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.
40 benchmarks4 sourcesUpdated 10 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 / 69 | Qwen2.5 Coder 32B Instruct24.3 GB · 3 benchmarks | — | – | 84 | – | – | – | 3 | 24.3 GB |
| 2 / 70 | GPT OSS 120B70.8 GB · 20 benchmarks | 98 | 82 | – | 103 | 97 | 20 | 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. 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.