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Large language models (LLMs)
Browse every major large language model in one place. This LLM list tracks frontier and open-source foundation models — GPT, Claude, Gemini, Llama, Mistral, Qwen and 400+ more — with parameter counts, context windows, benchmark scores and provider pricing.
Dou Bao
🇨🇳 ByteDance
KwaiYii 13B
🇨🇳 Kuaishou Technology
VARCO LLM 2.0 base
🇰🇷 NCSOFT
VARCO LLM 2.0 small Finetuning
🇰🇷 NCSOFT
VARCO LLM KO/EN-13B-IST ver.1
🇰🇷 NCSOFT
A.X (Adot) 7B
🇰🇷 SK Telecom
DeciCoder-1B
🇮🇱 Deci AI
Konan LLM 13B
🇰🇷 Konan Technology
Code Llama-13B
🇺🇸 Meta AI
Code Llama-34B
🇺🇸 Meta AI
Code Llama-7B
🇺🇸 Meta AI
Japanese-LM-3.6B
🇯🇵 LINE Corporation
Platypus-70B
🇺🇸 Boston University
Japanese StableLM Base Alpha 7B
🇺🇸 Stability AI
Baichuan2-53B
🇨🇳 Baichuan
Claude Instant
🇺🇸 Anthropic
SS-pLM
🇪🇸 Nostrum Biodiscovery
GGNN
🇨🇳 Westlake University
StableLM-Base-Alpha-7B
🇺🇸 Stability AI
Weblab-10B
🇯🇵 Matsuo Lab
OpenFlamingo
🇺🇸 University of Washington
YuLan-Chat-2 (13B)
🇨🇳 Renmin University of China
JIANG
🇨🇳 K.D. Feddersen (KDF)
Vicuna-13B-v1.5
🇺🇸 Large Model Systems Organization
Vicuna-7B-v1.5
🇺🇸 Large Model Systems Organization
bilingual-gpt-neox-4b
🇯🇵 rinna
Zi Yue
🇨🇳 NetEase
Zi Yue 2.0
🇨🇳 NetEase
BELLE-Llama2-13B-chat-0.4M
🇨🇳 KE Holdings Inc. (“Beike”)
WizardLM 13B v1.2
🇺🇸 Microsoft
LM-Design
🇨🇳 ByteDance
RFdiffusion
🇺🇸 University of Washington
YAYI-13B-Llama2
🇨🇳 Yayi (Wenge)
YAYI-7B-Llama2
🇨🇳 Yayi (Wenge)
Stable Beluga 1
🇺🇸 Stability AI
Stable Beluga 2
🇺🇸 Stability AI
GPT3-2.7B (FlashAttention-2)
🇺🇸 Stanford University
Llama 2-13B
🇺🇸 Meta AI
Llama 2-34B
🇺🇸 Meta AI
Llama 2-70B
🇺🇸 Meta AI
Llama 2-7B
🇺🇸 Meta AI
RetNet
🇺🇸 Microsoft Research
BaiLing
🇨🇳 Ant Group
CryoChains
🇺🇸 University of California Santa Barbara (UCSB)
ChatRhino
🇨🇳 JD.com
Med-PaLM
🇺🇸 Google Research
Uni-RNA-L-24
🇨🇳 DP Technology
Uni-RNA-L12
🇨🇳 DP Technology
Uni-RNA-L16
🇨🇳 DP Technology
Uni-RNA-L8
🇨🇳 DP Technology
Baichuan 1-13B
🇨🇳 Baichuan
Claude 2
🇺🇸 Anthropic
TeleChat
🇨🇳 China Telecom
CodeGen2.5
🇺🇸 Salesforce
InternLM
🇨🇳 Shanghai AI Lab
NEC LLM 13B
🇺🇸 NEC Laboratories
xTrimoPGLM -100B
🇨🇳 Tsinghua University
LongNet
🇺🇸 Microsoft
Pangu-Weather
🇨🇳 Huawei
Stable Diffusion XL (SDXL)
🇺🇸 Stability AI
About Large language models (LLMs)
Large language models (LLMs) are the foundation of modern generative AI — general-purpose text models trained on vast corpora that can write, reason, summarise, translate and code. Choosing the best LLM is rarely about a single winner: the best AI model for one task may lag on another. Reasoning-heavy work rewards models that score well on benchmarks like MMLU, GPQA and AIME, while agentic and tool-use workloads care more about instruction following, function calling and long-context recall. When you compare LLMs, weigh raw capability against the practical constraints that decide cost and feasibility — context window, throughput, latency, licensing and price per million tokens. Open-source LLMs such as Llama, Qwen, Mistral and DeepSeek let you self-host and fine-tune, while proprietary frontier models from OpenAI, Anthropic and Google often lead on raw quality. Use our benchmarks to see where each model ranks, and put two candidates side by side with compare before you commit to a provider.
Frequently asked questions
What is the best LLM right now?
There is no single best LLM — it depends on the task. Frontier proprietary models from OpenAI, Anthropic and Google tend to lead on reasoning benchmarks, while open-source LLMs like Llama, Qwen and DeepSeek are best when you need to self-host or fine-tune. Compare candidates on the benchmarks page for your specific workload.
What is the best open source LLM?
The strongest open-source and open-weights LLMs at any given time typically come from the Llama, Qwen, Mistral and DeepSeek families. They can be downloaded, self-hosted and fine-tuned, and the top ones rival proprietary models on many benchmarks. Filter the list above by availability to see current open-weights options.
How do I compare two LLMs?
Use the compare tool to put two models side by side on parameters, context window, availability and benchmark scores, then check the benchmarks page for task-specific rankings such as MMLU, GPQA and coding scores.