DeepSeek V4.1 Flash vs Muse Wonder
Updated October 21, 2026. 55 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 64.5 for Muse Wonder.
Pick Muse Wonder when what matters is LMArena WebDev or Terminal-Bench 4 (Vals AI), where it leads.
- Shared results
- 55
- DeepSeek V4.1 Flash only
- 46
- Muse Wonder only
- 5
- Like-for-like categories
- 8 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
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash scores 77.2 against 64.5.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only DeepSeek V4.1 Flash has a AA Intelligence Index result, 24.7.
Directional only
Web design
Design Arena: websites
Muse Wonder
Muse Wonder leads, 1361 against 1309.
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
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash $0.0003, Muse Wonder $0.014 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 77.2 from 2 benchmarks, Muse Wonder 64.5 from 1 benchmark.
- Widest category
- Math, 12.1 points apart on average over 3 shared benchmarks.
- Widest single gap
- ProofBench: DeepSeek V4.1 Flash 54.0% against Muse Wonder 84.0%, 30.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.525 against Muse Wonder $20.00 per million output tokens, 38.1x apart.
- Context window
- Both take 1.05M tokens.
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 V4.1 Flash | Bars | Muse Wonder | Ties |
|---|---|---|---|---|
| Coding | 2 | 11 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 6 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 6 | ||
| General | 1 | 15 | ||
| All shared boards | 4 | 51 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index211
| Benchmark | DeepSeek V4.1 Flash | Muse Wonder | Leader |
|---|---|---|---|
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | 1652 | Muse Wonder |
| Terminal-Bench 4 (Vals AI) | 19.7% | 49.5% | Muse Wonder |
| Vibe Code Bench (Vals AI) | 84.7% | 83.1% | DeepSeek V4.1 Flash |
| Code migration (Vals AI) | 45.6% | 62.5% | Muse Wonder |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IOI (Vals AI) | 40.3% | 66.9% | Muse Wonder |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| SWE-bench Multimodal (Vals AI) | 46.5% | 45.6% | DeepSeek V4.1 Flash |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | 26.6% | Muse Wonder |
| SkillsBench (Vals AI) | 69.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| CyberBench (Vals AI) | 73.7% | 80.4% | Muse Wonder |
| SciCode | 51.9% | 60.3% | Muse Wonder |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| ALE-Bench | 1092.3 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: coding | 1502 | 1522 | Muse Wonder |
| LMArena Text: software and IT | 1496 | 1515 | Muse Wonder |
| LMArena WebDev: React | 1628 | 1641 | Muse Wonder |
| LMArena WebDev: HTML | 1612 | 1694 | Muse Wonder |
| LMArena WebDev: from an image | 1606 | Not measured | Only DeepSeek V4.1 Flash measured |
| 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 V4.1 Flash | Muse Wonder | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | Not measured | Only DeepSeek V4.1 Flash measured |
| Epoch Capabilities Index | 155.9 | 161.8 | Muse Wonder |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | DeepSeek V4.1 Flash | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | Not measured | 1236 | Only Muse Wonder measured |
| LiveBench research | 86.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench long agentic | 40.1% | Not measured | Only DeepSeek V4.1 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Finance Agent (Vals AI) | 53.5% | 56.6% | Muse Wonder |
| Tax Agent Bench (Vals AI) | 26.6% | 36.3% | Muse Wonder |
| Legal Research Bench (Vals AI) | 41.4% | 50.5% | Muse Wonder |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 23.8% | Only Muse Wonder measured |
| Excel Modeling Benchmark (Vals AI) | 57.2% | 67.1% | Muse Wonder |
| BioMysteryBench (Vals AI) | 67.8% | 67.0% | DeepSeek V4.1 Flash |
| Terminal-Bench-Science (Vals AI) | 0.0% | 22.9% | Muse Wonder |
| Vals Science Index | 29.6% | 39.1% | Muse Wonder |
| CUA-bench (Vals AI) | Not measured | 7.2% | Only Muse Wonder measured |
| APEX-Agents | 39.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vending-Bench 2 | Not measured | $9,432 | Only Muse Wonder measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V4.1 Flash | Muse Wonder | Leader |
|---|---|---|---|
| LiveBench reasoning | 86.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| MysteryMechanism (Vals AI) | 21.2% | 27.5% | Muse Wonder |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 50.1% | Muse Wonder |
| GPQA Diamond (Epoch AI run) | 89.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI | 94.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI-2 | 72.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Chess puzzles | 48.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts | 1483 | 1506 | Muse Wonder |
| LMArena Text: expert | 1501 | 1513 | Muse Wonder |
| LMArena Text: hard prompts in English | 1488 | 1506 | Muse Wonder |
