llmrun Composite Score · Coding
The best open LLMs for coding, ranked on SWE-bench, LiveCodeBench, Aider and other coding boards. The score shown is the overall llmrun Composite Score; the highlighted column is the coding skill score used for the order.
40 benchmarks4 sourcesUpdated 10 Oct 2026Reference scale 2026-Q4How the Composite Score is built
Coding score vs VRAM needed
This chart doesn't show proprietary models.
| # (open / overall) | Model | llmrun Composite Score | Coding | Agents | Math | Science | Reasoning | Benchmarks | VRAM |
|---|---|---|---|---|---|---|---|---|---|
| 1 / 53 | Qwen3.8 27B17.9 GB · 12 benchmarks | 112 | – | 118 | 112 | 124 | 12 | 17.9 GB | |
| 2 / 64 | Gemma 4 31B IT21.7 GB · 11 benchmarks | 108 | – | – | 105 | 109 | 11 | 21.7 GB | |
| 3 / 70 | Qwen3.6 35B A3B22.0 GB · 12 benchmarks | 105 | – | 109 | 110 | 107 | 12 | 22.0 GB | |
| 4 / 72 | Gemma 4 26B A4B IT16.5 GB · 9 benchmarks | 104 | – | – | 102 | 101 | 9 | 16.5 GB | |
| 5 / 81 | Qwen3.6 27B17.9 GB · 12 benchmarks | 102 | – | 114 | 111 | 106 | 12 | 17.9 GB | |
| 6 / 89 | GPT OSS 20B13.1 GB · 11 benchmarks | 101 | – | – | 92 | 91 | 11 | 13.1 GB | |
| 7 / 101 | Qwen3.5 27B17.9 GB · 6 benchmarks | 98 | – | – | – | 111 | 6 | 17.9 GB | |
| 8 / 109 | Qwen3 32B22.6 GB · 9 benchmarks | 93 | – | – | 96 | 91 | 9 | 22.6 GB | |
| 9 / 119 | Gemma 3 27B IT21.7 GB · 10 benchmarks | 69 | – | – | 80 | 74 | 10 | 21.7 GB | |
| 10 / 121 | Llama 3.1 8B Instruct7.9 GB · 10 benchmarks | 56 | – | – | 56 | 69 | 10 | 7.9 GB |
The range after each score is a 90% interval: given the boards a model has results on, its true score is very likely inside it. Short ranges mean many agreeing results; long ranges mean few or conflicting ones.
Why some models are missing. A model receives a score only if it has results on at least 4 benchmarks across at least 2 categories, with one being reasoning or coding. Skill scores require at least 2 benchmarks for that skill; otherwise they display as –.
VRAM is llmrun's estimate at Q4_K_M (or the smallest quantization we track) for the weights plus room for a 16K-token context. Longer contexts need more memory. Proprietary models can't run locally, so the VRAM filter hides them.
How the llmrun Composite Score is built · llmrun doesn't run these benchmarks; scores come from public sources.