Muse Glimmer 30B

Muse Glimmer 30B is a multimodal reasoning model built for autonomous agentic work. It combines multi-step reasoning, reliable tool use, image and document understanding, and failure recovery in a single dense model, targeting agents that run end-to-end workflows rather than one-shot completions.

The model is published at meta-models/Muse-Glimmer-30B.

Key Features

• Agentic task completion - Executes end-to-end workflows with sequential planning and recovery from failed steps.
• Reliable tool use - Precise, schema-based function calling that holds up across long chains of operations.
• Multimodal reasoning - Processes text and images together through a dedicated perception encoder, aimed at screenshots and documents.
• Long-context understanding - Handles contexts of 131,072 tokens and beyond.
• Speculative decoding - Ships with a DFlash drafter that predicts blocks of 16 tokens per forward pass using block diffusion, accelerating generation.
• Multilingual - Trained across more than 100 languages.

Architecture

Muse Glimmer 30B is a dense causal transformer of roughly 29.6B parameters across 52 layers, using a repeating pattern of local and global attention. It pairs a 6,656 hidden dimension and 32 query heads under grouped-query attention at a 16:1 ratio with sliding-window attention over 2,048 tokens and RoPE position encoding, against a 202,048-token vocabulary. Vision is handled by a ViT-G/14 perception encoder of about 1.8B parameters, integrated directly rather than bolted on as a separate adapter stage.

Benchmarks

On agentic coding the model reports 76.0% on SWE-Bench Verified and 51.2% on the harder SWE-Bench Pro. General agentic evaluations place it at 75.5% on MCP Atlas and 74.6% on DeepSearch QA, with 43.3% on Gaia2.

Reasoning results are led by 94.7% on AIME 2026, alongside 83.5% on GPQA Diamond and 77.0% on IFBench. Multimodal performance covers 78.8% on Charxiv Reasoning, 75.8% on OmniDocBench, and 74% on MMMU Pro.

The publisher reports that the quantized releases stay close to full precision, with degradation of 0.2% and 1.0% respectively when averaged across 15 benchmarks.

Use Cases

• Autonomous agents that plan a sequence of steps and invoke tools along the way
• Software engineering work, including code generation and debugging
• Multi-turn workflows built on schema-based function calling
• Interpreting visual documents, charts, and screenshots inside agentic applications
• Synthetic data generation

Sampling

The publisher recommends a temperature of 1.0 with top-p 0.95 and top-k 64.