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. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.

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
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]
DeepSeek LLM	November 29, 2023	DeepSeek	67	2T tokens[85]: table 2	12,000}}	DeepSeek License	Trained on English and Chinese text. 1e24 FLOPs for 67B. 1e23 FLOPs for 7B[85]: figure 5
Phi-2	December 2023	Microsoft	2.7	1.4T tokens	419[86]	MIT	Trained on real and synthetic "textbook-quality" data, for 14 days on 96 A100 GPUs.[86]
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.[87]
Gemini Ultra	February 2024	Google DeepMind	Unknown	Unknown	Unknown
Gemma	February 2024	Google DeepMind	7	6T tokens	Unknown	Gemma Terms of Use[88]
Claude 3	March 2024	Anthropic	Unknown	Unknown	Unknown	Proprietary	Includes three models, Haiku, Sonnet, and Opus.[89]
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.[90]
Phi-3	April 2024	Microsoft	14[91]	4.8T Tokens		MIT	Microsoft markets them as "small language model".[92]
Granite Code Models	May 2024	IBM	Unknown	Unknown	Unknown	Apache 2.0
Qwen2	June 2024	Alibaba Cloud	72[93]	3T Tokens	Unknown	Qwen License	Multiple sizes, the smallest being 0.5B.
DeepSeek V2	June 2024	DeepSeek	236	8.1T tokens	28,000	DeepSeek License	1.4M hours on H800.[94]
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.[95][96]
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.[97][98]
DeepSeek V3	December 2024	DeepSeek	671	14.8T tokens	56,000	DeepSeek License	2.788M hours on H800 GPUs.[99]
Amazon Nova	December 2024	Amazon	Unknown	Unknown	Unknown	Proprietary	Includes three models, Nova Micro, Nova Lite, and Nova Pro[100]
DeepSeek R1	January 2025	DeepSeek	671	Unknown	Unknown	MIT	No pretraining. Reinforcement-learned upon V3-Base.[101][102]
Qwen2.5	January 2025	Alibaba	72	18T tokens	Unknown	Qwen License	[103]
MiniMax-Text-01	January 2025	Minimax	456	4.7T tokens[104]	Unknown	Minimax Model license	[105][104]

See also

• List of chatbots
• List of language model benchmarks

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]

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.
2. "finetune-transformer-lm". GitHub. Archived from the original on 19 May 2023. Retrieved 2 January 2024.
3. 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].
4. 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.
5. "BERT". March 13, 2023. Archived from the original on January 13, 2021. Retrieved March 13, 2023 – via GitHub.
6. Manning, Christopher D. (2022). "Human Language Understanding & Reasoning". Daedalus. 151 (2): 127–138. doi:10.1162/daed_a_01905. S2CID 248377870. Archived from the original on 2023-11-17. Retrieved 2023-03-09.
7. Patel, Ajay; Li, Bryan; Rasooli, Mohammad Sadegh; Constant, Noah; Raffel, Colin; Callison-Burch, Chris (2022). "Bidirectional Language Models Are Also Few-shot Learners". arXiv:2209.14500 [cs.LG].
8. 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].
9. Raffel, Colin; Shazeer, Noam; Roberts, Adam; Lee, Katherine; Narang, Sharan; Matena, Michael; Zhou, Yanqi; Li, Wei; Liu, Peter J. (2020). "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer". Journal of Machine Learning Research. 21 (140): 1–67. arXiv:1910.10683. ISSN 1533-7928.
10. google-research/text-to-text-transfer-transformer, Google Research, 2024-04-02, archived from the original on 2024-03-29, retrieved 2024-04-04
11. "Imagen: Text-to-Image Diffusion Models". imagen.research.google. Archived from the original on 2024-03-27. Retrieved 2024-04-04.
12. "Pretrained models — transformers 2.0.0 documentation". huggingface.co. Archived from the original on 2024-08-05. Retrieved 2024-08-05.
