GPT-5.6 Sol vs Muse Spark 1.1
GPT-5.6 Sol leads Muse Spark 1.1 on coding, 73.5 to 70.3 on the Vibe Coding Index, and Muse Spark 1.1 costs 4.7x less per output token ($4.25 vs $20.00 per million). Updated October 22, 2026; 94 benchmarks have a result for both.
GPT-5.6 Sol or Muse Spark 1.1: which to pick
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 70.3 for Muse Spark 1.1.
Pick Muse Spark 1.1 for output tokens 4.7x cheaper ($4.25 vs $20.00 per million) and a lower cost per solved task ($0.43 against $1.20).
- Shared results
- 94
- GPT-5.6 Sol only
- 45
- Muse Spark 1.1 only
- 2
- 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
GPT-5.6 Sol
GPT-5.6 Sol scores 73.5 against 70.3.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 50.2 against 25.8.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Sol
GPT-5.6 Sol leads, 95.2% against 91.2%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.6 Sol
GPT-5.6 Sol leads, 89.1% against 88.7%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1270.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Spark 1.1
GPT-5.6 Sol $0.014, Muse Spark 1.1 $0.0034 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Muse Spark 1.1
GPT-5.6 Sol $1.20, Muse Spark 1.1 $0.43.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Sol 73.5 from 6 benchmarks, Muse Spark 1.1 70.3 from 3 benchmarks.
- Widest category
- Math, 15.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- ProofBench: GPT-5.6 Sol 83.0% against Muse Spark 1.1 39.0%, 44.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Muse Spark 1.1 $4.25 per million output tokens, 4.7x apart.
- Context window
- GPT-5.6 Sol takes 1.05M 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 | GPT-5.6 Sol | Bars | Muse Spark 1.1 | Ties |
|---|---|---|---|---|
| Coding | 10 | 5 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 13 | 1 | ||
| Reasoning | 8 | 2 | ||
| Math | 2 | 1 | ||
| Knowledge | 4 | 6 | ||
| Web design | 8 | 0 | ||
| General | 17 | 15 | ||
| All shared boards | 64 | 30 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index105
| Benchmark | GPT-5.6 Sol | Muse Spark 1.1 | Leader |
|---|---|---|---|
| LiveBench coding | 89.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1616 | 1542 | GPT-5.6 Sol |
| AA Coding Index | 77.4 | 71.3 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 82.6% | 85.9% | Muse Spark 1.1 |
| SWE-bench Verified (Vals AI) | 96.2% | 82.0% | GPT-5.6 Sol |
| Terminal-Bench 4 (Vals AI) | 37.9% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench (Vals AI) | 80.5% | 72.2% | GPT-5.6 Sol |
| Code migration (Vals AI) | 52.9% | 31.1% | GPT-5.6 Sol |
| ProgramBench (Vals AI) | 1.5% | 0.0% | GPT-5.6 Sol |
| IOI (Vals AI) | 91.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | 69.3% | GPT-5.6 Sol |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | 54.1% | 59.2% | Muse Spark 1.1 |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 57.8% | 58.8% | Muse Spark 1.1 |
| Terminal-Bench Hard (Artificial Analysis run) | 65.9% | Not measured | Only GPT-5.6 Sol measured |
| FrontierCode | 47.5% | Not measured | Only GPT-5.6 Sol measured |
| FrontierSWE | 32.2% | Not measured | Only GPT-5.6 Sol measured |
| GSO-Bench | 76.5% | Not measured | Only GPT-5.6 Sol measured |
| WeirdML | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| MirrorCode | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| ALE-Bench | 2176.9 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: coding | 1496 | 1498 | Muse Spark 1.1 |
| LMArena Text: software and IT | 1489 | 1498 | Muse Spark 1.1 |
| LMArena WebDev: React | 1620 | 1532 | GPT-5.6 Sol |
| LMArena WebDev: HTML | 1618 | 1540 | GPT-5.6 Sol |
| LMArena WebDev: from an image | 1609 | 1540 | GPT-5.6 Sol |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol | Muse Spark 1.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | 33.7 | GPT-5.6 Sol |
| Epoch Capabilities Index | 162.8 | 154.1 | GPT-5.6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index131
| Benchmark | GPT-5.6 Sol | Muse Spark 1.1 | Leader |
|---|---|---|---|
| AA Agentic Index | 50.2 | 25.8 | GPT-5.6 Sol |
| Design Arena: agents building web apps | 1245 | 1207 | GPT-5.6 Sol |
| Design Arena: agents building full-stack apps | 1261 | 1199 | GPT-5.6 Sol |
