Model comparison
Muse Glimmer 30B vs Qwen3.8-27B
Meta against Qwen, compared on context, price, and verified benchmark results.
Catalog record checked August 14, 2026; individual provider fields may change.
Meta
Muse Glimmer 30B
Balanced · Open weights
Qwen
Qwen3.8-27B
Balanced · Open weights
| Specification | Muse Glimmer 30B | Qwen3.8-27B |
|---|---|---|
| Provider | Meta | Qwen |
| Tier | Balanced | Balanced |
| Context window | 131K | Winner: 262K |
| Max output | Not verified | 131K |
| Input / 1M tokens | $0 | Not verified |
| Output / 1M tokens | $0 | Not verified |
| Weights | Open | Open |
| Parameters | ~29.6B dense (incl. ~1.8B perception encoder) | 27B dense |
| Reasoning levels | Not verified | low, medium, xhigh |
| Modalities | text, image | text, image, video |
| Released | August 10, 2026 | August 14, 2026 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: Muse Glimmer 30B: Apache 2.0 open weights (BF16 + official GGUF); hosted inference billed by provider.
Muse Glimmer 30B
Meta's open-weight multimodal agentic model distilled from Muse Spark for local consumer hardware (~24–32GB class with 4-bit).
Best for
- Local agents
- On-device coding + tool use
- Privacy-sensitive multimodal work
Watch out
Full BF16 needs far more than 24GB — plan on official GGUF / 4-bit packs (and mmproj for vision). Agentic quality ≠ frontier Muse Spark API; validate on your workflows.
Qwen3.8-27B
Apache 2.0 Qwen3.8 dense VLM for local and self-hosted work — native 262K context, image and video input, thinking on by default but can be turned off. Distinct from hosted Qwen3.8-Max.
Best for
- Local multimodal agents
- Self-hosting a dense 27B VLM
- Apache 2.0 deployments
Watch out
The Hugging Face repo was created 2026-08-05; this row uses the 2026-08-14 card revision. Hosted 1M-context API is documented as coming soon. Vendor SWE-bench Pro 61.7 and DeepSWE 42.2 used a Claude Code harness, not swebench.com or the public Datacurve DeepSWE board. YaRN can extend context toward 1M.
Common questions
Muse Glimmer 30B vs Qwen3.8-27B
Answered from the verified figures on this page rather than general guidance.
Which has the larger context window, Muse Glimmer 30B or Qwen3.8-27B?
Qwen3.8-27B accepts 262K tokens against 131K for Muse Glimmer 30B. This only matters if you routinely send very long documents or large codebases.
Should I use Muse Glimmer 30B or Qwen3.8-27B?
Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. Muse Glimmer 30B suits local agents; Qwen3.8-27B suits local multimodal agents.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.