13. "xlnet". GitHub. Archived from the original on 2 January 2024. Retrieved 2 January 2024.
14. Yang, Zhilin; Dai, Zihang; Yang, Yiming; Carbonell, Jaime; Salakhutdinov, Ruslan; Le, Quoc V. (2 January 2020). "XLNet: Generalized Autoregressive Pretraining for Language Understanding". arXiv:1906.08237 [cs.CL].
15. "GPT-2: 1.5B Release". OpenAI. 2019-11-05. Archived from the original on 2019-11-14. Retrieved 2019-11-14.
16. "Better language models and their implications". openai.com. Archived from the original on 2023-03-16. Retrieved 2023-03-13.
17. "OpenAI's GPT-3 Language Model: A Technical Overview". lambdalabs.com. 3 June 2020. Archived from the original on 27 March 2023. Retrieved 13 March 2023.
18. "openai-community/gpt2-xl · Hugging Face". huggingface.co. Archived from the original on 2024-07-24. Retrieved 2024-07-24.
19. "gpt-2". GitHub. Archived from the original on 11 March 2023. Retrieved 13 March 2023.
20. Wiggers, Kyle (28 April 2022). "The emerging types of language models and why they matter". TechCrunch. Archived from the original on 16 March 2023. Retrieved 9 March 2023.
21. Table D.1 in Brown, Tom B.; Mann, Benjamin; Ryder, Nick; Subbiah, Melanie; Kaplan, Jared; Dhariwal, Prafulla; Neelakantan, Arvind; Shyam, Pranav; Sastry, Girish; Askell, Amanda; Agarwal, Sandhini; Herbert-Voss, Ariel; Krueger, Gretchen; Henighan, Tom; Child, Rewon; Ramesh, Aditya; Ziegler, Daniel M.; Wu, Jeffrey; Winter, Clemens; Hesse, Christopher; Chen, Mark; Sigler, Eric; Litwin, Mateusz; Gray, Scott; Chess, Benjamin; Clark, Jack; Berner, Christopher; McCandlish, Sam; Radford, Alec; Sutskever, Ilya; Amodei, Dario (May 28, 2020). "Language Models are Few-Shot Learners". arXiv:2005.14165v4 [cs.CL].
22. "ChatGPT: Optimizing Language Models for Dialogue". OpenAI. 2022-11-30. Archived from the original on 2022-11-30. Retrieved 2023-01-13.
23. "GPT Neo". March 15, 2023. Archived from the original on March 12, 2023. Retrieved March 12, 2023 – via GitHub.
24. Gao, Leo; Biderman, Stella; Black, Sid; Golding, Laurence; Hoppe, Travis; Foster, Charles; Phang, Jason; He, Horace; Thite, Anish; Nabeshima, Noa; Presser, Shawn; Leahy, Connor (31 December 2020). "The Pile: An 800GB Dataset of Diverse Text for Language Modeling". arXiv:2101.00027 [cs.CL].
25. Iyer, Abhishek (15 May 2021). "GPT-3's free alternative GPT-Neo is something to be excited about". VentureBeat. Archived from the original on 9 March 2023. Retrieved 13 March 2023.
26. "GPT-J-6B: An Introduction to the Largest Open Source GPT Model | Forefront". www.forefront.ai. Archived from the original on 2023-03-09. Retrieved 2023-02-28.
27. Dey, Nolan; Gosal, Gurpreet; Zhiming; Chen; Khachane, Hemant; Marshall, William; Pathria, Ribhu; Tom, Marvin; Hestness, Joel (2023-04-01). "Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster". arXiv:2304.03208 [cs.LG].
28. Alvi, Ali; Kharya, Paresh (11 October 2021). "Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, the World's Largest and Most Powerful Generative Language Model". Microsoft Research. Archived from the original on 13 March 2023. Retrieved 13 March 2023.
