Ember-1 vs Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 costs 19.4x less per output token than Ember-1 ($0.775 vs $15.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.775
Ember-1 or Qwen3 235B A22B Instruct 2507: which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Qwen3 235B A22B Instruct 2507 has none, so the two cannot be ranked on coding.
Pick Qwen3 235B A22B Instruct 2507 for output tokens 19.4x cheaper ($0.775 vs $15.00 per million).
No benchmark has tested both yet. Ember-1 has results on Terminal-Bench 4 (Vals AI), Vibe Code Bench (Vals AI) and Code migration (Vals AI); Qwen3 235B A22B Instruct 2507 on LiveCodeBench (Artificial Analysis run), SciCode and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead.
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 Ember-1 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3 235B A22B Instruct 2507 has a AA Intelligence Index result, 12.0.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3 235B A22B Instruct 2507 has a Design Arena: websites result, 1058.
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 235B A22B Instruct 2507
Ember-1 $0.011, Qwen3 235B A22B Instruct 2507 $0.0005 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, Qwen3 235B A22B Instruct 2507 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Ember-1 $15.00 against Qwen3 235B A22B Instruct 2507 $0.775 per million output tokens, 19.4x apart.
- Context window
- Ember-1 takes 1.05M tokens, Qwen3 235B A22B Instruct 2507 262K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards measured
| Benchmark | Ember-1 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| Terminal-Bench 4 (Vals AI) | 20.7% | Not measured | Only Ember-1 measured |
| Vibe Code Bench (Vals AI) | 83.9% | Not measured | Only Ember-1 measured |
| 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.8% | Only Qwen3 235B A22B Instruct 2507 measured |
| SciCode | Not measured | 41.4% | Only Qwen3 235B A22B Instruct 2507 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 15.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| WeirdML | Not measured | 38.7% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 12.0 | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | Ember-1 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| 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 | 53.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 52.1% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Ember-1 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 79.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 15.9% | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI | Not measured | 11.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI-2 | Not measured | 1.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Ember-1 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 91.0 | Only Qwen3 235B A22B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 91.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 84.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 19 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Ember-1 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1058 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: UI components | Not measured | 963 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: game dev | Not measured | 951 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: data viz | Not measured | 1070 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: 3D | Not measured | 994 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: all code categories | Not measured | 1038 | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Ember-1 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| IFBench | Not measured | 51.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| Long context reasoning (AA-LCR) | Not measured | 72.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| DTBench | Not measured | 78.4% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMCA | Not measured | 23.5% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 43 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 235B A22B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 235B A22B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 235B A22B Instruct 2507 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 235B A22B Instruct 2507 |
|---|---|---|
| Maker | Fireworks | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | July 21, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $3.00 | $0.145 |
| Output $/Mtok | $15.00 | $0.775 |
| Coding index | 52.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ember-1 better than Qwen3 235B A22B Instruct 2507 for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Qwen3 235B A22B Instruct 2507 has none, so the two cannot be ranked on coding. Pick Qwen3 235B A22B Instruct 2507 for output tokens 19.4x cheaper ($0.775 vs $15.00 per million).
Which is cheaper, Ember-1 or Qwen3 235B A22B Instruct 2507?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; Qwen3 235B A22B Instruct 2507 costs $0.145 and $0.775. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or Qwen3 235B A22B Instruct 2507?
Ember-1, with 1.05M tokens against 262K.
Has any benchmark tested both Ember-1 and Qwen3 235B A22B Instruct 2507?
No benchmark has tested both yet. Ember-1 has results on Terminal-Bench 4 (Vals AI), Vibe Code Bench (Vals AI) and Code migration (Vals AI); Qwen3 235B A22B Instruct 2507 on LiveCodeBench (Artificial Analysis run), SciCode and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead. Data as of October 22, 2026.