Muse Glimmer 30B

Muse Glimmer 30B (August 10, 2026) is Meta Superintelligence Labs’ first open model under the Apache 2.0 license, running without an EU exclusion clause for local deployment. The dense 29.6-billion-parameter model with an additional 1.8-billion-parameter vision encoder processes text and images in a 131,072-token context and delivers up to 233 tokens/second on a consumer GPU via DFlash Speculative Decoder — Workstation-class with genuine Desktop capability.

Meta Version Glimmer Commercial use permitted Dense 29.6 B (29.6 B active) 131 K Context 01/2026 locally tested

Open Weights
Desktop
vLLM
Text
Vision
Long Context
Unusable

Sovereign Risk: LOW Meta is a US company and subject to the CLOUD Act. However, Muse Glimmer 30B is released as fully open weights under the Apache 2.0 license — Meta’s first model ever under this license. When running entirely locally on your own hardware, any dependency on US cloud infrastructure is eliminated, which is why the risk is rated as low despite US jurisdiction.

Tool-use details

Asset performance, reliability, and runtime profile

Name	P1	P2	Combined
EU License Research	95	40	62
HTTP Fetch & Extract	80	60	70
Tool Failure Handling (404)	75	60	69.8
Web Search & Tool Selection	95	80	88.3
URL Construction & Fetch	75	80	77.5
Multilingual Search & Synthesis	95	40	67.5

Name

Combined

EU License Research

HTTP Fetch & Extract

Tool Failure Handling (404)

69.8

Web Search & Tool Selection

88.3

URL Construction & Fetch

77.5

Multilingual Search & Synthesis

67.5

CrucibleMark evaluates tool use across 6 independent tests. Click a test name for details.

Reliability

Tool Call Valid: No

Retry: Not required

Hallucination: Not detected

Reliability measures how consistently a model actually executes tool calls: Tool Call Valid schema and format accepted, Retry Required successful only after retry, Hallucination Flag fabricated tools or parameters detected. All three green means production-ready.

Runtime profile

Call 1: 47.13; First Request

MCP: 1.26; Protocol Latency

Synthesis: 129.13; Response Generation

Total: 1065.17; Sum of All Phases

Token: 19717; Input + Output

Cost: $0; Cost per Run

The runtime profile shows the latency and cost metrics for the model run: Call 1 First Request, MCP Protocol Latency, Synthesis Response Generation, Total sum of all phases, supplemented by Token input and output and Cost cost per run.

Deployment Verdict

Deploy with conditions: Tool execution is strong and hallucination-free, but Tool Calls were not consistently valid and synthesis quality remains too inconsistent for trust-critical pipelines.

Tool Execution Profile

Muse Glimmer 30B demonstrates genuine tool intelligence rather than a rigid fetch pattern. On the Web Search & Tool Selection test — which checks whether the model searches first instead of fetching directly, without an explicit hint — it performs with high confidence. This speaks to usable planning capability in agentic workflows. On the URL Construction & Fetch test, which measures correct derivation of a target URL from model knowledge, it falls off noticeably. The pattern is clear: it usually identifies the right tool type, but is weaker at precisely parameterizing the concrete call.

This matters for MCP pipelines. A model that selects the right tool but doesn’t always form the call validly creates orchestration overhead in the wrapper. On the positive side, no retry was necessary. This looks more like a precision problem at execution time than a fundamental protocol or comprehension issue.

Synthesis Fidelity

How well does it condense tool results? Only with limited reliability. The P2 performance shows that Muse Glimmer 30B does not consistently convert raw results into solid, concise answers. This is particularly noticeable on EU License Research, where current license restrictions must be aggregated from web sources, and on Multilingual Search & Synthesis, where cross-language research is to be condensed in German. For production pipelines, this means: retrieval works better than the last mile of content condensation.

Does it stay within the tool result or fall back on training? On the honeypot EU License Research — which tests exactly this trust failure — it does not hallucinate. That is the more important finding. The weak synthesis score indicates imprecision, but provides no evidence that the model outputs fabricated current facts as tool results.

Error Resilience

On the Tool Failure Handling (404) test, which measures transparent behavior when a retrieval fails, Muse Glimmer 30B stays on the safe side. It does not fabricate page content despite a 404. Error communication is not excellently condensed, but is production-ready. For real-world pipelines this is acceptable, because the error state remains visible and is not reinterpreted as apparent evidence.

Operational Profile

Call 1: 47.13s. Call 2: 129.13s. MCP latency: 1.26s. Total: 1065.17s.
Slow for the overall performance achieved.
Cost/run: local. Inexpensive to operate, but costly in time.

Conclusion & Recommendation

Suitable for locally operated MCP pipelines where tool selection, web research, and cautious error handling matter more than high-quality final condensation. Less suitable for compliance, policy, or executive summary flows where tool outputs must immediately yield precise, citable answers. Recommended as a research and orchestration model with a downstream validation or editorial stage — not as the sole final authority for synthesis-critical responses.

This analysis was generated automatically based on benchmark data. Model used: GPT 4.5 by OpenAI. The raw data and full methodology are documented in the GitHub project.