29. Smith, Shaden; Patwary, Mostofa; Norick, Brandon; LeGresley, Patrick; Rajbhandari, Samyam; Casper, Jared; Liu, Zhun; Prabhumoye, Shrimai; Zerveas, George; Korthikanti, Vijay; Zhang, Elton; Child, Rewon; Aminabadi, Reza Yazdani; Bernauer, Julie; Song, Xia (2022-02-04). "Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model". arXiv:2201.11990 [cs.CL].
30. 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
31. Wang, Shuohuan; Sun, Yu; Xiang, Yang; Wu, Zhihua; Ding, Siyu; Gong, Weibao; Feng, Shikun; Shang, Junyuan; Zhao, Yanbin; Pang, Chao; Liu, Jiaxiang; Chen, Xuyi; Lu, Yuxiang; Liu, Weixin; Wang, Xi; Bai, Yangfan; Chen, Qiuliang; Zhao, Li; Li, Shiyong; Sun, Peng; Yu, Dianhai; Ma, Yanjun; Tian, Hao; Wu, Hua; Wu, Tian; Zeng, Wei; Li, Ge; Gao, Wen; Wang, Haifeng (December 23, 2021). "ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation". arXiv:2112.12731 [cs.CL].
32. "Product". Anthropic. Archived from the original on 16 March 2023. Retrieved 14 March 2023.
33. Askell, Amanda; Bai, Yuntao; Chen, Anna; et al. (9 December 2021). "A General Language Assistant as a Laboratory for Alignment". arXiv:2112.00861 [cs.CL].
34. Bai, Yuntao; Kadavath, Saurav; Kundu, Sandipan; et al. (15 December 2022). "Constitutional AI: Harmlessness from AI Feedback". arXiv:2212.08073 [cs.CL].
35. Dai, Andrew M; Du, Nan (December 9, 2021). "More Efficient In-Context Learning with GLaM". ai.googleblog.com. Archived from the original on 2023-03-12. Retrieved 2023-03-09.
36. "Language modelling at scale: Gopher, ethical considerations, and retrieval". www.deepmind.com. 8 December 2021. Archived from the original on 20 March 2023. Retrieved 20 March 2023.
37. Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; et al. (29 March 2022). "Training Compute-Optimal Large Language Models". arXiv:2203.15556 [cs.CL].
38. Table 20 and page 66 of PaLM: Scaling Language Modeling with Pathways Archived 2023-06-10 at the Wayback Machine
39. 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.
40. Thoppilan, Romal; De Freitas, Daniel; Hall, Jamie; Shazeer, Noam; Kulshreshtha, Apoorv; Cheng, Heng-Tze; Jin, Alicia; Bos, Taylor; Baker, Leslie; Du, Yu; Li, YaGuang; Lee, Hongrae; Zheng, Huaixiu Steven; Ghafouri, Amin; Menegali, Marcelo (2022-01-01). "LaMDA: Language Models for Dialog Applications". arXiv:2201.08239 [cs.CL].
41. Black, Sidney; Biderman, Stella; Hallahan, Eric; et al. (2022-05-01). GPT-NeoX-20B: An Open-Source Autoregressive Language Model. Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models. Vol. Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models. pp. 95–136. Archived from the original on 2022-12-10. Retrieved 2022-12-19.
42. Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; Sifre, Laurent (12 April 2022). "An empirical analysis of compute-optimal large language model training". Deepmind Blog. Archived from the original on 13 April 2022. Retrieved 9 March 2023.
43. Narang, Sharan; Chowdhery, Aakanksha (April 4, 2022). "Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance". ai.googleblog.com. Archived from the original on 2022-04-04. Retrieved 2023-03-09.
44. 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.
45. 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].
46. "metaseq/projects/OPT/chronicles at main · facebookresearch/metaseq". GitHub. Retrieved 2024-10-18.
