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.
45 benchmarks4 sourcesUpdated 17 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 / 31 | DeepSeek V4 Flash 0731183 GB · 21 benchmarks | 121 | – | 128 | 124 | 129 | 21 | 183 GB | |
| 2 / 43 | GLM 5.3 Flash205 GB · 24 benchmarks | 117 | 127 | 121 | 124 | 127 | 24 | 205 GB | |
| 3 / 50 | Qwen3.8 27B17.9 GB · 15 benchmarks | 120 | 123 | 118 | 112 | 124 | 15 | 17.9 GB | |
| 4 / 53 | Inkling Small163 GB · 11 benchmarks | – | – | 117 | 118 | 123 | 11 | 163 GB | |
| 5 / 56 | Qwen3.6 27B17.9 GB · 11 benchmarks | 102 | – | 114 | 111 | 105 | 11 | 17.9 GB | |
| 6 / 57 | Qwen3.5 397B A17B243 GB · 8 benchmarks | – | – | 112 | – | 117 | 8 | 243 GB | |
| 7 / 62 | GLM 4.7218 GB · 13 benchmarks | 101 | 106 | 111 | 111 | 111 | 13 | 218 GB | |
| 8 / 65 | Qwen3.6 35B A3B22.0 GB · 11 benchmarks | 105 | – | 109 | 110 | 104 | 11 | 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, or on 3 benchmarks that narrow its 90% range to at most 15.6 points. The results must come from at least two different benchmarks: categories of one benchmark, such as LiveBench, count once. 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.