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.
No ranked model fits this much GPU memory yet. Try a larger size or clear the filter.
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.