GPT-5.6 Sol vs Qwen3.7 Max
GPT-5.6 Sol leads Qwen3.7 Max on coding, 73.5 to 54.1 on the Vibe Coding Index, and Qwen3.7 Max costs 4.5x less per output token ($4.425 vs $20.00 per million). Updated October 21, 2026; 59 benchmarks have a result for both.
GPT-5.6 Sol or Qwen3.7 Max: which to pick
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 54.1 for Qwen3.7 Max.
Pick Qwen3.7 Max for output tokens 4.5x cheaper ($4.425 vs $20.00 per million) and work measured by LiveCodeBench (Vals AI) or τ²-Bench Telecom, where it leads.
- Shared results
- 59
- GPT-5.6 Sol only
- 80
- Qwen3.7 Max only
- 0
- 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 54.1.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 29.5.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 50.2 against 22.5.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Sol
GPT-5.6 Sol leads, 95.2% against 90.2%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 89.1%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1274.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Max
GPT-5.6 Sol $0.014, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Sol
GPT-5.6 Sol $1.20, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Sol 73.5 from 6 benchmarks, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Math, 33.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- ProofBench: GPT-5.6 Sol 83.0% against Qwen3.7 Max 26.0%, 57.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Qwen3.7 Max $4.425 per million output tokens, 4.5x apart.
- Context window
- GPT-5.6 Sol takes 1.05M tokens, Qwen3.7 Max 1M.
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 | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 11 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 13 | 1 | ||
| Reasoning | 7 | 1 | ||
| Math | 4 | 0 | ||
| Knowledge | 4 | 2 | ||
| Web design | 8 | 0 | ||
| General | 4 | 1 | ||
| All shared boards | 53 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index111
| Benchmark | GPT-5.6 Sol | Qwen3.7 Max | Leader |
|---|---|---|---|
| LiveBench coding | 89.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1616 | 1515 | GPT-5.6 Sol |
| AA Coding Index | 77.4 | 66.0 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 82.6% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | 96.2% | 68.8% | 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% | 47.7% | GPT-5.6 Sol |
| Code migration (Vals AI) | 52.9% | 13.1% | GPT-5.6 Sol |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Sol measured |
| IOI (Vals AI) | 91.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | 61.0% | 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% | Not measured | Only GPT-5.6 Sol measured |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 57.8% | 49.5% | GPT-5.6 Sol |
| Terminal-Bench Hard (Artificial Analysis run) | 65.9% | 50.8% | GPT-5.6 Sol |
| 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 | 1189.4 | GPT-5.6 Sol |
| LMArena Text: coding | 1496 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: software and IT | 1489 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: React | 1620 | 1514 | GPT-5.6 Sol |
| LMArena WebDev: HTML | 1618 | 1512 | GPT-5.6 Sol |
| LMArena WebDev: from an image | 1609 | Not measured | Only GPT-5.6 Sol measured |
| 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 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | 29.5 | GPT-5.6 Sol |
| Epoch Capabilities Index | 162.8 | 154.0 | GPT-5.6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index131
| Benchmark | GPT-5.6 Sol | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | 50.2 | 22.5 | GPT-5.6 Sol |
| Design Arena: agents building web apps | 1245 | 1208 | GPT-5.6 Sol |
| Design Arena: agents building full-stack apps | 1261 | 1191 | GPT-5.6 Sol |
| Design Arena: agents building mobile apps | 1248 | 1155 | GPT-5.6 Sol |
| Design Arena: agents building Android apps | 1239 | 1159 | GPT-5.6 Sol |
| Design Arena: agents building Godot games | 1260 | 1187 | GPT-5.6 Sol |
| Design Arena: agents building games | 1267 | 1161 | GPT-5.6 Sol |
| Design Arena: HTML slides | 1286 | 1158 | GPT-5.6 Sol |
| Design Arena: PowerPoint slides | 1241 | 1197 | GPT-5.6 Sol |
| 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% | 47.8% | GPT-5.6 Sol |
| Tax Agent Bench (Vals AI) | 31.1% | 15.8% | GPT-5.6 Sol |
| Legal Research Bench (Vals AI) | 48.1% | 25.5% | GPT-5.6 Sol |
| Excel Modeling Benchmark (Vals AI) | 72.3% | 57.0% | 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% | Not measured | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | 8.3% | Not measured | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 85.1% | 94.7% | Qwen3.7 Max |
| 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 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Search | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 13 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GPT-5.6 Sol | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | 90.2% | 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% | Not measured | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 94.1% | 92.3% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | 40.5% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | 90.9% | GPT-5.6 Sol |
| 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% | 70.4% | Qwen3.7 Max |
| Chess puzzles | 59.0% | 26.0% | GPT-5.6 Sol |
| Mystery game puzzles | 58.0% | 32.0% | GPT-5.6 Sol |
| EBR-bench | 44.8% | 9.5% | GPT-5.6 Sol |
| LMArena Text: hard prompts | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: expert | 1517 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts in English | 1477 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: diagrams | 1305 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | 1305 | Not measured | Only GPT-5.6 Sol measured |
