Muse Glimmer 30B vs Qwen 3.6 35B vs Qwen 3.8 27B — Local Agent-Task Benchmark

A controlled comparison of Meta's Muse Glimmer 30B, Alibaba's Qwen 3.6 35B, and Qwen 3.8 27B on real agentic workloads — tool calling, multi-step tool chains, failure recovery, code generation, and instruction following — all running locally on the same Apple M1 Max (64 GB) via Ollama's MLX engine.

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TLDR

• On the expanded 30-task suite (12 core agentic + 18 data-science workload tests): Qwen 3.8 27B passed 30/30 (100%); Muse Glimmer 30B passed 24/30 (80%).
• Qwen 3.8 is 2.1× faster overall (889.9s vs 1847.4s total; per-task avg 29.7s vs 61.6s).
• The decisive difference is the DS workload: on 18 data-science tasks (pandas/SQL coding, A/B experimentation, causal inference), Qwen 3.8 passed 18/18 (100%); Glimmer passed 12/18 (67%) — failing 4 tasks by tool-looping past its turn budget (max_turns_exceeded) and 2 by returning empty output.
• On the original 12 core agentic tasks, all three models are close: Glimmer 12/12, Qwen 3.6 11/12 (shortcut a tool step), Qwen 3.8 12/12.
• Why the speed gap — the key architectural factor: Glimmer is a dense model (~32.3B params, all active per token); Qwen 3.6 35B is a Mixture-of-Experts (MoE) model (~35B total, only ~3B active per token); Qwen 3.8 27B is dense (~27.8B params, all active) — yet still ~2× faster than Glimmer.
• Recommendation: Qwen 3.8 27B is the clear best local agent model for data-science workloads — perfect tool-use fidelity, no tool-looping, and 2× the speed.

Metric	Muse Glimmer 30B	Qwen 3.6 35B	Qwen 3.8 27B
Core 12 tasks	12/12 (100%)	11/12 (92%)	12/12 (100%)
Full 30 tasks	24/30 (80%)	—	30/30 (100%)
DS workload (18)	12/18 (67%)	—	18/18 (100%)
Total time (30)	1847.4s	—	889.9s (2.1× faster)
Architecture	Dense	MoE	Dense

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Table of Contents

1. Background & Motivation
2. Models Under Test
3. Benchmark Design
4. Results
5. Analysis: Why the Difference?
6. Caveats & Methodology Notes
7. [Conclusions & Recommendations](#conclusions