Muse Glimmer 30B vs Qwen3.8 27B
Qwen3.8 27B leads Muse Glimmer 30B on the Artificial Analysis Intelligence Index, 33.7 to 17.5, and Muse Glimmer 30B costs 1.9x less per output token ($1.35 vs $2.55 per million). Updated October 23, 2026; 3 benchmarks have a result for both.
Muse Glimmer 30B or Qwen3.8 27B: which to pick
Only Qwen3.8 27B has a coding score, 74.1 on the Vibe Coding Index; Muse Glimmer 30B has none, so the two cannot be ranked on coding.
Pick Muse Glimmer 30B for output tokens 1.9x cheaper ($1.35 vs $2.55 per million).
- Shared results
- 3
- Muse Glimmer 30B only
- 2
- Qwen3.8 27B only
- 82
- Like-for-like categories
- 3 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Qwen3.8 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 17.5.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 27B
Qwen3.8 27B leads, 45.8 against 8.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Qwen3.8 27B has a GPQA Diamond result, 88.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 27B has a MMLU Pro result, 84.3%.
Directional only
Long documents
Listed context window
Qwen3.8 27B
Qwen3.8 27B takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Glimmer 30B
Muse Glimmer 30B $0.0010, Qwen3.8 27B $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- Muse Glimmer 30B not benchmarked, Qwen3.8 27B 74.1 from 4 benchmarks.
- Widest category
- Agentic work, 37.3 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Agentic Index: Muse Glimmer 30B 8.5 against Qwen3.8 27B 45.8, 37.3 points apart on a 0 to 100 scale.
- Output price
- Muse Glimmer 30B $1.35 against Qwen3.8 27B $2.55 per million output tokens, 1.9x apart.
- Context window
- Muse Glimmer 30B takes 131K tokens, Qwen3.8 27B 1M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Muse Glimmer 30B | Bars | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | Not measured | 1591 | Only Qwen3.8 27B measured |
| AA Coding Index | 49.0 | 68.1 | Qwen3.8 27B |
| LiveCodeBench (Vals AI) | Not measured | 84.0% | Only Qwen3.8 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 86.0% | Only Qwen3.8 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.8% | Only Qwen3.8 27B measured |
| Code migration (Vals AI) | Not measured | 14.2% | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| IOI (Vals AI) | Not measured | 39.1% | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 58.4% | Only Qwen3.8 27B measured |
| SkillsBench (Vals AI) | Not measured | 38.1% | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | Not measured | 67.8% | Only Qwen3.8 27B measured |
| SciCode | Not measured | 46.6% | Only Qwen3.8 27B measured |
| ALE-Bench | 962.5 | Not measured | Only Muse Glimmer 30B measured |
| LMArena Text: coding | Not measured | 1483 | Only Qwen3.8 27B measured |
| LMArena Text: software and IT | Not measured | 1477 | Only Qwen3.8 27B measured |
| LMArena WebDev: React | Not measured | 1600 | Only Qwen3.8 27B measured |
| LMArena WebDev: HTML | Not measured | 1569 | Only Qwen3.8 27B measured |
| LMArena WebDev: from an image | Not measured | 1577 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 17.5 | 33.7 | Qwen3.8 27B |
| Epoch Capabilities Index | Not measured | 151.7 | Only Qwen3.8 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 8.5 | 45.8 | Qwen3.8 27B |
| LiveBench research | Not measured | 59.4% | Only Qwen3.8 27B measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 27B measured |
| LiveBench long agentic | Not measured | 27.9% | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | Not measured | 48.5% | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.9% | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | Not measured | 36.1% | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 59.7% | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| APEX-Agents | Not measured | 47.5% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 88.9% | Only Qwen3.8 27B measured |
| LiveBench reasoning | Not measured | 80.0% | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | Not measured | 83.9% | Only Qwen3.8 27B measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 90.5% | Only Qwen3.8 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 33.9% | Only Qwen3.8 27B measured |
| ARC-AGI | Not measured | 87.5% | Only Qwen3.8 27B measured |
| ARC-AGI-2 | Not measured | 42.4% | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts | Not measured | 1459 | Only Qwen3.8 27B measured |
| LMArena Text: expert | Not measured | 1480 | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts in English | Not measured | 1462 | Only Qwen3.8 27B measured |
| LMArena Vision: diagrams | Not measured | 1285 | Only Qwen3.8 27B measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| ProofBench | Not measured | 16.0% | Only Qwen3.8 27B measured |
