GPT-5.6 Sol vs Qwen3.8 Max (0902)
GPT-5.6 Sol leads Qwen3.8 Max (0902) on coding, 73.5 to 62.4 on the Vibe Coding Index, and Qwen3.8 Max (0902) costs 3.3x less per output token ($6.00 vs $20.00 per million). Updated October 22, 2026; 110 benchmarks have a result for both.
GPT-5.6 Sol or Qwen3.8 Max (0902): which to pick
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 62.4 for Qwen3.8 Max (0902).
Pick Qwen3.8 Max (0902) for output tokens 3.3x cheaper ($6.00 vs $20.00 per million) and work measured by LMArena WebDev or LiveCodeBench (Vals AI), where it leads.
- Shared results
- 110
- GPT-5.6 Sol only
- 29
- Qwen3.8 Max (0902) only
- 3
- 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 62.4.
6 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 45.4.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 56.0 against 50.2.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Sol
GPT-5.6 Sol leads, 95.2% against 93.7%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.6 Sol
GPT-5.6 Sol leads, 89.1% against 88.6%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1275.
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
Qwen3.8 Max (0902)
GPT-5.6 Sol $0.014, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Sol
GPT-5.6 Sol $1.20, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Sol 73.5 from 6 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
- Widest category
- Math, 13.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- LiveBench browser: GPT-5.6 Sol 88.0% against Qwen3.8 Max (0902) 32.0%, 56.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Qwen3.8 Max (0902) $6.00 per million output tokens, 3.3x apart.
- Context window
- GPT-5.6 Sol takes 1.05M tokens, Qwen3.8 Max (0902) 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 | Qwen3.8 Max (0902) | Ties |
|---|---|---|---|---|
| Coding | 14 | 7 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 10 | 6 | ||
| Reasoning | 10 | 4 | ||
| Math | 4 | 1 | 1 tie | |
| Knowledge | 5 | 6 | ||
| Web design | 7 | 1 | ||
| General | 9 | 23 | ||
| All shared boards | 61 | 48 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index147
| Benchmark | GPT-5.6 Sol | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench coding | 89.7% | 60.3% | GPT-5.6 Sol |
| LMArena WebDev | 1616 | 1674 | Qwen3.8 Max (0902) |
| AA Coding Index | 77.4 | 76.2 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 82.6% | 87.8% | Qwen3.8 Max (0902) |
| SWE-bench Verified (Vals AI) | 96.2% | 85.6% | GPT-5.6 Sol |
| Terminal-Bench 4 (Vals AI) | 37.9% | 34.3% | GPT-5.6 Sol |
| Vibe Code Bench (Vals AI) | 80.5% | 64.7% | GPT-5.6 Sol |
| Code migration (Vals AI) | 52.9% | 24.0% | GPT-5.6 Sol |
| ProgramBench (Vals AI) | 1.5% | 0.0% | GPT-5.6 Sol |
| IOI (Vals AI) | 91.2% | 68.9% | GPT-5.6 Sol |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | 67.4% | GPT-5.6 Sol |
| SWE-bench Multimodal (Vals AI) | Not measured | 42.5% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | 12.8% | GPT-5.6 Sol |
| SkillsBench (Vals AI) | 54.1% | 42.0% | GPT-5.6 Sol |
| CyberBench (Vals AI) | 76.3% | 28.6% | GPT-5.6 Sol |
| SciCode | 57.8% | 53.2% | GPT-5.6 Sol |
| 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% | 17.8% | GPT-5.6 Sol |
| 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 | 1502 | Qwen3.8 Max (0902) |
| LMArena Text: software and IT | 1489 | 1498 | Qwen3.8 Max (0902) |
| LMArena WebDev: React | 1620 | 1692 | Qwen3.8 Max (0902) |
| LMArena WebDev: HTML | 1618 | 1649 | Qwen3.8 Max (0902) |
| LMArena WebDev: from an image | 1609 | 1637 | Qwen3.8 Max (0902) |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | 45.4 | GPT-5.6 Sol |
| Epoch Capabilities Index | 162.8 | 157.4 | GPT-5.6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index106
| Benchmark | GPT-5.6 Sol | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Agentic Index | 50.2 | 56.0 | Qwen3.8 Max (0902) |
| Design Arena: agents building web apps | 1245 | 1300 | Qwen3.8 Max (0902) |
