DeepSeek V3.2 vs Muse Wonder
Muse Wonder leads DeepSeek V3.2 on coding, 71.9 to 30.0 on the Vibe Coding Index, and DeepSeek V3.2 costs 22.7x less per output token ($0.88 vs $20.00 per million). Updated October 22, 2026; 39 benchmarks have a result for both.
DeepSeek V3.2 or Muse Wonder: which to pick
Pick Muse Wonder for coding: it scores 71.9 on the Vibe Coding Index against 30.0 for DeepSeek V3.2.
Pick DeepSeek V3.2 for output tokens 22.7x cheaper ($0.88 vs $20.00 per million).
- Shared results
- 39
- DeepSeek V3.2 only
- 36
- Muse Wonder only
- 21
- Like-for-like categories
- 8 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 Wonder
Muse Wonder scores 71.9 against 30.0.
3 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.2 has a AA Intelligence Index result, 21.5.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V3.2 has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3.2 has a MMLU Pro result, 84.9%.
Directional only
Web design
Design Arena: websites
Muse Wonder
Muse Wonder leads, 1362 against 1175.
Same board, both measured
General
Vals Index
No call
Only Muse Wonder has a Vals Index result, 64.9%.
Directional only
Long documents
Listed context window
Muse Wonder
Muse Wonder takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0007, Muse Wonder $0.014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V3.2 has the number, $0.99.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 30.0 from 3 benchmarks, Muse Wonder 71.9 from 3 benchmarks.
- Widest category
- Agentic work, 52.5 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against Muse Wonder 83.1%, 78.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.88 against Muse Wonder $20.00 per million output tokens, 22.7x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, Muse Wonder 1.05M.
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 | DeepSeek V3.2 | Bars | Muse Wonder | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 6 | ||
| General | 0 | 15 | ||
| All shared boards | 0 | 39 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | DeepSeek V3.2 | Muse Wonder | Leader |
|---|---|---|---|
| SWE-bench Verified | 70.0% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | 59.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev | 1362 | 1652 | Muse Wonder |
| AA Coding Index | 44.2 | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | 80.7% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Verified (Vals AI) | 67.6% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 49.5% | Only Muse Wonder measured |
| Vibe Code Bench (Vals AI) | 5.1% | 83.1% | Muse Wonder |
| Code migration (Vals AI) | Not measured | 62.5% | Only Muse Wonder measured |
| IOI (Vals AI) | Not measured | 66.9% | Only Muse Wonder measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 45.6% | Only Muse Wonder measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 26.6% | Only Muse Wonder measured |
| CyberBench (Vals AI) | Not measured | 80.4% | Only Muse Wonder measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SciCode | Not measured | 60.3% | Only Muse Wonder measured |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: coding | 1453 | 1522 | Muse Wonder |
| LMArena Text: software and IT | 1449 | 1515 | Muse Wonder |
| LMArena WebDev: React | 1350 | 1641 | Muse Wonder |
| LMArena WebDev: HTML | 1371 | 1694 | Muse Wonder |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | Muse Wonder | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | Not measured | Only DeepSeek V3.2 measured |
| Epoch Capabilities Index | 146.1 | 161.8 | Muse Wonder |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | Not measured | 1243 | Only Muse Wonder measured |
| Finance Agent (Vals AI) | Not measured | 56.6% | Only Muse Wonder measured |
| Tax Agent Bench (Vals AI) | Not measured | 36.3% | Only Muse Wonder measured |
| Legal Research Bench (Vals AI) | Not measured | 50.5% | Only Muse Wonder measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 23.8% | Only Muse Wonder measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 67.1% | Only Muse Wonder measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Muse Wonder measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 22.9% | Only Muse Wonder measured |
| Vals Science Index | Not measured | 39.1% | Only Muse Wonder measured |
| CUA-bench (Vals AI) | Not measured | 7.2% | Only Muse Wonder measured |
| τ²-Bench Telecom | 90.6% | Not measured | Only DeepSeek V3.2 measured |
| APEX-Agents | 21.3% | Not measured | Only DeepSeek V3.2 measured |
| Vending-Bench 2 | $1,034 | $9,432 | Muse Wonder |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V3.2 | Muse Wonder | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | Not measured | Only DeepSeek V3.2 measured |
| MysteryMechanism (Vals AI) | Not measured | 27.5% | Only Muse Wonder measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | Not measured | Only DeepSeek V3.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 50.1% | Muse Wonder |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts | 1434 | 1506 | Muse Wonder |
