Is Muse Glimmer better than Qwen3.6-27B and Gemma 4 31B?

For agent orchestration and tool use, yes. Meta's Muse Glimmer 30B (released August 10, 2026) scores 75.5 on MCP Atlas — well ahead of Gemma 4 31B (54.2) and Qwen3.6-27B (62.5) — and leads on AIME 2026 math (94.7). But it has a much higher hallucination rate (82% on AA-Omniscience) than Qwen3.6-27B (49%), so for factual knowledge work Qwen is safer.

Can Muse Glimmer run on a single GPU?

Yes. Muse Glimmer 30B is designed for local, always-on agent workflows and runs on a single consumer GPU with 24GB or 32GB VRAM (or Mac-class unified memory). Weights quantize to under 20GB, it has a 120K+ context window, and with DFlash speculative decoding it hits ~233 tokens/sec on an RTX 5090.

Which open-weight agent model is best in August 2026?

For agentic tool-calling and local deployment, Muse Glimmer 30B leads its size class. For lower hallucination and general knowledge work, Qwen3.6-27B is safer. Gemma 4 31B trails both on agent benchmarks. Pick Muse Glimmer for tool-use agents, Qwen3.6-27B for factual tasks.

Quick Answer

Muse Glimmer vs Qwen3.6-27B vs Gemma 4 31B (Aug 2026)

Published: August 13, 2026

The Short Answer

As of August 2026: Muse Glimmer 30B (Meta, released Aug 10) is the agent-orchestration leader in its size class — best at tool-calling and math — but it hallucinates a lot. Qwen3.6-27B is the safer factual pick. Gemma 4 31B trails on agent benchmarks.

Quick Comparison

	Muse Glimmer 30B	Qwen3.6-27B	Gemma 4 31B
Vendor	Meta	Alibaba	Google
MCP Atlas (agents)	75.5	62.5	54.2
AIME 2026 (math)	94.7	94.1	89.2
DeepSearch QA	74.6	71.1	61.7
Hallucination (AA-Omniscience)	82%	49%	—
Local deploy	Single 24-32GB GPU	Single GPU	Single GPU

Muse Glimmer 30B — Agent Leader, Hallucinates

Meta Superintelligence Labs’ Muse Glimmer 30B (Aug 10, 2026) is built for local, always-on agent workflows. It scores 75.5 on MCP Atlas (tool-calling / agent orchestration), far ahead of both rivals, and 94.7 on AIME 2026 math. It matches trillion-parameter Kimi K2.5 on the Intelligence Index (35) with a fraction of the parameters. The catch: an 82% hallucination rate on AA-Omniscience — high, so keep it grounded with tools and retrieval rather than trusting freeform recall.

Qwen3.6-27B — Safer for Facts

Qwen3.6-27B trails on agent orchestration (62.5 MCP Atlas) but has a much lower 49% hallucination rate and competitive math (94.1 AIME). The better pick when factual accuracy matters more than raw tool-calling.

Gemma 4 31B — Trails on Agents

Gemma 4 31B posts 54.2 on MCP Atlas and 89.2 on AIME 2026 — behind both rivals on agentic and math benchmarks. Still a capable general local model, but not the agent pick.

Local Deployment

Muse Glimmer runs on a single 24-32GB consumer GPU (or Mac unified memory), quantizes to under 20GB, and has a 120K+ context window. With DFlash speculative decoding, decode throughput jumps ~3.1× (to ~233 tok/s on an RTX 5090) — genuinely fast for a local agent loop.

Which Should You Pick?

Local tool-use / agent orchestration → Muse Glimmer 30B.
Factual knowledge work, lower hallucination → Qwen3.6-27B.
General local model in Google’s ecosystem → Gemma 4 31B.

The Reality Check

Muse Glimmer’s headline is real — frontier-tier tool-calling on one consumer GPU — but the 82% hallucination rate means it’s an agent model, not a knowledge base. Wire it to real tools and retrieval; don’t ask it to freehand facts.

Sources

Meta AI Research — Muse Glimmer: research.meta.ai
Artificial Analysis — Muse Glimmer analysis: artificialanalysis.ai
Qwen — model cards: qwenlm.github.io