Muse Ember vs Qwen3.6 27B
Muse Ember leads Qwen3.6 27B on coding, 64.9 to 36.8 on the Vibe Coding Index, and Muse Ember costs 5.4x less per output token ($0.50 vs $2.70 per million). Updated November 14, 2026; 2 benchmarks have a result for both.
Muse Ember or Qwen3.6 27B: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 36.8 for Qwen3.6 27B.
Qwen3.6 27B is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 2
- Muse Ember only
- 13
- Qwen3.6 27B only
- 23
- Like-for-like categories
- 1 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
Muse Ember
Muse Ember scores 64.9 against 36.8.
3 and 2 benchmarks behind the scores
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
Muse Ember $0.0003, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 27B has the number, $1.30.
Directional only
At a glance
- Coding index
- Muse Ember 64.9 from 3 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Coding, 56.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Muse Ember 83.7% against Qwen3.6 27B 11.9%, 71.8 points apart on a 0 to 100 scale.
- Output price
- Muse Ember $0.50 against Qwen3.6 27B $2.70 per million output tokens, 5.4x apart.
- Context window
- Muse Ember takes 1.05M tokens, Qwen3.6 27B 262K.
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 | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| All shared boards | 2 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Muse Ember | Qwen3.6 27B | Leader |
|---|---|---|---|
| LMArena WebDev | 1609 | Not measured | Only Muse Ember measured |
| AA Coding Index | Not measured | 53.7 | Only Qwen3.6 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 70.0% | Only Qwen3.6 27B measured |
| Terminal-Bench 4 (Vals AI) | 39.4% | Not measured | Only Muse Ember measured |
| Vibe Code Bench (Vals AI) | 83.7% | 11.9% | Muse Ember |
| SciCode | Not measured | 42.8% | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only Qwen3.6 27B measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | 40.9% | 0.0% | Muse Ember |
| FrontierCode 2.0 | 27.2% | Not measured | Only Muse Ember measured |
| LMArena WebDev: React | 1616 | Not measured | Only Muse Ember measured |
| LMArena WebDev: HTML | 1621 | Not measured | Only Muse Ember measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Muse Ember | Qwen3.6 27B | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 146.7 | Only Qwen3.6 27B measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Muse Ember | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| Tax Agent Bench (Vals AI) | 38.2% | Not measured | Only Muse Ember measured |
| Legal Research Bench (Vals AI) | 49.0% | Not measured | Only Muse Ember measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 25.0% | Not measured | Only Muse Ember measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Qwen3.6 27B measured |
| APEX-Agents | 64.3% | Not measured | Only Muse Ember measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Muse Ember | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.2% | Only Qwen3.6 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 23.1% | Only Qwen3.6 27B measured |
| HLE-Diamond (Artificial Analysis run) | 25.1% | Not measured | Only Muse Ember measured |
| CritPt v2, hard subset (Artificial Analysis run) | 39.0% | Not measured | Only Muse Ember measured |
| GPQA Diamond (Epoch AI run) | Not measured | 85.9% | Only Qwen3.6 27B measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Muse Ember | Qwen3.6 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 91.1% | Only Qwen3.6 27B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 35.1% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Muse Ember | Qwen3.6 27B | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | Not measured | 62.3% | Only Qwen3.6 27B measured |
| TaxEval (Vals AI) | Not measured | 71.3% | Only Qwen3.6 27B measured |
| MortgageTax (Vals AI) | Not measured | 68.3% | Only Qwen3.6 27B measured |
| AA-Omniscience | 7.7 | Not measured | Only Muse Ember measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Muse Ember | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Muse Ember | Qwen3.6 27B | Leader |
|---|---|---|---|
| Vals Index | 60.5% | Not measured | Only Muse Ember measured |
| IFBench | Not measured | 67.6% | Only Qwen3.6 27B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.3% | Only Qwen3.6 27B measured |
| GDPval-AA | Not measured | 993 | Only Qwen3.6 27B measured |
| DTBench | Not measured | 78.1% | Only Qwen3.6 27B measured |
| LMCA | Not measured | 34.5% | Only Qwen3.6 27B measured |
| EQ-Bench 4 | Not measured | 1015 | Only Qwen3.6 27B measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Muse Ember | Qwen3.6 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 | Qwen3.6 27B |
|---|---|---|
| Maker | Meta | Qwen |
| Weights | No public weights listed | Open weights |
| Released | November 9, 2026 | April 27, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.10 | $0.45 |
| Output $/Mtok | $0.50 | $2.70 |
| Coding index | 64.9 | 36.8 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | Not computable | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Muse Ember better than Qwen3.6 27B for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 36.8 for Qwen3.6 27B. Qwen3.6 27B is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, Muse Ember or Qwen3.6 27B?
Muse Ember costs $0.10 per million input tokens and $0.50 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Cost per solved task is known for Qwen3.6 27B only.
Which has the bigger context window, Muse Ember or Qwen3.6 27B?
Muse Ember, with 1.05M tokens against 262K.
How many benchmarks is the Muse Ember vs Qwen3.6 27B comparison based on?
2 benchmarks have a result for both models as of November 14, 2026. Muse Ember's coding index rests on 3 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.