| LMArena Text: expert | 1436 | 1513 | Muse Wonder |
| LMArena Text: hard prompts in English | 1446 | 1506 | Muse Wonder |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | Muse Wonder | Leader |
|---|---|---|---|
| AIME | 84.6% | Not measured | Only DeepSeek V3.2 measured |
| MGSM (Vals AI) | 86.0% | Not measured | Only DeepSeek V3.2 measured |
| AA Math Index | 92.0 | Not measured | Only DeepSeek V3.2 measured |
| AIME 2025 (Artificial Analysis run) | 92.0% | Not measured | Only DeepSeek V3.2 measured |
| ProofBench | Not measured | 84.0% | Only Muse Wonder measured |
| LMArena Text: math | 1435 | 1486 | Muse Wonder |
| LMArena Text: mathematical work | 1440 | 1514 | Muse Wonder |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V3.2 | Muse Wonder | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | Not measured | Only DeepSeek V3.2 measured |
| LegalBench (Vals AI) | 76.1% | Not measured | Only DeepSeek V3.2 measured |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | Not measured | Only DeepSeek V3.2 measured |
| TaxEval (Vals AI) | 68.2% | Not measured | Only DeepSeek V3.2 measured |
| MedQA (Vals AI) | 93.9% | Not measured | Only DeepSeek V3.2 measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: science | 1446 | 1506 | Muse Wonder |
| LMArena Text: medicine and healthcare | 1440 | 1512 | Muse Wonder |
| LMArena Text: legal and government | 1425 | 1505 | Muse Wonder |
| LMArena Text: business and finance | 1410 | 1483 | Muse Wonder |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V3.2 | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | 1362 | Muse Wonder |
| Design Arena: UI components | 1146 | 1379 | Muse Wonder |
| Design Arena: game dev | 1131 | 1376 | Muse Wonder |
| Design Arena: data viz | 1158 | 1317 | Muse Wonder |
| Design Arena: 3D | 1132 | 1427 | Muse Wonder |
| Design Arena: all code categories | 1163 | 1371 | Muse Wonder |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: ASCII art | 1083 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index015
| Benchmark | DeepSeek V3.2 | Muse Wonder | Leader |
|---|---|---|---|
| Vals Index | Not measured | 64.9% | Only Muse Wonder measured |
| Public Benefits Bench (Vals AI) | Not measured | 70.2% | Only Muse Wonder measured |
| IFBench | 60.7% | Not measured | Only DeepSeek V3.2 measured |
| Long context reasoning (AA-LCR) | 73.3% | 80.7% | Muse Wonder |
| DTBench | 85.6% | 95.2% | Muse Wonder |
| LMCA | 28.8% | 56.8% | Muse Wonder |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1502 | Muse Wonder |
| LMArena Text: instruction following | 1413 | 1491 | Muse Wonder |
| LMArena Text: creative writing | 1400 | 1491 | Muse Wonder |
| LMArena Text: English | 1439 | 1507 | Muse Wonder |
| LMArena Text: non-English | 1409 | 1492 | Muse Wonder |
| LMArena Text: Chinese | 1455 | 1537 | Muse Wonder |
| LMArena Text: German | 1413 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Japanese | 1374 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Korean | 1370 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: French | 1429 | 1523 | Muse Wonder |
| LMArena Text: Spanish | 1424 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Russian | 1424 | 1522 | Muse Wonder |
| LMArena Text: Polish | 1419 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: multi-turn | 1427 | 1510 | Muse Wonder |
| LMArena Text: longer queries | 1428 | 1493 | Muse Wonder |
| LMArena Text: writing and language | 1407 | 1496 | Muse Wonder |
| LMArena Text: entertainment and media | 1395 | 1482 | Muse Wonder |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | Muse Wonder | 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
DeepSeek V3.2 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 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 | DeepSeek V3.2 | Meta Muse Wonder |
|---|---|---|
| Maker | DeepSeek | Meta |
| Weights | Open weights | No public weights listed |
| Released | December 1, 2025 | October 12, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.29 | $4.00 |
| Output $/Mtok | $0.88 | $20.00 |
| Coding index | 30.0 | 71.9 |
| Independent measurements behind it | 3 | 3 |
| Cost per solved task | $0.99 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Muse Wonder for coding?
Pick Muse Wonder for coding: it scores 71.9 on the Vibe Coding Index against 30.0 for DeepSeek V3.2. Pick DeepSeek V3.2 for output tokens 22.7x cheaper ($0.88 vs $20.00 per million).
Which is cheaper, DeepSeek V3.2 or Muse Wonder?
DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Muse Wonder costs $4.00 and $20.00. Cost per solved task is known for DeepSeek V3.2 only.
Which has the bigger context window, DeepSeek V3.2 or Muse Wonder?
Muse Wonder, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs Muse Wonder comparison based on?
39 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Muse Wonder's on 3. Where only one model has a result, the page says so instead of naming a winner.