| LMArena Text: math | Not measured | 1455 | Only Qwen3.8 27B measured |
| LMArena Text: mathematical work | Not measured | 1458 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.3% | Only Qwen3.8 27B measured |
| LegalBench (Vals AI) | Not measured | 82.4% | Only Qwen3.8 27B measured |
| TaxEval (Vals AI) | Not measured | 70.8% | Only Qwen3.8 27B measured |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedCode (Vals AI) | Not measured | 28.7% | Only Qwen3.8 27B measured |
| LMArena Text: science | Not measured | 1468 | Only Qwen3.8 27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1438 | Only Qwen3.8 27B measured |
| LMArena Text: legal and government | Not measured | 1447 | Only Qwen3.8 27B measured |
| LMArena Text: business and finance | Not measured | 1438 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| Design Arena: game dev | 1124 | Not measured | Only Muse Glimmer 30B measured |
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 7 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index30 boards, none shared
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.6% | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | Not measured | 52.4% | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | Not measured | 83.8% | Only Qwen3.8 27B measured |
| Long context reasoning (AA-LCR) | Not measured | 82.0% | Only Qwen3.8 27B measured |
| DTBench | Not measured | 88.0% | Only Qwen3.8 27B measured |
| LMCA | Not measured | 41.4% | Only Qwen3.8 27B measured |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| LMArena Text | Not measured | 1440 | Only Qwen3.8 27B measured |
| LMArena Text: instruction following | Not measured | 1436 | Only Qwen3.8 27B measured |
| LMArena Text: creative writing | Not measured | 1383 | Only Qwen3.8 27B measured |
| LMArena Vision | Not measured | 1269 | Only Qwen3.8 27B measured |
| LMArena Text: English | Not measured | 1448 | Only Qwen3.8 27B measured |
| LMArena Text: non-English | Not measured | 1428 | Only Qwen3.8 27B measured |
| LMArena Text: Chinese | Not measured | 1501 | Only Qwen3.8 27B measured |
| LMArena Text: German | Not measured | 1442 | Only Qwen3.8 27B measured |
| LMArena Text: Japanese | Not measured | 1388 | Only Qwen3.8 27B measured |
| LMArena Text: Korean | Not measured | 1380 | Only Qwen3.8 27B measured |
| LMArena Text: French | Not measured | 1464 | Only Qwen3.8 27B measured |
| LMArena Text: Spanish | Not measured | 1448 | Only Qwen3.8 27B measured |
| LMArena Text: Russian | Not measured | 1414 | Only Qwen3.8 27B measured |
| LMArena Text: Polish | Not measured | 1447 | Only Qwen3.8 27B measured |
| LMArena Text: multi-turn | Not measured | 1440 | Only Qwen3.8 27B measured |
| LMArena Text: longer queries | Not measured | 1449 | Only Qwen3.8 27B measured |
| LMArena Text: writing and language | Not measured | 1405 | Only Qwen3.8 27B measured |
| LMArena Text: entertainment and media | Not measured | 1393 | Only Qwen3.8 27B measured |
| LMArena Vision: English | Not measured | 1267 | Only Qwen3.8 27B measured |
| LMArena Vision: Chinese | Not measured | 1326 | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | Not measured | 1281 | Only Qwen3.8 27B measured |
| LMArena Vision: humor | Not measured | 1235 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Muse Glimmer 30B | Qwen3.8 27B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Muse Glimmer 30B costs less
Repository review
50K input + 3K output tokens
Muse Glimmer 30B costs less
Agent loop
200K input + 10K output tokens
Muse Glimmer 30B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Meta Muse Glimmer 30B | Qwen3.8 27B |
|---|---|---|
| Maker | Meta | Qwen |
| Weights | Open weights | Open weights |
| Released | August 9, 2026 | August 14, 2026 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.325 | $0.425 |
| Output $/Mtok | $1.35 | $2.55 |
| Coding index | Not benchmarked | 74.1 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Muse Glimmer 30B better than Qwen3.8 27B for coding?
Only Qwen3.8 27B has a coding score, 74.1 on the Vibe Coding Index; Muse Glimmer 30B has none, so the two cannot be ranked on coding. Pick Muse Glimmer 30B for output tokens 1.9x cheaper ($1.35 vs $2.55 per million).
Which is cheaper, Muse Glimmer 30B or Qwen3.8 27B?
Muse Glimmer 30B costs $0.325 per million input tokens and $1.35 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, Muse Glimmer 30B or Qwen3.8 27B?
Qwen3.8 27B, with 1M tokens against 131K.
How many benchmarks is the Muse Glimmer 30B vs Qwen3.8 27B comparison based on?
3 benchmarks have a result for both models as of October 23, 2026. Muse Glimmer 30B's coding index rests on 0 independent benchmarks and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.