DeepSeek V3.2 vs Muse Spark 1.1
Updated October 21, 2026. 57 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 V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 48.9 for Muse Spark 1.1.
Pick Muse Spark 1.1 when a solved task costs $0.43 against $0.99, or when the context window is larger, 1.05M against 164K tokens.
- Shared results
- 57
- DeepSeek V3.2 only
- 18
- Muse Spark 1.1 only
- 39
- 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 V3.2
DeepSeek V3.2 scores 54.4 against 48.9.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Muse Spark 1.1
Muse Spark 1.1 leads, 33.7 against 21.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Muse Spark 1.1 has a AA Agentic Index result, 25.8.
Directional only
Reasoning
GPQA Diamond
Muse Spark 1.1
Muse Spark 1.1 leads, 91.2% against 80.3%.
Same board, both measured
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
Muse Spark 1.1
Muse Spark 1.1 leads, 88.7% against 84.9%.
Same board, both measured
Web design
Design Arena: websites
Muse Spark 1.1
Muse Spark 1.1 leads, 1270 against 1175.
Same board, both measured
Long documents
Listed context window
Muse Spark 1.1
Muse Spark 1.1 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0005, Muse Spark 1.1 $0.0034 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Muse Spark 1.1
DeepSeek V3.2 $0.99, Muse Spark 1.1 $0.43.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V3.2 54.4 from 2 benchmarks, Muse Spark 1.1 48.9 from 1 benchmark.
- Widest category
- Coding, 23.7 points apart on average over 9 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against Muse Spark 1.1 72.2%, 67.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.42 against Muse Spark 1.1 $4.25 per million output tokens, 10.1x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, Muse Spark 1.1 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 Spark 1.1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 9 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 8 | ||
| Web design | 0 | 8 | ||
| General | 0 | 20 | ||
| All shared boards | 0 | 57 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index09
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | 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 | 1542 | Muse Spark 1.1 |
| AA Coding Index | 44.2 | 71.3 | Muse Spark 1.1 |
| LiveCodeBench (Vals AI) | 80.7% | 85.9% | Muse Spark 1.1 |
| SWE-bench Verified (Vals AI) | 67.6% | 82.0% | Muse Spark 1.1 |
| Vibe Code Bench (Vals AI) | 5.1% | 72.2% | Muse Spark 1.1 |
| Code migration (Vals AI) | Not measured | 31.1% | Only Muse Spark 1.1 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Muse Spark 1.1 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 69.3% | Only Muse Spark 1.1 measured |
| SkillsBench (Vals AI) | Not measured | 59.2% | Only Muse Spark 1.1 measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SciCode | Not measured | 58.8% | Only Muse Spark 1.1 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 | 1498 | Muse Spark 1.1 |
| LMArena Text: software and IT | 1449 | 1498 | Muse Spark 1.1 |
| LMArena WebDev: React | 1350 | 1532 | Muse Spark 1.1 |
| LMArena WebDev: HTML | 1371 | 1540 | Muse Spark 1.1 |
| LMArena WebDev: from an image | Not measured | 1540 | Only Muse Spark 1.1 measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 33.7 | Muse Spark 1.1 |
| Epoch Capabilities Index | 146.1 | 154.1 | Muse Spark 1.1 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 25.8 | Only Muse Spark 1.1 measured |
| Design Arena: agents building web apps | Not measured | 1207 | Only Muse Spark 1.1 measured |
| Design Arena: agents building full-stack apps | Not measured | 1199 | Only Muse Spark 1.1 measured |
| Design Arena: agents building mobile apps | Not measured | 1177 | Only Muse Spark 1.1 measured |
| Design Arena: agents building Android apps | Not measured | 1184 | Only Muse Spark 1.1 measured |
| Design Arena: agents building Godot games | Not measured | 1103 | Only Muse Spark 1.1 measured |
| Design Arena: agents building games | Not measured | 1183 | Only Muse Spark 1.1 measured |
| Design Arena: HTML slides | Not measured | 1194 | Only Muse Spark 1.1 measured |
| Design Arena: PowerPoint slides | Not measured | 1156 | Only Muse Spark 1.1 measured |
| Design Arena: spreadsheets | Not measured | 974 | Only Muse Spark 1.1 measured |
| Finance Agent (Vals AI) | Not measured | 57.2% | Only Muse Spark 1.1 measured |
| Legal Research Bench (Vals AI) | Not measured | 38.0% | Only Muse Spark 1.1 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.4% | Only Muse Spark 1.1 measured |
| Time Horizon Index (Vals AI) | Not measured | 1.7% | Only Muse Spark 1.1 measured |
| τ²-Bench Telecom | 90.6% | Not measured | Only DeepSeek V3.2 measured |
| APEX-Agents | 21.3% | 31.8% | Muse Spark 1.1 |
| Vending-Bench 2 | $1,034 | $6,520 | Muse Spark 1.1 |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | 91.2% | Muse Spark 1.1 |
| MMMU Pro (Vals AI) | Not measured | 86.6% | Only Muse Spark 1.1 measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | 89.8% | Muse Spark 1.1 |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 46.2% | Muse Spark 1.1 |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| Chess puzzles | Not measured | 34.0% | Only Muse Spark 1.1 measured |
| LMArena Text: hard prompts | 1434 | 1485 | Muse Spark 1.1 |
