Muse Spark Open vs Qwen2.5 Coder 32B Instruct
Updated October 21, 2026. 3 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Muse Spark Open scores 62.1 and Qwen2.5 Coder 32B Instruct 30.5 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking.
Pick Muse Spark Open when the context window is larger, 1.05M against 33K tokens, or when what matters is Epoch Capabilities Index or GPQA Diamond (Artificial Analysis run), where it leads; pick Qwen2.5 Coder 32B Instruct when output tokens cost 4.3x less.
- Shared results
- 3
- Muse Spark Open only
- 23
- Qwen2.5 Coder 32B Instruct only
- 32
- Like-for-like categories
- 2 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, four public coding benchmarks
No call
62.1 against 30.5, from different benchmarks with none in common.
Directional only
Intelligence
AA Intelligence Index
No call
Only Qwen2.5 Coder 32B Instruct has a AA Intelligence Index result, 6.7.
Directional only
Web design
Design Arena: websites
No call
Only Muse Spark Open has a Design Arena: websites result, 1311.
Directional only
Long documents
Listed context window
Muse Spark Open
Muse Spark Open takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 Coder 32B Instruct
Muse Spark Open $0.0034, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Muse Spark Open 62.1 from 2 benchmarks, Qwen2.5 Coder 32B Instruct 30.5 from 2 benchmarks.
- Widest category
- Intelligence, 44.6 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Muse Spark Open 89.0% against Qwen2.5 Coder 32B Instruct 41.7%, 47.3 points apart on a 0 to 100 scale.
- Output price
- Muse Spark Open $4.25 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 4.3x apart.
- Context window
- Muse Spark Open 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.
| Category | Muse Spark Open | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlyFour of these boards feed the Vibe Coding Index14 boards, none shared
| Benchmark | Muse Spark Open | 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 |
| LiveBench coding | 73.6% | Not measured | Only Muse Spark Open measured |
| LMArena WebDev | 1528 | Not measured | Only Muse Spark Open measured |
| Terminal-Bench 4 (Vals AI) | 6.6% | Not measured | Only Muse Spark Open measured |
| Vibe Code Bench (Vals AI) | 76.2% | Not measured | Only Muse Spark Open measured |
| Code migration (Vals AI) | 21.2% | Not measured | Only Muse Spark Open measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.5% | Only Qwen2.5 Coder 32B Instruct measured |
| SciCode | 58.4% | Not measured | Only Muse Spark Open measured |
| ALE-Bench | 1590.1 | Not measured | Only Muse Spark Open 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 |
| LMArena WebDev: React | 1529 | Not measured | Only Muse Spark Open measured |
| LMArena WebDev: HTML | 1495 | Not measured | Only Muse Spark Open measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Muse Spark Open | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only Qwen2.5 Coder 32B Instruct measured |
| Epoch Capabilities Index | 155.1 | 119.5 | Muse Spark Open |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Muse Spark Open | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| LiveBench research | 80.3% | Not measured | Only Muse Spark Open measured |
| LiveBench browser | 36.0% | Not measured | Only Muse Spark Open measured |
| LiveBench long agentic | 18.9% | Not measured | Only Muse Spark Open measured |
| Finance Agent (Vals AI) | 61.3% | Not measured | Only Muse Spark Open measured |
| Tax Agent Bench (Vals AI) | 26.3% | Not measured | Only Muse Spark Open measured |
| Legal Research Bench (Vals AI) | 38.5% | Not measured | Only Muse Spark Open measured |
| Excel Modeling Benchmark (Vals AI) | 53.7% | Not measured | Only Muse Spark Open measured |
| APEX-Agents | 33.4% | Not measured | Only Muse Spark Open measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Muse Spark Open | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| LiveBench reasoning | 73.7% | Not measured | Only Muse Spark Open measured |
| GPQA Diamond (Artificial Analysis run) | 89.0% | 41.7% | Muse Spark Open |
| Humanity's Last Exam (Artificial Analysis run) | 43.9% | 3.5% | Muse Spark Open |
| 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 | Muse Spark Open | 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 | Muse Spark Open | 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 designDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Muse Spark Open | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1311 | Not measured | Only Muse Spark Open measured |
| Design Arena: game dev | 1290 | Not measured | Only Muse Spark Open measured |
| LiveBench design | 60.3% | Not measured | Only Muse Spark Open measured |
| Neither model has a result on the other 6 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | Muse Spark Open | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| LiveBench data analysis | 60.2% | Not measured | Only Muse Spark Open measured |
| Long context reasoning (AA-LCR) | 78.3% | Not measured | Only Muse Spark Open 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 31 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Muse Spark Open | 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 | Meta Muse Spark Open | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Meta | Qwen |
| Weights | Open weights | Open weights |
| Released | October 15, 2026 | November 11, 2024 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $1.25 | $0.66 |
| Output $/Mtok | $4.25 | $1.00 |
| Coding index | 62.1 | 30.5 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Muse Spark Open better than Qwen2.5 Coder 32B Instruct for coding?
Muse Spark Open scores 62.1 and Qwen2.5 Coder 32B Instruct 30.5 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking. Pick Muse Spark Open when the context window is larger, 1.05M against 33K tokens, or when what matters is Epoch Capabilities Index or GPQA Diamond (Artificial Analysis run), where it leads; pick Qwen2.5 Coder 32B Instruct when output tokens cost 4.3x less.
Which is cheaper, Muse Spark Open or Qwen2.5 Coder 32B Instruct?
Muse Spark Open costs $1.25 per million input tokens and $4.25 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, Muse Spark Open or Qwen2.5 Coder 32B Instruct?
Muse Spark Open, with 1.05M tokens against 33K.
How many benchmarks is the Muse Spark Open vs Qwen2.5 Coder 32B Instruct comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. Muse Spark Open's coding index rests on 2 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 2. Where only one model has a result, the page says so instead of naming a winner.