Open Source LLM Rankings
Discover which open source large language models deliver the best quality per parameter. Models are ranked by efficiency score — benchmark quality divided by parameter count — using public benchmark and metadata signals.
Explore hardware tier recommendations to find models that run on your setup, from consumer GPUs to datacenter clusters.
Open Source LLM Hub
Track the best open-source large language models.
540
Models Tracked
458.0M
Downloads
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Efficiency Rankings
| # | Model | Params | Quality | Efficiency | Price/1M | License |
|---|---|---|---|---|---|---|
| 1 | Qwen3.5 0.8B Alibaba | 1B | 5.0 | 6.25 | $0.02 | apache-2.0 |
| 2 | Qwen3.5 2B Alibaba | 2B | 10.2 | 5.10 | $0.04 | apache-2.0 |
| 3 | Qwen3.5 4B Alibaba | 4B | 20.1 | 5.03 | $0.06 | apache-2.0 |
| 4 | Nanbeige4.1-3B Nanbeige | 3B | 10.1 | 3.37 | N/A | apache-2.0 |
| 5 | Qwen: Qwen3.5-9B Alibaba | 9B | 25.0 | 2.78 | $0.11 | apache-2.0 |
| 6 | DeepSeek R1 Distill Qwen 1.5B DeepSeek | 2B | 3.7 | 2.47 | N/A | mit |
| 7 | Qwen: Qwen3.6 27B Alibaba | 27B | 37.1 | 1.37 | $0.79 | apache-2.0 |
| 8 | DeepSeek R1 0528 Qwen3 8B DeepSeek | 8B | 10.4 | 1.30 | N/A | mit |
| 9 | Qwen: Qwen3.5-27B Alibaba | 27B | 33.8 | 1.25 | $0.54 | apache-2.0 |
| 10 | Google: Gemma 4 26B A4B | 26B | 30.3 | 1.17 | $0.13 | apache-2.0 |
| 11 | Qwen: Qwen3 VL 8B Instruct Alibaba | 8B | 8.4 | 1.05 | $0.18 | apache-2.0 |
| 12 | Qwen3 VL 4B Instruct Alibaba | 4B | 4.1 | 1.02 | N/A | apache-2.0 |
| 13 | Step3 VL 10B StepFun | 10B | 9.5 | 0.95 | N/A | apache-2.0 |
| 14 | Qwen: Qwen3.6 35B A3B Alibaba | 35B | 31.6 | 0.90 | $0.35 | apache-2.0 |
| 15 | Qwen: Qwen3.5-35B-A3B Alibaba | 35B | 29.3 | 0.84 | $0.35 | apache-2.0 |
| 16 | DeepSeek R1 Distill Llama 8B DeepSeek | 8B | 6.4 | 0.80 | N/A | mit |
| 17 | OpenAI: gpt-oss-20b OpenAI | 20B | 14.9 | 0.74 | $0.06 | apache-2.0 |
| 18 | DeepSeek R1 Distill Qwen 14B DeepSeek | 14B | 9.8 | 0.70 | N/A | mit |
| 19 | EXAONE 4.5 33B LG AI Research | 33B | 23.0 | 0.70 | N/A | other |
| 20 | Qwen2.5 Coder 7B Instruct Alibaba | 7B | 4.5 | 0.64 | $0.04 | apache-2.0 |
| 21 | Qwen3 14B (Non-reasoning) Alibaba | 14B | 7.0 | 0.50 | $0.38 | apache-2.0 |
| 22 | 30B | 13.6 | 0.45 | $0.12 | apache-2.0 | |
| 23 | Olmo 3 7B Instruct Allen AI | 7B | 2.8 | 0.40 | $0.13 | apache-2.0 |
| 24 | Qwen: Qwen3 VL 32B Instruct Alibaba | 32B | 11.1 | 0.35 | $0.18 | apache-2.0 |
| 25 | DeepSeek R1 Distill Qwen 32B DeepSeek | 32B | 11.0 | 0.34 | $0.29 | mit |
| 26 | 30B | 10.0 | 0.33 | $0.23 | apache-2.0 | |
| 27 | Qwen: Qwen3.5-122B-A10B Alibaba | 122B | 32.3 | 0.26 | $0.72 | apache-2.0 |
| 28 | Molmo2 8B Allen AI | 8B | 2.0 | 0.25 | N/A | apache-2.0 |
| 29 | OpenAI: gpt-oss-120b OpenAI | 120B | 23.8 | 0.20 | $0.06 | apache-2.0 |
| 30 | 80B | 13.7 | 0.17 | $0.34 | apache-2.0 | |
| 31 | Qwen: Qwen3.5 397B A17B Alibaba | 397B | 33.7 | 0.08 | $0.90 | apache-2.0 |
| 32 | 235B | 14.3 | 0.06 | $0.37 | apache-2.0 |
Best by Hardware
Consumer GPU (16-24GB VRAM)
Models suitable for RTX 4090, M2 Max, and similar hardware.
Prosumer (48-80GB VRAM)
Models for A6000, A100, or multi-consumer-GPU setups.
Data Center (Multi-GPU)
Large models requiring multiple A100/H100 GPUs.