Artificial Intelligence · 12.08.2026, 22:10 UTC
Introducing Muse Glimmer
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | Simon Willison ↗ |
| Veröffentlicht | 12.08.2026 UTC |
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Introducing Muse Glimmer 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). They claim to have optimized it for exactly the kind of things I'm looking for in a local model:
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. 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. [...]
Here's a pelican which I generated using LM Studio's 18.16 GB version of the model:
I also tried it out with my llm-coding-agent plugin, running against a fresh checkout of Datasette with the prompt:
how does auth work?
Here's the response, at the end of a long transcript showing all of the tool calls it made to explore the codebase. I ran this using llm-lmstudio with this patch applied to upgrade it for compatibility with LLM 0.32. I 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. Glimmer is a vision model, so I asked it to describe this image: llm -m lmstudio/meta/muse-glimmer -a …