Chameleon	June 2024	Meta AI	34[60]	4.4 trillion
Cerebras-GPT	March 2023	Cerebras	13[61]		270[27]	Apache 2.0	Trained with Chinchilla formula.
Falcon	March 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]
BloombergGPT	March 2023	Bloomberg L.P.	50	363 billion token dataset based on Bloomberg's data sources, plus 345 billion tokens from general purpose datasets[66]		Proprietary	Trained on financial data from proprietary sources, for financial tasks.
PanGu-Σ	March 2023	Huawei	1085	329 billion tokens[67]		Proprietary
OpenAssistant[68]	March 2023	LAION	17	1.5 trillion tokens		Apache 2.0	Trained on crowdsourced open data
Jurassic-2[69]	March 2023	AI21 Labs	Unknown	Unknown		Proprietary	Multilingual[70]
PaLM 2 (Pathways Language Model 2)	May 2023	Google	340[71]	3.6 trillion tokens[71]	85,000[57]	Proprietary	Was used in Bard chatbot.[72]
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	Unknown	Unknown	Unknown	Proprietary	Used in Claude chatbot.[75]
Granite 13b	July 2023	IBM	Unknown	Unknown	Unknown	Proprietary	Used in IBM Watsonx.[76]
Mistral 7B	September 2023	Mistral AI	7.3[77]	Unknown		Apache 2.0
Claude 2.1	November 2023	Anthropic	Unknown	Unknown	Unknown	Proprietary	Used in Claude chatbot. Has a context window of 200,000 tokens, or ~500 pages.[78]
Grok-1[79]	November 2023	xAI	314	Unknown	Unknown	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	Unknown	Unknown	Unknown	Proprietary	Multimodal model, comes in three sizes. Used in the chatbot of the same name.[81]
Mixtral 8x7B	December 2023	Mistral AI	46.7	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]
Mixtral 8x22B	April 2024	Mistral AI	141	Unknown	Unknown	Apache 2.0	[84]
Phi-2	December 2023	Microsoft	2.7	1.4T tokens	419[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	Unknown	Unknown	Unknown	Proprietary	Multimodal model, based on a Mixture-of-Experts (MoE) architecture. Context window above 1 million tokens.[86]
Gemini Ultra	February 2024	Google DeepMind	Unknown	Unknown	Unknown
Gemma	February 2024	Google DeepMind	7	6T tokens	Unknown	Gemma Terms of Use[87]
Claude 3	March 2024	Anthropic	Unknown	Unknown	Unknown	Proprietary	Includes three models, Haiku, Sonnet, and Opus.[88]
Nova	October 2024	Rubik's AI	Unknown	Unknown	Unknown	Proprietary	Includes three models, Nova-Instant, Nova-Air, and Nova-Pro.
DBRX	March 2024	Databricks and Mosaic ML	136	12T Tokens		Databricks Open Model License	Training cost 10 million USD.
Fugaku-LLM	May 2024	Fujitsu, Tokyo Institute of Technology, etc.	13	380B Tokens			The largest model ever trained on CPU-only, on the Fugaku.[89]
Phi-3	April 2024	Microsoft	14[90]	4.8T Tokens		MIT	Microsoft markets them as "small language model".[91]
Granite Code Models	May 2024	IBM	Unknown	Unknown	Unknown	Apache 2.0
Qwen2	June 2024	Alibaba Cloud	72[92]	3T Tokens			Multiple sizes, the smallest being 0.5B.
Nemotron-4	June 2024	Nvidia	340	9T Tokens	200,000	NVIDIA Open Model License	Trained for 1 epoch. Trained on 6144 H100 GPUs between December 2023 and May 2024.[93][94]
Llama 3.1	July 2024	Meta AI	405	15.6T tokens	440,000	Llama 3 license	405B version took 31 million hours on H100-80GB, at 3.8E25 FLOPs.[95][96]
DeepSeek V3	December 2024	DeepSeek	671	14.8T tokens	440,00	DeepSeek License	2.788M hours on H800 GPUs.[97]

See also

• List of chatbots

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]

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