47. Khrushchev, Mikhail; Vasilev, Ruslan; Petrov, Alexey; Zinov, Nikolay (2022-06-22), YaLM 100B, archived from the original on 2023-06-16, retrieved 2023-03-18
48. Lewkowycz, Aitor; Andreassen, Anders; Dohan, David; Dyer, Ethan; Michalewski, Henryk; Ramasesh, Vinay; Slone, Ambrose; Anil, Cem; Schlag, Imanol; Gutman-Solo, Theo; Wu, Yuhuai; Neyshabur, Behnam; Gur-Ari, Guy; Misra, Vedant (30 June 2022). "Solving Quantitative Reasoning Problems with Language Models". arXiv:2206.14858 [cs.CL].
49. "Minerva: Solving Quantitative Reasoning Problems with Language Models". ai.googleblog.com. 30 June 2022. Retrieved 20 March 2023.
50. Ananthaswamy, Anil (8 March 2023). "In AI, is bigger always better?". Nature. 615 (7951): 202–205. Bibcode:2023Natur.615..202A. doi:10.1038/d41586-023-00641-w. PMID 36890378. S2CID 257380916. Archived from the original on 16 March 2023. Retrieved 9 March 2023.
51. "bigscience/bloom · Hugging Face". huggingface.co. Archived from the original on 2023-04-12. Retrieved 2023-03-13.
52. Taylor, Ross; Kardas, Marcin; Cucurull, Guillem; Scialom, Thomas; Hartshorn, Anthony; Saravia, Elvis; Poulton, Andrew; Kerkez, Viktor; Stojnic, Robert (16 November 2022). "Galactica: A Large Language Model for Science". arXiv:2211.09085 [cs.CL].
53. "20B-parameter Alexa model sets new marks in few-shot learning". Amazon Science. 2 August 2022. Archived from the original on 15 March 2023. Retrieved 12 March 2023.
54. Soltan, Saleh; Ananthakrishnan, Shankar; FitzGerald, Jack; et al. (3 August 2022). "AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model". arXiv:2208.01448 [cs.CL].
55. "AlexaTM 20B is now available in Amazon SageMaker JumpStart | AWS Machine Learning Blog". aws.amazon.com. 17 November 2022. Archived from the original on 13 March 2023. Retrieved 13 March 2023.
56. "Introducing LLaMA: A foundational, 65-billion-parameter large language model". Meta AI. 24 February 2023. Archived from the original on 3 March 2023. Retrieved 9 March 2023.
57. "The Falcon has landed in the Hugging Face ecosystem". huggingface.co. Archived from the original on 2023-06-20. Retrieved 2023-06-20.
58. "GPT-4 Technical Report" (PDF). OpenAI. 2023. Archived (PDF) from the original on March 14, 2023. Retrieved March 14, 2023.
59. Schreiner, Maximilian (2023-07-11). "GPT-4 architecture, datasets, costs and more leaked". THE DECODER. Archived from the original on 2023-07-12. Retrieved 2024-07-26.
60. Dickson, Ben (22 May 2024). "Meta introduces Chameleon, a state-of-the-art multimodal model". VentureBeat.
61. Dey, Nolan (March 28, 2023). "Cerebras-GPT: A Family of Open, Compute-efficient, Large Language Models". Cerebras. Archived from the original on March 28, 2023. Retrieved March 28, 2023.
62. "Abu Dhabi-based TII launches its own version of ChatGPT". tii.ae. Archived from the original on 2023-04-03. Retrieved 2023-04-03.
63. Penedo, Guilherme; Malartic, Quentin; Hesslow, Daniel; Cojocaru, Ruxandra; Cappelli, Alessandro; Alobeidli, Hamza; Pannier, Baptiste; Almazrouei, Ebtesam; Launay, Julien (2023-06-01). "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only". arXiv:2306.01116 [cs.CL].
64. "tiiuae/falcon-40b · Hugging Face". huggingface.co. 2023-06-09. Retrieved 2023-06-20.
65. UAE's Falcon 40B, World's Top-Ranked AI Model from Technology Innovation Institute, is Now Royalty-Free Archived 2024-02-08 at the Wayback Machine, 31 May 2023
66. Wu, Shijie; Irsoy, Ozan; Lu, Steven; Dabravolski, Vadim; Dredze, Mark; Gehrmann, Sebastian; Kambadur, Prabhanjan; Rosenberg, David; Mann, Gideon (March 30, 2023). "BloombergGPT: A Large Language Model for Finance". arXiv:2303.17564 [cs.LG].