| Design Arena: agents building mobile apps | 1248 | 1175 | GPT-5.6 Sol |
| Design Arena: agents building Android apps | 1240 | 1178 | GPT-5.6 Sol |
| Design Arena: agents building Godot games | 1260 | 1103 | GPT-5.6 Sol |
| Design Arena: agents building games | 1266 | 1183 | GPT-5.6 Sol |
| Design Arena: HTML slides | 1236 | 1145 | GPT-5.6 Sol |
| Design Arena: PowerPoint slides | 1217 | 1134 | GPT-5.6 Sol |
| Design Arena: spreadsheets | Not measured | 1021 | Only Muse Spark 1.1 measured |
| LiveBench research | 54.5% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench long agentic | 48.5% | Not measured | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 53.8% | 57.2% | Muse Spark 1.1 |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only GPT-5.6 Sol measured |
| Legal Research Bench (Vals AI) | 48.1% | 38.0% | GPT-5.6 Sol |
| Excel Modeling Benchmark (Vals AI) | 72.3% | 56.4% | GPT-5.6 Sol |
| BioMysteryBench (Vals AI) | 71.1% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| Vals Science Index | 41.5% | Not measured | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | 23.8% | 1.7% | GPT-5.6 Sol |
| CUA-bench (Vals AI) | 8.3% | Not measured | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 85.1% | Not measured | Only GPT-5.6 Sol measured |
| APEX-Agents | Not measured | 31.8% | Only Muse Spark 1.1 measured |
| OSWorld 2.0 | 27.3% | Not measured | Only GPT-5.6 Sol measured |
| BALROG | 60.0% | Not measured | Only GPT-5.6 Sol measured |
| PostTrainBench | 36.2% | Not measured | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | $9,619 | $6,520 | GPT-5.6 Sol |
| LMArena Search | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index82
| Benchmark | GPT-5.6 Sol | Muse Spark 1.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | 91.2% | GPT-5.6 Sol |
| LiveBench reasoning | 91.7% | Not measured | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | 33.3% | Not measured | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | 88.8% | 86.6% | GPT-5.6 Sol |
| GPQA Diamond (Artificial Analysis run) | 94.1% | 89.8% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | 46.2% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI | 97.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | 92.5% | Not measured | Only GPT-5.6 Sol measured |
| SimpleBench | 64.8% | Not measured | Only GPT-5.6 Sol measured |
| Chess puzzles | 59.0% | 34.0% | GPT-5.6 Sol |
| Mystery game puzzles | 58.0% | Not measured | Only GPT-5.6 Sol measured |
| EBR-bench | 44.8% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1483 | 1485 | Muse Spark 1.1 |
| LMArena Text: expert | 1517 | 1478 | GPT-5.6 Sol |
| LMArena Text: hard prompts in English | 1477 | 1480 | Muse Spark 1.1 |
| LMArena Vision: diagrams | 1305 | 1305 | GPT-5.6 Sol |
| LMArena Vision: homework | 1305 | 1287 | GPT-5.6 Sol |
| Kagi LLM Benchmark | 67.0% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GPT-5.6 Sol | Muse Spark 1.1 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tiers 1 to 3 | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tier 4 | 82.9% | Not measured | Only GPT-5.6 Sol measured |
| ProofBench | 83.0% | 39.0% | GPT-5.6 Sol |
| LMArena Text: math | 1473 | 1482 | Muse Spark 1.1 |
| LMArena Text: mathematical work | 1496 | 1487 | GPT-5.6 Sol |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
| Benchmark | GPT-5.6 Sol | Muse Spark 1.1 | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | 88.7% | GPT-5.6 Sol |
| LegalBench (Vals AI) | 87.0% | 85.0% | GPT-5.6 Sol |
| CorpFin (Vals AI) | 64.4% | 71.3% | Muse Spark 1.1 |
| TaxEval (Vals AI) | 74.8% | 79.7% | Muse Spark 1.1 |
| MortgageTax (Vals AI) | 67.3% | 66.1% | GPT-5.6 Sol |
| MedCode (Vals AI) | 44.0% | Not measured | Only GPT-5.6 Sol measured |
| SimpleQA Verified | 69.7% | 57.8% | GPT-5.6 Sol |
| LMArena Text: science | 1455 | 1484 | Muse Spark 1.1 |
| LMArena Text: medicine and healthcare | 1431 | 1479 | Muse Spark 1.1 |
| LMArena Text: legal and government | 1476 | 1481 | Muse Spark 1.1 |
| LMArena Text: business and finance | 1457 | 1470 | Muse Spark 1.1 |
| FACTS Benchmark Suite | 52.4% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.6 Sol | Muse Spark 1.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | 1270 | GPT-5.6 Sol |
