Muse Glimmer vs Llama, Qwen and Gemma: how Meta's open model compares

On Meta's own benchmarks, Muse Glimmer 30B beats Gemma4-31B almost everywhere and edges Qwen3.6-27B on most agentic tests, while Qwen still wins SWE-Bench Verified, GPQA Diamond and MMMU Pro. Community tests split the same way. Builders like Glimmer for tool calling, writing style and speed to an answer, but at least one controlled tool-calling test had Qwen 3.8 27B ahead. Against Llama, the bigger differences are license and architecture: Glimmer is dense and Apache 2.0, while Llama 4 is a mixture-of-experts model under Meta's own community license.

Specs side by side

	Muse Glimmer 30B	Llama 4 (Scout / Maverick)
Released	Aug 10, 2026	Apr 5, 2025
Architecture	Dense, ~29.6B params incl. ~1.8B vision encoder	Mixture of experts, 17B active (109B / 400B total)
License	Apache 2.0	Llama 4 Community License
Context	131,072 tokens	See Meta's Llama docs
Input	Text and image	Text and image

Glimmer figures are from the Hugging Face model card. Llama 4 figures are from Wikipedia's Llama article, which lists Llama 4 as the latest Llama generation. We couldn't confirm a newer Llama release from a Meta source, so treat third-party claims of one with care.

Glimmer's other details: 52 layers, a 202,048-token vocabulary, 100+ languages, a knowledge cutoff of January 4, 2026, and reasoning strength settings from low to xhigh. Sebastian Raschka's architecture notes point out the 3:1 sliding-window to global attention and just 2 KV heads, which works out to about 52 KiB of KV cache per token.

Meta's benchmarks: Glimmer vs Gemma4-31B vs Qwen3.6-27B

These are Meta's numbers from the model card.

Benchmark	Glimmer 30B	Gemma4-31B	Qwen3.6-27B
MCP Atlas	75.5	54.2	62.5
DeepSearch QA	74.6	61.7	71.1
τ3-Banking	23.5	15.1	16.7
WildClawBench	47.6	37.6	43.2
Gaia2	43.3	36.4	40.0
SWE-Bench Pro	51.2	36.9	50.2
SWE-Bench Verified	76.0	66.6	77.2
SciCode	43.6	43.4	39.8
CharXiv Reasoning	78.8	77.7	78.4
MMMU Pro	74	73	75
AIME 2026	94.7	89.2	94.1
GPQA Diamond	83.5	85.7	84.2

The pattern is clear. Glimmer's biggest leads are on tool-use benchmarks like MCP Atlas, which is what Meta built it for. On coding and general knowledge the margins over Qwen are small or reversed. There's no Llama model in Meta's comparison.

Against the larger Qwen3.6-35B-A3B mixture-of-experts model, llm-stats counts Glimmer ahead on 7 of 10 shared benchmarks, with Qwen ahead on GPQA, MMMU-Pro and OmniDocBench 1.5.

Independent and community tests

Third-party results are more mixed. As reported by each author:

• LM Studio's BionicBench v0.1: Glimmer completed 83.3% of 18 tasks versus 77.7% for Gemma 4 31B and Qwen 3.6 27B (LM Studio).
• Controlled tool-calling test on an M1 Max: Glimmer passed 24/30, Qwen 3.8 27B passed 30/30 (Ricky Zhou).
• Implicit-knowledge eval: Glimmer finished in 3 to 4 hours versus about 30 for Qwen 3.8 at xhigh, and scored better (Reddit).
• Calorie estimation from photos: Glimmer 32% within 20% error, Qwen 3.8 27B 16%, Muse Spark 1.3 48% (Reddit).
• Style imitation writing: one tester found Glimmer well ahead of Qwen 3.8 27B (Reddit).
• Refactoring: a LocalLLM user prefers Glimmer's style, especially with typo-ridden prompts (Reddit).
• Four-way comparison with Qwen-3.8-27B, Nemotron-3.5-Lightning and Ornith-1.5-35B-A3B, with run details on llm-bench.io (Reddit).

A common setup is to use both. One LocalLLaMA user runs Glimmer for chat and Qwen 3.8 27B for coding, and another pipeline has Qwen write code and Glimmer review it.

Hardware: dense vs mixture of experts

A dense 30B model does the full 30B of compute on every token, where a 35B-A3B MoE activates only a few billion. So a Qwen A3B model will usually generate faster on the same GPU, while Glimmer's memory footprint is more predictable.

Meta says Glimmer fits under 20GB at 4-bit and targets 24–32GB devices. Unsloth lists 17GB for 4-bit and 58GB for BF16. Builders have pushed lower:

• 130K context in 19.3GB of VRAM on one RTX 4090
• 100K context in 12GB with an EXL3 3.00bpw quant, about 30 tok/s (Reddit)
• Q4_K_M on an 8GB RTX 4060 laptop

Speculative decoding with DFlash is Glimmer's speed trick. Meta reports 3.1x on an RTX 5090, but one tester measured under 1.2% speedup on an M5 Pro. Your hardware matters.

License: why Apache 2.0 matters

Glimmer's Apache 2.0 license has no user-count condition. Llama 4's community license includes a 700 million monthly-active-user condition and an acceptable use policy. For most individuals it makes no difference. For a company shipping a product, Apache 2.0 is simpler to clear with legal.

Which should you pick

• Local agents with lots of tool calls: Glimmer, then test against Qwen on your own tools.
• Pure coding on a single GPU: Qwen 3.8 27B is a strong default in community tests; try Glimmer as a reviewer.
• Long-context on modest VRAM: Glimmer's 2 KV heads help.
• Licensing simplicity: Glimmer or Qwen; check Qwen's specific model license.

More local builds are on the research and data shelf, and evaluating agent builds covers how to run your own comparison.

Frequently asked questions

Is Muse Glimmer better than Qwen?

On Meta's agentic benchmarks, Glimmer leads Qwen3.6-27B on most rows, but Qwen wins SWE-Bench Verified, GPQA Diamond and MMMU Pro. Community tests are split, including one controlled tool-calling test where Qwen 3.8 27B scored 30/30 to Glimmer's 24/30.

Is Muse Glimmer the same as Llama?

No. Glimmer is a separate Muse-family model, distilled from Muse Spark, dense rather than mixture-of-experts, and licensed under Apache 2.0 instead of the Llama community license.

Can Muse Glimmer run on a 24GB GPU?

Yes. Meta targets 24–32GB devices, and one builder ran 130K context in 19.3GB of VRAM on an RTX 4090. Smaller quants have run on 12GB and even 8GB GPUs with trade-offs.

Does Glimmer beat Gemma 4?

On Meta's table, Glimmer beats Gemma4-31B on every benchmark except GPQA Diamond. LM Studio's BionicBench also had Glimmer ahead, 83.3% to 77.7%.