67. Ren, Xiaozhe; Zhou, Pingyi; Meng, Xinfan; Huang, Xinjing; Wang, Yadao; Wang, Weichao; Li, Pengfei; Zhang, Xiaoda; Podolskiy, Alexander; Arshinov, Grigory; Bout, Andrey; Piontkovskaya, Irina; Wei, Jiansheng; Jiang, Xin; Su, Teng; Liu, Qun; Yao, Jun (March 19, 2023). "PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing". arXiv:2303.10845 [cs.CL].
68. Köpf, Andreas; Kilcher, Yannic; von Rütte, Dimitri; Anagnostidis, Sotiris; Tam, Zhi-Rui; Stevens, Keith; Barhoum, Abdullah; Duc, Nguyen Minh; Stanley, Oliver; Nagyfi, Richárd; ES, Shahul; Suri, Sameer; Glushkov, David; Dantuluri, Arnav; Maguire, Andrew (2023-04-14). "OpenAssistant Conversations – Democratizing Large Language Model Alignment". arXiv:2304.07327 [cs.CL].
69. Wrobel, Sharon. "Tel Aviv startup rolls out new advanced AI language model to rival OpenAI". www.timesofisrael.com. Archived from the original on 2023-07-24. Retrieved 2023-07-24.
70. Wiggers, Kyle (2023-04-13). "With Bedrock, Amazon enters the generative AI race". TechCrunch. Archived from the original on 2023-07-24. Retrieved 2023-07-24.
71. Elias, Jennifer (16 May 2023). "Google's newest A.I. model uses nearly five times more text data for training than its predecessor". CNBC. Archived from the original on 16 May 2023. Retrieved 18 May 2023.
72. "Introducing PaLM 2". Google. May 10, 2023. Archived from the original on May 18, 2023. Retrieved May 18, 2023.
73. "Introducing Llama 2: The Next Generation of Our Open Source Large Language Model". Meta AI. 2023. Archived from the original on 2024-01-05. Retrieved 2023-07-19.
74. "llama/MODEL_CARD.md at main · meta-llama/llama". GitHub. Archived from the original on 2024-05-28. Retrieved 2024-05-28.
75. "Claude 2". anthropic.com. Archived from the original on 15 December 2023. Retrieved 12 December 2023.
76. Nirmal, Dinesh (2023-09-07). "Building AI for business: IBM's Granite foundation models". IBM Blog. Archived from the original on 2024-07-22. Retrieved 2024-08-11.
77. "Announcing Mistral 7B". Mistral. 2023. Archived from the original on 2024-01-06. Retrieved 2023-10-06.
78. "Introducing Claude 2.1". anthropic.com. Archived from the original on 15 December 2023. Retrieved 12 December 2023.
79. xai-org/grok-1, xai-org, 2024-03-19, archived from the original on 2024-05-28, retrieved 2024-03-19
80. "Grok-1 model card". x.ai. Retrieved 12 December 2023.
81. "Gemini – Google DeepMind". deepmind.google. Archived from the original on 8 December 2023. Retrieved 12 December 2023.
82. Franzen, Carl (11 December 2023). "Mistral shocks AI community as latest open source model eclipses GPT-3.5 performance". VentureBeat. Archived from the original on 11 December 2023. Retrieved 12 December 2023.
83. "Mixtral of experts". mistral.ai. 11 December 2023. Archived from the original on 13 February 2024. Retrieved 12 December 2023.
84. AI, Mistral (2024-04-17). "Cheaper, Better, Faster, Stronger". mistral.ai. Archived from the original on 2024-05-05. Retrieved 2024-05-05.
