Ember-1 vs Qwen3.6 27B
Ember-1 leads Qwen3.6 27B on coding, 52.6 to 36.8 on the Vibe Coding Index, and Qwen3.6 27B costs 5.6x less per output token ($2.70 vs $15.00 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
Ember-1 or Qwen3.6 27B: which to pick
Pick Ember-1 for coding: it scores 52.6 on the Vibe Coding Index against 36.8 for Qwen3.6 27B.
Pick Qwen3.6 27B for output tokens 5.6x cheaper ($2.70 vs $15.00 per million).
- Shared results
- 1
- Ember-1 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
Ember-1
Ember-1 scores 52.6 against 36.8.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only Qwen3.6 27B has a AA Intelligence Index result, 21.4.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 27B
Ember-1 $0.011, 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
- Ember-1 52.6 from 2 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Coding, 71.9 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): Ember-1 83.9% against Qwen3.6 27B 11.9%, 71.9 points apart on a 0 to 100 scale.
- Output price
- Ember-1 $15.00 against Qwen3.6 27B $2.70 per million output tokens, 5.6x apart.
- Context window
- Ember-1 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 | Ember-1 | Bars | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Ember-1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| 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) | 20.7% | Not measured | Only Ember-1 measured |
| Vibe Code Bench (Vals AI) | 83.9% | 11.9% | Ember-1 |
| Code migration (Vals AI) | 32.5% | Not measured | Only Ember-1 measured |
| IOI (Vals AI) | 46.3% | Not measured | Only Ember-1 measured |
| CyberBench (Vals AI) | 69.5% | Not measured | Only Ember-1 measured |
| 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 |
| 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 | Ember-1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 21.4 | Only Qwen3.6 27B measured |
| Epoch Capabilities Index | Not measured | 146.2 | Only Qwen3.6 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Ember-1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| Finance Agent (Vals AI) | 51.8% | Not measured | Only Ember-1 measured |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only Ember-1 measured |
| Legal Research Bench (Vals AI) | 44.7% | Not measured | Only Ember-1 measured |
| Excel Modeling Benchmark (Vals AI) | 63.4% | Not measured | Only Ember-1 measured |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Ember-1 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only Ember-1 measured |
| Vals Science Index | 31.8% | Not measured | Only Ember-1 measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Ember-1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| 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 |
| 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 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ember-1 | 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 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Ember-1 | 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 |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ember-1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Ember-1 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| SAGE (Vals AI) | Not measured | 45.6% | Only Qwen3.6 27B 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 |
| DTBench | Not measured | 78.1% | Only Qwen3.6 27B measured |
| LMCA | Not measured | 34.5% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 40 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ember-1 | 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
Qwen3.6 27B costs less
Repository review
50K input + 3K output tokens
Qwen3.6 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 27B 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 | Fireworks Ember-1 | Qwen3.6 27B |
|---|---|---|
| Maker | Fireworks | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | April 27, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $3.00 | $0.45 |
| Output $/Mtok | $15.00 | $2.70 |
| Coding index | 52.6 | 36.8 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ember-1 better than Qwen3.6 27B for coding?
Pick Ember-1 for coding: it scores 52.6 on the Vibe Coding Index against 36.8 for Qwen3.6 27B. Pick Qwen3.6 27B for output tokens 5.6x cheaper ($2.70 vs $15.00 per million).
Which is cheaper, Ember-1 or Qwen3.6 27B?
Ember-1 costs $3.00 per million input tokens and $15.00 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, Ember-1 or Qwen3.6 27B?
Ember-1, with 1.05M tokens against 262K.
How many benchmarks is the Ember-1 vs Qwen3.6 27B comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. Ember-1's coding index rests on 2 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.