llmrun Composite Score · Agents
The best open LLMs for agents, ranked on agentic and tool-use boards such as Terminal-Bench and tau-bench. The score shown is the overall llmrun Composite Score; the highlighted column is the agentic tool use skill score used for the order.
45 benchmarks4 sourcesUpdated 17 Oct 2026Reference scale 2026-Q4How the Composite Score is built
Agents 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 / 38 | GLM 5.3 Flash205 GB · 24 benchmarks | 117 | 127 | 121 | 124 | 127 | 24 | 205 GB | |
| 2 / 43 | Qwen3.8 27B17.9 GB · 15 benchmarks | 120 | 123 | 118 | 112 | 124 | 15 | 17.9 GB | |
| 3 / 57 | MiniMax M2.7140 GB · 7 benchmarks | 103 | 112 | – | 112 | – | 7 | 140 GB | |
| 4 / 60 | MiniMax M2.5140 GB · 11 benchmarks | 109 | 110 | – | – | 114 | 11 | 140 GB | |
| 5 / 65 | GLM 4.7218 GB · 13 benchmarks | 101 | 106 | 111 | 111 | 111 | 13 | 218 GB | |
| 6 / 66 | GLM 4.5218 GB · 7 benchmarks | 99 | 105 | – | – | – | 7 | 218 GB | |
| 7 / 69 | MiniMax M2140 GB · 5 benchmarks | – | 105 | – | – | – | 5 | 140 GB | |
| 8 / 70 | GLM 4.6217 GB · 6 benchmarks | 96 | 104 | – | 100 | – | 6 | 217 GB | |
| 9 / 81 | Qwen2.5 Coder 32B Instruct24.3 GB · 3 benchmarks | – | 84 | – | – | – | 3 | 24.3 GB | |
| 10 / 82 | GPT OSS 120B70.8 GB · 19 benchmarks | 98 | 82 | – | 103 | 96 | 19 | 70.8 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.