Best Open-Source LLMs in 2026
Top 10 Ranked by Performance & Adoption
The open-source AI landscape has never been more competitive. From Meta's Llama 3.1 to Mistral's MoE models, we rank the top 10 open-source LLMs by benchmarks, community adoption, licensing, and real-world performance to help you find the best model for your project.
How We Ranked These Models
Benchmark Performance
MMLU, HumanEval, GSM8K, and other standardized benchmarks for objective capability measurement.
Community Adoption
HuggingFace downloads, GitHub stars, and community fine-tunes indicate real-world utility.
Licensing & Commercial Use
We favor truly open licenses (Apache 2.0, MIT) but include strong models with community licenses.
Efficiency & Deployability
How well the model performs relative to its size. MoE models score bonus points for efficiency.
Top 3 at a Glance
The most capable open-source LLM ever released. Matches GPT-4 on most benchmarks with full weights available.
88.6%
MMLU
89.0%
HumanEval
96.8%
GSM8K
Sweet spot of capability and efficiency. Runs on 2 GPUs and handles most tasks as well as much larger models.
83.6%
MMLU
81.7%
HumanEval
95.1%
GSM8K
MoE architecture delivers excellent quality with only 39B active parameters. Best open-source MoE model.
77.8%
MMLU
75.0%
HumanEval
91.2%
GSM8K
Complete Top 10 Ranking
| # | Model | Developer | Size | Context | MMLU | HumanEval | License |
|---|---|---|---|---|---|---|---|
| #1 | Llama 3.1 405B The most capable open-source LLM ever released. Matches GPT-4 on most benchmarks with full weights available. | Meta AI | 405B | 128K | 88.6% | 89.0% | Llama 3.1 Community |
| #2 | Llama 3.1 70B Sweet spot of capability and efficiency. Runs on 2 GPUs and handles most tasks as well as much larger models. | Meta AI | 70B | 128K | 83.6% | 81.7% | Llama 3.1 Community |
| #3 | Mixtral 8x22B MoE architecture delivers excellent quality with only 39B active parameters. Best open-source MoE model. | Mistral AI | 141B (MoE) | 64K | 77.8% | 75.0% | Apache 2.0 |
| #4 | Qwen 2.5 72B Exceptional multilingual capabilities, especially for Chinese and Asian languages. Strong coding performance. | Alibaba | 72B | 128K | 86.1% | 86.4% | Apache 2.0 |
| #5 | Gemma 2 27B Google's best open-weight model. Excellent instruction following and safety at a manageable size. | 27B | 8K | 75.2% | 62.8% | Gemma Terms | |
| #6 | Command R+ Purpose-built for RAG and search applications. Excellent tool use and multi-step reasoning. | Cohere | 104B | 128K | 75.7% | 71.0% | CC-BY-NC-4.0 |
| #7 | DeepSeek V2.5 Exceptional coding and math performance. MoE architecture with only 21B active parameters per token. | DeepSeek | 236B (MoE) | 128K | 84.0% | 89.2% | DeepSeek License |
| #8 | Yi-Lightning Strong performance from a lean architecture. Good balance of capability and deployment ease. | 01.AI | Undisclosed | 16K | 74.1% | 66.0% | Yi License |
| #9 | Mistral Large 2 European AI excellence. Strong multilingual support and code generation with research availability. | Mistral AI | 123B | 128K | 84.0% | 84.0% | Research |
| #10 | Falcon 180B Fully Apache 2.0 licensed — no restrictions. Good for commercial projects needing permissive licensing. | TII | 180B | 2K | 70.4% | 50.0% | Apache 2.0 |
Detailed Model Reviews
Meta AI · 405B parameters · 128K context
The most capable open-source LLM ever released. Matches GPT-4 on most benchmarks with full weights available.
88.6%
MMLU
89.0%
HumanEval
96.8%
GSM8K
Llama 3.1 Community
License
Meta AI · 70B parameters · 128K context
Sweet spot of capability and efficiency. Runs on 2 GPUs and handles most tasks as well as much larger models.
83.6%
MMLU
81.7%
HumanEval
95.1%
GSM8K
Llama 3.1 Community
License
Mistral AI · 141B (MoE) parameters · 64K context
MoE architecture delivers excellent quality with only 39B active parameters. Best open-source MoE model.
77.8%
MMLU
75.0%
HumanEval
91.2%
GSM8K
Apache 2.0
License
Alibaba · 72B parameters · 128K context
Exceptional multilingual capabilities, especially for Chinese and Asian languages. Strong coding performance.
86.1%
MMLU
86.4%
HumanEval
95.8%
GSM8K
Apache 2.0
License
Google · 27B parameters · 8K context
Google's best open-weight model. Excellent instruction following and safety at a manageable size.
75.2%
MMLU
62.8%
HumanEval
78.0%
GSM8K
Gemma Terms
License
Cohere · 104B parameters · 128K context
Purpose-built for RAG and search applications. Excellent tool use and multi-step reasoning.
75.7%
MMLU
71.0%
HumanEval
82.4%
GSM8K
CC-BY-NC-4.0
License
DeepSeek · 236B (MoE) parameters · 128K context
Exceptional coding and math performance. MoE architecture with only 21B active parameters per token.
84.0%
MMLU
89.2%
HumanEval
94.0%
GSM8K
DeepSeek License
License
01.AI · Undisclosed parameters · 16K context
Strong performance from a lean architecture. Good balance of capability and deployment ease.
74.1%
MMLU
66.0%
HumanEval
82.0%
GSM8K
Yi License
License
Mistral AI · 123B parameters · 128K context
European AI excellence. Strong multilingual support and code generation with research availability.
84.0%
MMLU
84.0%
HumanEval
91.0%
GSM8K
Research
License
TII · 180B parameters · 2K context
Fully Apache 2.0 licensed — no restrictions. Good for commercial projects needing permissive licensing.
70.4%
MMLU
50.0%
HumanEval
65.0%
GSM8K
Apache 2.0
License
For most developers in 2026, Llama 3.1 70B offers the best balance of capability, efficiency, and accessibility. It matches GPT-3.5 on most tasks and approaches GPT-4 on many benchmarks, while being free to run locally.
If you need maximum capability and have the hardware, Llama 3.1 405B is the most powerful open-source model available. For efficiency-focused deployments, Mixtral 8x22B delivers excellent quality with MoE efficiency.
Check out our other guides: Best Code LLMs · Best Small LLMs · Llama 3 vs GPT-4