Ember-1 vs Qwen2.5 Coder 32B Instruct
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
- Coding
- No data
- Overall
- 6.7
- Output $/M
- $1.00
Decision reading
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Qwen2.5 Coder 32B Instruct has none, so the two cannot be ranked on coding.
Qwen2.5 Coder 32B Instruct has the edge where output tokens cost 15.0x less.
- Shared results
- 0
- Ember-1 only
- 14
- Qwen2.5 Coder 32B Instruct only
- 35
- Like-for-like categories
- 0 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 Qwen2.5 Coder 32B Instruct has a AA Intelligence Index result, 6.7.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 Coder 32B Instruct
Ember-1 $0.011, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- Ember-1 $15.00 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 15.0x apart.
- Context window
- Ember-1 takes 1.05M tokens, Qwen2.5 Coder 32B Instruct 33K.
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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| 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 | 29.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: coding | Not measured | 1276 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: software and IT | Not measured | 1261 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only Qwen2.5 Coder 32B Instruct measured |
| Epoch Capabilities Index | Not measured | 119.5 | Only Qwen2.5 Coder 32B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | 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 |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 41.7% | Only Qwen2.5 Coder 32B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: hard prompts | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: expert | Not measured | 1221 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1269 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1243 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 63.5% | Only Qwen2.5 Coder 32B Instruct measured |
| MMLU | Not measured | 79.1% | Only Qwen2.5 Coder 32B Instruct measured |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | Not measured | 1221 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1187 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: legal and government | Not measured | 1257 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: business and finance | Not measured | 1224 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | Not measured | 1230 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: instruction following | Not measured | 1223 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: creative writing | Not measured | 1174 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: English | Not measured | 1248 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: non-English | Not measured | 1205 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Chinese | Not measured | 1222 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Russian | Not measured | 1228 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1222 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: longer queries | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: writing and language | Not measured | 1191 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1150 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 32 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ember-1 | Qwen2.5 Coder 32B Instruct | 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
Qwen2.5 Coder 32B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 Coder 32B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 Coder 32B Instruct 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 | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Fireworks | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | November 11, 2024 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $3.00 | $0.66 |
| Output $/Mtok | $15.00 | $1.00 |
| 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 Qwen2.5 Coder 32B Instruct for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Qwen2.5 Coder 32B Instruct has none, so the two cannot be ranked on coding. Qwen2.5 Coder 32B Instruct has the edge where output tokens cost 15.0x less.
Which is cheaper, Ember-1 or Qwen2.5 Coder 32B Instruct?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or Qwen2.5 Coder 32B Instruct?
Ember-1, with 1.05M tokens against 33K.
How many benchmarks is the Ember-1 vs Qwen2.5 Coder 32B Instruct comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. Ember-1's coding index rests on 2 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.