| Design Arena: UI components | 1335 | 1287 | GPT-5.6 Sol |
| Design Arena: game dev | 1367 | 1269 | GPT-5.6 Sol |
| Design Arena: data viz | 1323 | 1267 | GPT-5.6 Sol |
| Design Arena: 3D | 1384 | 1259 | GPT-5.6 Sol |
| Design Arena: all code categories | 1341 | 1276 | GPT-5.6 Sol |
| Design Arena: SVG | 1315 | 1234 | GPT-5.6 Sol |
| Design Arena: ASCII art | 1304 | 1269 | GPT-5.6 Sol |
| LiveBench design | 81.6% | Not measured | Only GPT-5.6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index1715
| Benchmark | GPT-5.6 Sol | Muse Spark 1.1 | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.8% | Not measured | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | 66.5% | 61.3% | GPT-5.6 Sol |
| SAGE (Vals AI) | 52.6% | 45.6% | GPT-5.6 Sol |
| MedScribe (Vals AI) | 85.2% | 88.9% | Muse Spark 1.1 |
| Vals Multimodal Index | 72.6% | 66.7% | GPT-5.6 Sol |
| IFBench | 72.7% | Not measured | Only GPT-5.6 Sol measured |
| Long context reasoning (AA-LCR) | 84.0% | 77.7% | GPT-5.6 Sol |
| DTBench | 95.7% | 94.4% | GPT-5.6 Sol |
| LMCA | 58.5% | 49.9% | GPT-5.6 Sol |
| Surface Evolver Bench | 93.1% | 52.5% | GPT-5.6 Sol |
| Furniture assembly | 56.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text | 1457 | 1478 | Muse Spark 1.1 |
| LMArena Text: instruction following | 1483 | 1457 | GPT-5.6 Sol |
| LMArena Text: creative writing | 1449 | 1435 | GPT-5.6 Sol |
| LMArena Vision | 1281 | 1293 | Muse Spark 1.1 |
| LMArena Text: English | 1457 | 1478 | Muse Spark 1.1 |
| LMArena Text: non-English | 1451 | 1471 | Muse Spark 1.1 |
| LMArena Text: Chinese | 1525 | 1515 | GPT-5.6 Sol |
| LMArena Text: German | 1476 | 1469 | GPT-5.6 Sol |
| LMArena Text: Japanese | 1472 | 1450 | GPT-5.6 Sol |
| LMArena Text: Korean | 1442 | 1459 | Muse Spark 1.1 |
| LMArena Text: French | 1472 | 1492 | Muse Spark 1.1 |
| LMArena Text: Spanish | 1443 | 1464 | Muse Spark 1.1 |
| LMArena Text: Russian | 1471 | 1482 | Muse Spark 1.1 |
| LMArena Text: Polish | 1459 | 1499 | Muse Spark 1.1 |
| LMArena Text: multi-turn | 1461 | 1484 | Muse Spark 1.1 |
| LMArena Text: longer queries | 1481 | 1462 | GPT-5.6 Sol |
| LMArena Text: writing and language | 1462 | 1448 | GPT-5.6 Sol |
| LMArena Text: entertainment and media | 1450 | 1438 | GPT-5.6 Sol |
| LMArena Vision: English | 1280 | 1292 | Muse Spark 1.1 |
| LMArena Vision: Chinese | 1313 | 1348 | Muse Spark 1.1 |
| LMArena Vision: reading text | 1290 | 1301 | Muse Spark 1.1 |
| LMArena Vision: humor | 1246 | 1288 | Muse Spark 1.1 |
| LMArena Vision: creative writing | 1283 | 1253 | GPT-5.6 Sol |
| LMArena Document | 1483 | 1465 | GPT-5.6 Sol |
| 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 | GPT-5.6 Sol | 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
Muse Spark 1.1 costs less
Repository review
50K input + 3K output tokens
Muse Spark 1.1 costs less
Agent loop
200K input + 10K output tokens
Muse Spark 1.1 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 | OpenAI GPT-5.6 Sol | Meta Muse Spark 1.1 |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | July 16, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $4.00 | $1.25 |
| Output $/Mtok | $20.00 | $4.25 |
| Coding index | 73.5 | 70.3 |
| Independent measurements behind it | 6 | 3 |
| Cost per solved task | $1.20 | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Muse Spark 1.1 for coding?
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 70.3 for Muse Spark 1.1. Pick Muse Spark 1.1 for output tokens 4.7x cheaper ($4.25 vs $20.00 per million) and a lower cost per solved task ($0.43 against $1.20).
Which is cheaper, GPT-5.6 Sol or Muse Spark 1.1?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Muse Spark 1.1 costs $1.25 and $4.25. Per solved SWE-bench Verified task, GPT-5.6 Sol costs $1.20 and Muse Spark 1.1 $0.43.
Which has the bigger context window, GPT-5.6 Sol or Muse Spark 1.1?
GPT-5.6 Sol, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-5.6 Sol vs Muse Spark 1.1 comparison based on?
94 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Sol's coding index rests on 6 independent benchmarks and Muse Spark 1.1's on 3. Where only one model has a result, the page says so instead of naming a winner.