| 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 index40
| Benchmark | GPT-5.6 Sol | Qwen3.7 Max | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | 95.6% | GPT-5.6 Sol |
| FrontierMath, tiers 1 to 3 | 89.1% | 64.6% | GPT-5.6 Sol |
| FrontierMath, tier 4 | 82.9% | 34.1% | GPT-5.6 Sol |
| ProofBench | 83.0% | 26.0% | GPT-5.6 Sol |
| LMArena Text: math | 1473 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: mathematical work | 1496 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | GPT-5.6 Sol | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 87.0% | 84.9% | GPT-5.6 Sol |
| CorpFin (Vals AI) | 64.4% | 63.7% | GPT-5.6 Sol |
| TaxEval (Vals AI) | 74.8% | 75.3% | Qwen3.7 Max |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | 44.0% | 38.8% | GPT-5.6 Sol |
| SimpleQA Verified | 69.7% | 55.8% | GPT-5.6 Sol |
| LMArena Text: science | 1455 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: medicine and healthcare | 1431 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: legal and government | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: business and finance | 1457 | Not measured | Only GPT-5.6 Sol measured |
| 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 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | 1274 | GPT-5.6 Sol |
| Design Arena: UI components | 1336 | 1270 | GPT-5.6 Sol |
| Design Arena: game dev | 1368 | 1265 | GPT-5.6 Sol |
| Design Arena: data viz | 1322 | 1275 | GPT-5.6 Sol |
| Design Arena: 3D | 1386 | 1266 | GPT-5.6 Sol |
| Design Arena: all code categories | 1341 | 1275 | GPT-5.6 Sol |
| Design Arena: SVG | 1316 | 1217 | GPT-5.6 Sol |
| Design Arena: ASCII art | 1304 | 1211 | 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 index41
| Benchmark | GPT-5.6 Sol | Qwen3.7 Max | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.8% | Not measured | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | 66.5% | Not measured | Only GPT-5.6 Sol measured |
| SAGE (Vals AI) | 52.6% | Not measured | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | 85.2% | 79.4% | GPT-5.6 Sol |
| Vals Multimodal Index | 72.6% | Not measured | Only GPT-5.6 Sol measured |
| IFBench | 72.7% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 84.0% | 79.0% | GPT-5.6 Sol |
| DTBench | 95.7% | 92.3% | GPT-5.6 Sol |
| LMCA | 58.5% | 44.0% | GPT-5.6 Sol |
| Surface Evolver Bench | 93.1% | Not measured | Only GPT-5.6 Sol measured |
| Furniture assembly | 56.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: instruction following | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: creative writing | 1449 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision | 1281 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: English | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: non-English | 1451 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Chinese | 1525 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: German | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Japanese | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Korean | 1442 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: French | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Spanish | 1443 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Russian | 1471 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Polish | 1459 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: multi-turn | 1461 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: longer queries | 1481 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: writing and language | 1462 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: entertainment and media | 1450 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: English | 1280 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | 1313 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | 1290 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | 1246 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | 1283 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Document | 1483 | Not measured | Only GPT-5.6 Sol 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 | GPT-5.6 Sol | Qwen3.7 Max | 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
Qwen3.7 Max costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Max costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 Max 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 | Qwen3.7 Max |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | May 21, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $4.00 | $1.475 |
| Output $/Mtok | $20.00 | $4.425 |
| Coding index | 73.5 | 54.1 |
| Independent measurements behind it | 6 | 3 |
| Cost per solved task | $1.20 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Qwen3.7 Max for coding?
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 54.1 for Qwen3.7 Max. Pick Qwen3.7 Max for output tokens 4.5x cheaper ($4.425 vs $20.00 per million) and work measured by LiveCodeBench (Vals AI) or τ²-Bench Telecom, where it leads.
Which is cheaper, GPT-5.6 Sol or Qwen3.7 Max?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GPT-5.6 Sol costs $1.20 and Qwen3.7 Max $4.69.
Which has the bigger context window, GPT-5.6 Sol or Qwen3.7 Max?
GPT-5.6 Sol, with 1.05M tokens against 1M.
How many benchmarks is the GPT-5.6 Sol vs Qwen3.7 Max comparison based on?
59 benchmarks have a result for both models as of October 21, 2026. GPT-5.6 Sol's coding index rests on 6 independent benchmarks and Qwen3.7 Max's on 3. Where only one model has a result, the page says so instead of naming a winner.