Muse Ember vs Qwen3.8 27B
Qwen3.8 27B leads Muse Ember on coding, 69.7 to 64.9 on the Vibe Coding Index, and Muse Ember costs 5.1x less per output token ($0.50 vs $2.55 per million). Updated November 14, 2026; 12 benchmarks have a result for both.
Muse Ember or Qwen3.8 27B: which to pick
Pick Qwen3.8 27B for coding: it scores 69.7 on the Vibe Coding Index against 64.9 for Muse Ember.
Pick Muse Ember for output tokens 5.1x cheaper ($0.50 vs $2.55 per million) and a larger context window (1.05M against 1.02M tokens).
- Shared results
- 12
- Muse Ember only
- 3
- Qwen3.8 27B only
- 81
- Like-for-like categories
- 4 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
Qwen3.8 27B
Qwen3.8 27B scores 69.7 against 64.9.
3 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only Qwen3.8 27B has a AA Intelligence Index result, 20.1.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
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
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
Muse Ember
Muse Ember takes 1.05M tokens against 1.02M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
Muse Ember $0.0003, 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 Ember 64.9 from 3 benchmarks, Qwen3.8 27B 69.7 from 4 benchmarks.
- Widest category
- Reasoning, 16.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- Terminal-Bench 4.0 (Artificial Analysis run): Muse Ember 40.9% against Qwen3.8 27B 5.6%, 35.4 points apart on a 0 to 100 scale.
- Output price
- Muse Ember $0.50 against Qwen3.8 27B $2.55 per million output tokens, 5.1x apart.
- Context window
- Muse Ember takes 1.05M tokens, Qwen3.8 27B 1.02M.
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 Ember | Bars | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 12 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | Muse Ember | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 69.3% | Only Qwen3.8 27B measured |
| LMArena WebDev | 1609 | 1590 | Muse Ember |
| AA Coding Index | Not measured | 68.1 | Only Qwen3.8 27B measured |
| 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 |
| Terminal-Bench 4 (Vals AI) | 39.4% | Not measured | Only Muse Ember measured |
| Vibe Code Bench (Vals AI) | 83.7% | 64.8% | Muse Ember |
| 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 |
| Terminal-Bench 4.0 (Artificial Analysis run) | 40.9% | 5.6% | Muse Ember |
| FrontierCode 2.0 | 27.2% | 14.9% | Muse Ember |
| LMArena Text: coding | Not measured | 1479 | Only Qwen3.8 27B measured |
| LMArena Text: software and IT | Not measured | 1474 | Only Qwen3.8 27B measured |
| LMArena WebDev: React | 1616 | 1596 | Muse Ember |
| LMArena WebDev: HTML | 1621 | 1570 | Muse Ember |
| LMArena WebDev: from an image | Not measured | 1574 | Only Qwen3.8 27B measured |
| GameBench 2 | Not measured | 43.8 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Muse Ember | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 20.1 | Only Qwen3.8 27B measured |
| Epoch Capabilities Index | Not measured | 152.6 | Only Qwen3.8 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Muse Ember | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 45.8 | Only Qwen3.8 27B measured |
| LiveBench research | Not measured | 61.3% | Only Qwen3.8 27B measured |
| LiveBench browser | Not measured | 24.2% | Only Qwen3.8 27B measured |
| LiveBench long agentic | Not measured | 15.9% | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | Not measured | 48.5% | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | 38.2% | 30.9% | Muse Ember |
| Legal Research Bench (Vals AI) | 49.0% | 36.1% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | 25.0% | Not measured | Only Muse Ember 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 | 64.3% | 47.5% | Muse Ember |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Muse Ember | 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 |
| HLE-Diamond (Artificial Analysis run) | 25.1% | 15.3% | Muse Ember |
| CritPt v2, hard subset (Artificial Analysis run) | 39.0% | 16.5% | Muse Ember |
| ARC-AGI | Not measured | 87.5% | Only Qwen3.8 27B measured |
| ARC-AGI-2 | Not measured | 42.4% | Only Qwen3.8 27B measured |
| SimpleBench | Not measured | 60.2% | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts | Not measured | 1456 | Only Qwen3.8 27B measured |
| LMArena Text: expert | Not measured | 1479 | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts in English | Not measured | 1462 | Only Qwen3.8 27B measured |
| LMArena Vision: diagrams | Not measured | 1281 | Only Qwen3.8 27B measured |
