Muse Glimmer is optimized for end-to-end agentic task completion, achieving strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, τ-Bench and SWE-Bench.

The model is said to be tuned for end-to-end agentic work, scoring well on benchmarks that require working within scaffolds, writing and debugging code, and resolving multi-turn requests. ✦ AI generated

Simon Willison · Simon Willison's Weblog · 2026-08-10 · original ↗

End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, τ-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish.

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