Introducing Muse Glimmer

<p><strong><a href="https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model">Introducing Muse Glimmer</a></strong></p> Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama licenses of old).</p> <p>They claim to have optimized it for exactly the kind of things I'm looking for in a local model:</p> <blockquote> <ul> <li><strong>End-to-end Agentic Task Completion.</strong> 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.</li> <li><strong>Reliable Tool Use.</strong> The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows.</li> <li><strong>Multi-Step Reasoning.</strong> Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows. [...]</li> </ul> </blockquote> <p>Here's <a href="https://gist.github.com/simonw/f20d4cd0ea7596990f7910ead616493e">a pelican</a> which I generated using LM Studio's <a href="https://lmstudio.ai/models/muse-glimmer">18.16 GB version of the model</a>:</p> <p><img alt="All the pieces are there but they are pretty jumbled together." src="https://static.simonwillison.net/static/2026/glimmer-pelican.png" /></p> <p>I also tried it out with my <a href="https://github.com/simonw/llm-coding-agent">llm-coding-agent</a> plugin, running against a fresh checkout of Datasette with the prompt:</p> <blockquote> <p><code>how does auth work?</code></p> </blockquote> <p><a href="https://gist.github.com/simonw/3623676275da1a6078b2500dcc78116f#response-24">Here's the response</a>, at the end of a long transcript showing all of the tool calls it made to explore the codebase. I ran this using <code>llm-lmstudio</code> with <a href="https://github.com/ag