| LMArena Text: expert | 1436 | 1478 | Muse Spark 1.1 |
| LMArena Text: hard prompts in English | 1446 | 1480 | Muse Spark 1.1 |
| LMArena Vision: diagrams | Not measured | 1305 | Only Muse Spark 1.1 measured |
| LMArena Vision: homework | Not measured | 1287 | Only Muse Spark 1.1 measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | 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 | 39.0% | Only Muse Spark 1.1 measured |
| LMArena Text: math | 1435 | 1482 | Muse Spark 1.1 |
| LMArena Text: mathematical work | 1440 | 1487 | Muse Spark 1.1 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | 88.7% | Muse Spark 1.1 |
| LegalBench (Vals AI) | 76.1% | 85.0% | Muse Spark 1.1 |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | 71.3% | Muse Spark 1.1 |
| TaxEval (Vals AI) | 68.2% | 79.7% | Muse Spark 1.1 |
| MortgageTax (Vals AI) | Not measured | 66.1% | Only Muse Spark 1.1 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 |
| SimpleQA Verified | Not measured | 57.8% | Only Muse Spark 1.1 measured |
| LMArena Text: science | 1446 | 1484 | Muse Spark 1.1 |
| LMArena Text: medicine and healthcare | 1440 | 1479 | Muse Spark 1.1 |
| LMArena Text: legal and government | 1425 | 1481 | Muse Spark 1.1 |
| LMArena Text: business and finance | 1410 | 1470 | Muse Spark 1.1 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | 1270 | Muse Spark 1.1 |
| Design Arena: UI components | 1149 | 1290 | Muse Spark 1.1 |
| Design Arena: game dev | 1132 | 1269 | Muse Spark 1.1 |
| Design Arena: data viz | 1158 | 1268 | Muse Spark 1.1 |
| Design Arena: 3D | 1133 | 1260 | Muse Spark 1.1 |
| Design Arena: all code categories | 1164 | 1276 | Muse Spark 1.1 |
| Design Arena: SVG | 1032 | 1234 | Muse Spark 1.1 |
| Design Arena: ASCII art | 1082 | 1270 | Muse Spark 1.1 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 61.3% | Only Muse Spark 1.1 measured |
| SAGE (Vals AI) | Not measured | 45.6% | Only Muse Spark 1.1 measured |
| MedScribe (Vals AI) | Not measured | 88.9% | Only Muse Spark 1.1 measured |
| Vals Multimodal Index | Not measured | 66.7% | Only Muse Spark 1.1 measured |
| IFBench | 60.7% | Not measured | Only DeepSeek V3.2 measured |
| Long context reasoning (AA-LCR) | 73.3% | 77.7% | Muse Spark 1.1 |
| DTBench | 85.6% | 94.4% | Muse Spark 1.1 |
| LMCA | 28.8% | 49.9% | Muse Spark 1.1 |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| Surface Evolver Bench | Not measured | 52.5% | Only Muse Spark 1.1 measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1478 | Muse Spark 1.1 |
| LMArena Text: instruction following | 1413 | 1457 | Muse Spark 1.1 |
| LMArena Text: creative writing | 1400 | 1435 | Muse Spark 1.1 |
| LMArena Vision | Not measured | 1293 | Only Muse Spark 1.1 measured |
| LMArena Text: English | 1439 | 1478 | Muse Spark 1.1 |
| LMArena Text: non-English | 1409 | 1471 | Muse Spark 1.1 |
| LMArena Text: Chinese | 1455 | 1515 | Muse Spark 1.1 |
| LMArena Text: German | 1413 | 1469 | Muse Spark 1.1 |
| LMArena Text: Japanese | 1374 | 1450 | Muse Spark 1.1 |
| LMArena Text: Korean | 1370 | 1459 | Muse Spark 1.1 |
| LMArena Text: French | 1429 | 1492 | Muse Spark 1.1 |
| LMArena Text: Spanish | 1424 | 1464 | Muse Spark 1.1 |
| LMArena Text: Russian | 1424 | 1482 | Muse Spark 1.1 |
| LMArena Text: Polish | 1419 | 1499 | Muse Spark 1.1 |
| LMArena Text: multi-turn | 1427 | 1484 | Muse Spark 1.1 |
| LMArena Text: longer queries | 1428 | 1462 | Muse Spark 1.1 |
| LMArena Text: writing and language | 1407 | 1448 | Muse Spark 1.1 |
| LMArena Text: entertainment and media | 1395 | 1438 | Muse Spark 1.1 |
| LMArena Vision: English | Not measured | 1292 | Only Muse Spark 1.1 measured |
| LMArena Vision: Chinese | Not measured | 1348 | Only Muse Spark 1.1 measured |
| LMArena Vision: reading text | Not measured | 1301 | Only Muse Spark 1.1 measured |
| LMArena Vision: humor | Not measured | 1288 | Only Muse Spark 1.1 measured |
| LMArena Vision: creative writing | Not measured | 1253 | Only Muse Spark 1.1 measured |
| LMArena Document | Not measured | 1465 | Only Muse Spark 1.1 measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | Muse Spark 1.1 | 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 Spark 1.1 |
|---|---|---|
| Maker | DeepSeek | Meta |
| Weights | Open weights | No public weights listed |
| Released | December 1, 2025 | July 16, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.28 | $1.25 |
| Output $/Mtok | $0.42 | $4.25 |
| Coding index | 54.4 | 48.9 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.99 | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Muse Spark 1.1 for coding?
Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 48.9 for Muse Spark 1.1. Pick Muse Spark 1.1 when a solved task costs $0.43 against $0.99, or when the context window is larger, 1.05M against 164K tokens.
Which is cheaper, DeepSeek V3.2 or Muse Spark 1.1?
DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; Muse Spark 1.1 costs $1.25 and $4.25. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and Muse Spark 1.1 $0.43.
Which has the bigger context window, DeepSeek V3.2 or Muse Spark 1.1?
Muse Spark 1.1, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs Muse Spark 1.1 comparison based on?
57 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks and Muse Spark 1.1's on 1. Where only one model has a result, the page says so instead of naming a winner.