{"slug": "introducing-muse-glimmer", "title": "Introducing Muse Glimmer", "summary": "Meta released Muse Glimmer, a new local large language model optimized for end-to-end agentic task completion, reliable tool use, and multi-step reasoning, achieving strong success rates on benchmarks including DeepSearch QA, MCP-Atlas, τ-Bench, and SWE-Bench. Simon Willison tested the 18.16 GB version via LM Studio and his llm-coding-agent plugin, noting the model's suitability for machines with 32 GB or more RAM.", "body_md": "They claim to have optimized it for exactly the kind of things I'm looking for in a local model:\n\nEnd-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.Reliable Tool Use.The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows.Multi-Step Reasoning.Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows. [...]\n\nHere's [a pelican](https://gist.github.com/simonw/f20d4cd0ea7596990f7910ead616493e) which I generated using LM Studio's [18.16 GB version of the model](https://lmstudio.ai/models/muse-glimmer):\n\nI also tried it out with my [llm-coding-agent](https://github.com/simonw/llm-coding-agent) plugin, running against a fresh checkout of Datasette with the prompt:\n\n`how does auth work?`\n\n[Here's the response](https://gist.github.com/simonw/3623676275da1a6078b2500dcc78116f#response-24), at the end of a long transcript showing all of the tool calls it made to explore the codebase.\n\nI really like this size of model, because if a machine has 32 GB of RAM or more (mine has 128GB) it leaves plenty of space for running other applications at the same time.\n\nVia [Hacker News](https://news.ycombinator.com/item?id=49241679)\n\nTags: [ai](https://simonwillison.net/tags/ai), [generative-ai](https://simonwillison.net/tags/generative-ai), [llama](https://simonwillison.net/tags/llama), [local-llms](https://simonwillison.net/tags/local-llms), [llms](https://simonwillison.net/tags/llms), [meta](https://simonwillison.net/tags/meta), [llm-release](https://simonwillison.net/tags/llm-release)", "url": "https://wpnews.pro/news/introducing-muse-glimmer", "canonical_source": "https://simonwillison.net/2026/Aug/10/introducing-muse-glimmer/#atom-everything", "published_at": "2026-08-10 23:56:03+00:00", "updated_at": "2026-08-11 00:07:14.555661+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-products", "ai-agents"], "entities": ["Meta", "Muse Glimmer", "LM Studio", "Simon Willison", "llm-coding-agent", "Datasette", "DeepSearch QA", "MCP-Atlas"], "alternates": {"html": "https://wpnews.pro/news/introducing-muse-glimmer", "markdown": "https://wpnews.pro/news/introducing-muse-glimmer.md", "text": "https://wpnews.pro/news/introducing-muse-glimmer.txt", "jsonld": "https://wpnews.pro/news/introducing-muse-glimmer.jsonld"}}