85. DeepSeek-AI; Bi, Xiao; Chen, Deli; Chen, Guanting; Chen, Shanhuang; Dai, Damai; Deng, Chengqi; Ding, Honghui; Dong, Kai (2024-01-05), DeepSeek LLM: Scaling Open-Source Language Models with Longtermism, arXiv, doi:10.48550/arXiv.2401.02954, arXiv:2401.02954
86. Hughes, Alyssa (12 December 2023). "Phi-2: The surprising power of small language models". Microsoft Research. Archived from the original on 12 December 2023. Retrieved 13 December 2023.
87. "Our next-generation model: Gemini 1.5". Google. 15 February 2024. Archived from the original on 16 February 2024. Retrieved 16 February 2024. This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we've also successfully tested up to 10 million tokens.
88. "Gemma" – via GitHub.
89. "Introducing the next generation of Claude". www.anthropic.com. Archived from the original on 2024-03-04. Retrieved 2024-03-04.
90. "Fugaku-LLM/Fugaku-LLM-13B · Hugging Face". huggingface.co. Archived from the original on 2024-05-17. Retrieved 2024-05-17.
91. "Phi-3". azure.microsoft.com. 23 April 2024. Archived from the original on 2024-04-27. Retrieved 2024-04-28.
92. "Phi-3 Model Documentation". huggingface.co. Archived from the original on 2024-05-13. Retrieved 2024-04-28.
93. "Qwen2". GitHub. Archived from the original on 2024-06-17. Retrieved 2024-06-17.
94. DeepSeek-AI; Liu, Aixin; Feng, Bei; Wang, Bin; Wang, Bingxuan; Liu, Bo; Zhao, Chenggang; Dengr, Chengqi; Ruan, Chong (2024-06-19), DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model, arXiv, doi:10.48550/arXiv.2405.04434, arXiv:2405.04434
95. "nvidia/Nemotron-4-340B-Base · Hugging Face". huggingface.co. 2024-06-14. Archived from the original on 2024-06-15. Retrieved 2024-06-15.
96. "Nemotron-4 340B | Research". research.nvidia.com. Archived from the original on 2024-06-15. Retrieved 2024-06-15.
97. "The Llama 3 Herd of Models" (July 23, 2024) Llama Team, AI @ Meta
98. "llama-models/models/llama3_1/MODEL_CARD.md at main · meta-llama/llama-models". GitHub. Archived from the original on 2024-07-23. Retrieved 2024-07-23.
99. deepseek-ai/DeepSeek-V3, DeepSeek, 2024-12-26, retrieved 2024-12-26
100. Amazon Nova Micro, Lite, and Pro - AWS AI Service Cards3, Amazon, 2024-12-27, retrieved 2024-12-27
101. deepseek-ai/DeepSeek-R1, DeepSeek, 2025-01-21, retrieved 2025-01-21
102. DeepSeek-AI; Guo, Daya; Yang, Dejian; Zhang, Haowei; Song, Junxiao; Zhang, Ruoyu; Xu, Runxin; Zhu, Qihao; Ma, Shirong (2025-01-22), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, arXiv, doi:10.48550/arXiv.2501.12948, arXiv:2501.12948
103. Qwen; Yang, An; Yang, Baosong; Zhang, Beichen; Hui, Binyuan; Zheng, Bo; Yu, Bowen; Li, Chengyuan; Liu, Dayiheng (2025-01-03), Qwen2.5 Technical Report, arXiv, doi:10.48550/arXiv.2412.15115, arXiv:2412.15115
104. MiniMax; Li, Aonian; Gong, Bangwei; Yang, Bo; Shan, Boji; Liu, Chang; Zhu, Cheng; Zhang, Chunhao; Guo, Congchao (2025-01-14), MiniMax-01: Scaling Foundation Models with Lightning Attention, arXiv, doi:10.48550/arXiv.2501.08313, arXiv:2501.08313, retrieved 2025-01-26
105. MiniMax-AI/MiniMax-01, MiniMax, 2025-01-26, retrieved 2025-01-26