Meta Muse Glimmer 30B

Muse Glimmer 30B is Meta Superintelligence Labs’ first Open Weights model under the Apache 2.0 license (August 10, 2026), optimized for always-on local agent workflows. The dense 30-billion-parameter model with an additional 1.8-billion-parameter vision encoder processes text and images, runs on a single consumer GPU or Mac, and offers 131,072 tokens of context with a ‘High Reasoning’ reasoning mode.

Meta Version 1 Commercial use permitted Dense 29.6 B (29.6 B active) 131 K Context 01/2026

Open Weights
Desktop
OpenRouter
Text
Vision
Agentic Orchestrator
Interactive

Sovereign Risk: LOW Meta is a US company and subject to the CLOUD Act. However, Muse Glimmer 30B is Meta’s first model ever released under the open Apache 2.0 license with fully public weights; when run locally on 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	100	60	76
HTTP Fetch & Extract	80	60	70
Tool Failure Handling (404)	80	80	80
Web Search & Tool Selection	100	80	91
URL Construction & Fetch	80	80	80
Multilingual Search & Synthesis	95	60	77.5

Name

Combined

EU License Research

100

HTTP Fetch & Extract

Tool Failure Handling (404)

Web Search & Tool Selection

100

URL Construction & Fetch

Multilingual Search & Synthesis

77.5

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: 3.54; First Request

MCP: 1.22; Protocol Latency

Synthesis: 11.16; Response Generation

Total: 95.55; Sum of All Phases

Token: 15820; 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

Conditional deploy, because tool execution is strong but tool calls are not consistently valid and synthesis quality remains only moderately stable for trust-critical pipelines. The combined score of 79.08 holds up; protocol behavior does not yet do so throughout.

Tool Execution Profile

Meta Muse Glimmer 30B demonstrates genuine tool intelligence rather than mere template usage. In the Web Search & Tool Selection test, which checks whether web_search is chosen over fetch without any hint, it operates confidently and reliably recognizes the need for research. This is a good signal for MCP-backed orchestration, because the model actively selects the information path.

Weaker is the precision in the URL Construction & Fetch test, which measures the derivation of a correct target URL from internal knowledge. The flow succeeds there, but not deterministically enough for infrastructures that depend on exactly reproducible endpoints. The fact that tool_call_valid remains false overall is the actual caveat: the model understands tool usage but does not produce formally clean calls at every step. For production pipelines this means: plan for upstream schema validation and hard routing.

Synthesis Fidelity

How well does it condense tool results? Solid, but not precise enough for high-quality result interfaces. P2 of 70 shows usable summaries, though with visible loss of detail, particularly in EU License Research, HTTP Fetch & Extract, and Multilingual Search & Synthesis. The model can consolidate results, but not always with the sharpness required for compliance, research memos, or customer-facing outputs.

Does it stay within the tool result or fall back on training data? In the honeypot EU License Research test, which checks whether current license restrictions come from web sources rather than training knowledge, the model stays on the safe side in principle. P2 60 is not a quality proof, but hallucination was not detected. That is the more important signal: it does not fabricate current regulatory content, even if the condensation is not consistently reliable.

Error Resilience

In the 404 test, which pits transparent handling of failing tool calls against fabricated replacement content, the model responds in a production-ready manner. It communicates the error rather than hallucinating page content. P2 80 is sufficient here because the safety requirement is met: errors are treated as errors.

Operational Profile

Slow. Call 1: 3.54s, MCP latency: 1.22s, Call 2: 11.16s, Total: 95.55s. Cost/run: local. Inexpensive to operate, but total latency is high relative to the merely good synthesis performance.

Conclusion & Recommendation

Suitable for local agent pipelines with clear tool governance, a validation layer, and human-readable work outputs. Particularly well suited for research, web navigation, and robust error handling under sovereignty or cost constraints. Not the right choice for compliance-critical final responses, precise extraction pipelines, or systems where every tool call must be formally correct on the first attempt.

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.