llmrun Composite Score · Math
The best open LLMs for math, ranked on competition and research math boards such as AIME and FrontierMath. The score shown is the overall llmrun Composite Score; the highlighted column is the mathematics skill score used for the order.
40 benchmarks4 sourcesUpdated 10 Oct 2026Reference scale 2026-Q4How the Composite Score is built
Math 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 / 47 | Qwen3.8 27B17.9 GB · 12 benchmarks | 112 | – | 118 | 112 | 124 | 12 | 17.9 GB | |
| 2 / 53 | Qwen3.6 27B17.9 GB · 12 benchmarks | 102 | – | 114 | 111 | 106 | 12 | 17.9 GB | |
| 3 / 62 | Qwen3.6 35B A3B22.0 GB · 12 benchmarks | 105 | – | 109 | 110 | 107 | 12 | 22.0 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.