| LMArena Vision: diagrams | 1297 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: homework | 1307 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4.1 Flash | Muse Wonder | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProofBench | 54.0% | 84.0% | Muse Wonder |
| LMArena Text: math | 1478 | 1486 | Muse Wonder |
| LMArena Text: mathematical work | 1485 | 1514 | Muse Wonder |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V4.1 Flash | Muse Wonder | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedCode (Vals AI) | 41.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| SimpleQA Verified | 42.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: science | 1469 | 1506 | Muse Wonder |
| LMArena Text: medicine and healthcare | 1484 | 1512 | Muse Wonder |
| LMArena Text: legal and government | 1443 | 1505 | Muse Wonder |
| LMArena Text: business and finance | 1461 | 1483 | Muse Wonder |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V4.1 Flash | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | 1361 | Muse Wonder |
| Design Arena: UI components | 1314 | 1371 | Muse Wonder |
| Design Arena: game dev | 1331 | 1380 | Muse Wonder |
| Design Arena: data viz | 1290 | 1330 | Muse Wonder |
| Design Arena: 3D | 1356 | 1425 | Muse Wonder |
| Design Arena: all code categories | 1320 | 1370 | Muse Wonder |
| Design Arena: SVG | 1243 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: ASCII art | 1265 | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench design | 79.2% | Not measured | Only DeepSeek V4.1 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index115
| Benchmark | DeepSeek V4.1 Flash | Muse Wonder | Leader |
|---|---|---|---|
| Vals Index | Not measured | 64.9% | Only Muse Wonder measured |
| LiveBench data analysis | 79.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Public Benefits Bench (Vals AI) | 64.3% | 70.2% | Muse Wonder |
| SAGE (Vals AI) | 47.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedScribe (Vals AI) | 85.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Long context reasoning (AA-LCR) | 84.0% | 80.7% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | 95.2% | Muse Wonder |
| LMCA | 47.0% | 56.8% | Muse Wonder |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text | 1464 | 1502 | Muse Wonder |
| LMArena Text: instruction following | 1473 | 1491 | Muse Wonder |
| LMArena Text: creative writing | 1437 | 1491 | Muse Wonder |
| LMArena Vision | 1278 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: English | 1473 | 1507 | Muse Wonder |
| LMArena Text: non-English | 1449 | 1492 | Muse Wonder |
| LMArena Text: Chinese | 1501 | 1537 | Muse Wonder |
| LMArena Text: German | 1470 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Japanese | 1432 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Korean | 1436 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: French | 1465 | 1523 | Muse Wonder |
| LMArena Text: Spanish | 1459 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Russian | 1467 | 1522 | Muse Wonder |
| LMArena Text: Polish | 1457 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: multi-turn | 1461 | 1510 | Muse Wonder |
| LMArena Text: longer queries | 1473 | 1493 | Muse Wonder |
| LMArena Text: writing and language | 1450 | 1496 | Muse Wonder |
| LMArena Text: entertainment and media | 1445 | 1482 | Muse Wonder |
| LMArena Vision: English | 1288 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: Chinese | 1327 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: reading text | 1282 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: humor | 1260 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: creative writing | 1275 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | 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 V4.1 Flash costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 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 V4.1 Flash | Meta Muse Wonder |
|---|---|---|
| Maker | DeepSeek | Meta |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | October 12, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.049 | $4.00 |
| Output $/Mtok | $0.525 | $20.00 |
| Coding index | 77.2 | 64.5 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than Muse Wonder for coding?
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 64.5 for Muse Wonder. Pick Muse Wonder when what matters is LMArena WebDev or Terminal-Bench 4 (Vals AI), where it leads.
Which is cheaper, DeepSeek V4.1 Flash or Muse Wonder?
DeepSeek V4.1 Flash costs $0.049 per million input tokens and $0.525 per million output tokens; Muse Wonder costs $4.00 and $20.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash or Muse Wonder?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash vs Muse Wonder comparison based on?
55 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash's coding index rests on 2 independent benchmarks and Muse Wonder's on 1. Where only one model has a result, the page says so instead of naming a winner.