| LMArena Vision: homework | Not measured | 1284 | 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 Ember | 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 | 1461 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Muse Ember | 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 |
| AA-Omniscience | 7.7 | -6.4 | Muse Ember |
| GeoBench | Not measured | 38.0% | Only Qwen3.8 27B measured |
| LMArena Text: science | Not measured | 1463 | Only Qwen3.8 27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1439 | Only Qwen3.8 27B measured |
| LMArena Text: legal and government | Not measured | 1446 | Only Qwen3.8 27B measured |
| LMArena Text: business and finance | Not measured | 1440 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Muse Ember | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench design | Not measured | 72.4% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index31 boards, none shared
| Benchmark | Muse Ember | Qwen3.8 27B | Leader |
|---|---|---|---|
| Vals Index | 60.5% | Not measured | Only Muse Ember measured |
| LiveBench data analysis | Not measured | 76.6% | 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 |
| GDPval-AA | Not measured | 1426 | 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 | 1437 | Only Qwen3.8 27B measured |
| LMArena Text: instruction following | Not measured | 1431 | Only Qwen3.8 27B measured |
| LMArena Text: creative writing | Not measured | 1377 | Only Qwen3.8 27B measured |
| LMArena Vision | Not measured | 1270 | Only Qwen3.8 27B measured |
| LMArena Text: English | Not measured | 1447 | Only Qwen3.8 27B measured |
| LMArena Text: non-English | Not measured | 1424 | Only Qwen3.8 27B measured |
| LMArena Text: Chinese | Not measured | 1493 | Only Qwen3.8 27B measured |
| LMArena Text: German | Not measured | 1436 | Only Qwen3.8 27B measured |
| LMArena Text: Japanese | Not measured | 1398 | Only Qwen3.8 27B measured |
| LMArena Text: Korean | Not measured | 1389 | Only Qwen3.8 27B measured |
| LMArena Text: French | Not measured | 1466 | Only Qwen3.8 27B measured |
| LMArena Text: Spanish | Not measured | 1443 | Only Qwen3.8 27B measured |
| LMArena Text: Russian | Not measured | 1408 | Only Qwen3.8 27B measured |
| LMArena Text: Polish | Not measured | 1441 | Only Qwen3.8 27B measured |
| LMArena Text: multi-turn | Not measured | 1436 | Only Qwen3.8 27B measured |
| LMArena Text: longer queries | Not measured | 1446 | Only Qwen3.8 27B measured |
| LMArena Text: writing and language | Not measured | 1401 | Only Qwen3.8 27B measured |
| LMArena Text: entertainment and media | Not measured | 1388 | Only Qwen3.8 27B measured |
| LMArena Vision: English | Not measured | 1272 | Only Qwen3.8 27B measured |
| LMArena Vision: Chinese | Not measured | 1327 | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | Not measured | 1280 | Only Qwen3.8 27B measured |
| LMArena Vision: humor | Not measured | 1245 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | Not measured | 1234 | 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 Ember | 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 Ember costs less
Repository review
50K input + 3K output tokens
Muse Ember costs less
Agent loop
200K input + 10K output tokens
Muse Ember 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 Ember | Qwen3.8 27B |
|---|---|---|
| Maker | Meta | Qwen |
| Weights | No public weights listed | Open weights |
| Released | November 9, 2026 | August 14, 2026 |
| Context window | 1.05M | 1.02M |
| Input $/Mtok | $0.10 | $0.425 |
| Output $/Mtok | $0.50 | $2.55 |
| Coding index | 64.9 | 69.7 |
| Independent measurements behind it | 3 | 4 |
| Cost per solved task | Not computable | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Muse Ember better than Qwen3.8 27B for coding?
Pick Qwen3.8 27B for coding: it scores 69.7 on the Vibe Coding Index against 64.9 for Muse Ember. Pick Muse Ember for output tokens 5.1x cheaper ($0.50 vs $2.55 per million) and a larger context window (1.05M against 1.02M tokens).
Which is cheaper, Muse Ember or Qwen3.8 27B?
Muse Ember costs $0.10 per million input tokens and $0.50 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 Ember or Qwen3.8 27B?
Muse Ember, with 1.05M tokens against 1.02M.
How many benchmarks is the Muse Ember vs Qwen3.8 27B comparison based on?
12 benchmarks have a result for both models as of November 14, 2026. Muse Ember's coding index rests on 3 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.