| Design Arena: agents building full-stack apps | 1261 | 1305 | Qwen3.8 Max (0902) |
| Design Arena: agents building mobile apps | 1248 | 1224 | GPT-5.6 Sol |
| Design Arena: agents building Android apps | 1240 | 1196 | GPT-5.6 Sol |
| Design Arena: agents building Godot games | 1260 | 1234 | GPT-5.6 Sol |
| Design Arena: agents building games | 1266 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | 1236 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: PowerPoint slides | 1215 | 1233 | Qwen3.8 Max (0902) |
| Design Arena: spreadsheets | Not measured | 967 | Only Qwen3.8 Max (0902) measured |
| LiveBench research | 54.5% | 69.9% | Qwen3.8 Max (0902) |
| LiveBench browser | 88.0% | 32.0% | GPT-5.6 Sol |
| LiveBench long agentic | 48.5% | 42.3% | GPT-5.6 Sol |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 53.8% | 50.6% | GPT-5.6 Sol |
| Tax Agent Bench (Vals AI) | 31.1% | 32.1% | Qwen3.8 Max (0902) |
| Legal Research Bench (Vals AI) | 48.1% | 47.6% | GPT-5.6 Sol |
| Excel Modeling Benchmark (Vals AI) | 72.3% | 60.1% | GPT-5.6 Sol |
| BioMysteryBench (Vals AI) | 71.1% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 20.0% | 12.9% | GPT-5.6 Sol |
| 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% | Not measured | Only GPT-5.6 Sol measured |
| APEX-Agents | Not measured | 63.3% | Only Qwen3.8 Max (0902) 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 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Search | 1261 | 1261 | GPT-5.6 Sol |
| Neither model has a result on the other 11 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index104
| Benchmark | GPT-5.6 Sol | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | 93.7% | GPT-5.6 Sol |
| LiveBench reasoning | 91.7% | 88.2% | GPT-5.6 Sol |
| MysteryMechanism (Vals AI) | 33.3% | 23.9% | GPT-5.6 Sol |
| MMMU Pro (Vals AI) | 88.8% | 88.0% | GPT-5.6 Sol |
| GPQA Diamond (Artificial Analysis run) | 94.1% | 92.8% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | 43.1% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | 92.7% | 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% | Not measured | Only GPT-5.6 Sol measured |
| Chess puzzles | 59.0% | 46.0% | GPT-5.6 Sol |
| Mystery game puzzles | 58.0% | 38.0% | GPT-5.6 Sol |
| EBR-bench | 44.8% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1483 | 1496 | Qwen3.8 Max (0902) |
| LMArena Text: expert | 1517 | 1507 | GPT-5.6 Sol |
| LMArena Text: hard prompts in English | 1477 | 1503 | Qwen3.8 Max (0902) |
| LMArena Vision: diagrams | 1305 | 1323 | Qwen3.8 Max (0902) |
| LMArena Vision: homework | 1305 | 1336 | Qwen3.8 Max (0902) |
| 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 index41
| Benchmark | GPT-5.6 Sol | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | 100.0% | Tie |
| FrontierMath, tiers 1 to 3 | 89.1% | 74.7% | GPT-5.6 Sol |
| FrontierMath, tier 4 | 82.9% | 46.3% | GPT-5.6 Sol |
| ProofBench | 83.0% | 58.0% | GPT-5.6 Sol |
| LMArena Text: math | 1473 | 1501 | Qwen3.8 Max (0902) |
| LMArena Text: mathematical work | 1496 | 1491 | 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 index56
| Benchmark | GPT-5.6 Sol | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | 88.6% | GPT-5.6 Sol |
| LegalBench (Vals AI) | 87.0% | 83.6% | GPT-5.6 Sol |
| CorpFin (Vals AI) | 64.4% | 65.8% | Qwen3.8 Max (0902) |
| TaxEval (Vals AI) | 74.8% | 75.5% | Qwen3.8 Max (0902) |
| MortgageTax (Vals AI) | 67.3% | 64.0% | GPT-5.6 Sol |
| MedCode (Vals AI) | 44.0% | 40.7% | GPT-5.6 Sol |
| SimpleQA Verified | 69.7% | 47.3% | GPT-5.6 Sol |
| LMArena Text: science | 1455 | 1512 | Qwen3.8 Max (0902) |
| LMArena Text: medicine and healthcare | 1431 | 1503 | Qwen3.8 Max (0902) |
| LMArena Text: legal and government | 1476 | 1492 | Qwen3.8 Max (0902) |
| LMArena Text: business and finance | 1457 | 1480 | Qwen3.8 Max (0902) |
| 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 index71
| Benchmark | GPT-5.6 Sol | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | 1275 | GPT-5.6 Sol |
| Design Arena: UI components | 1335 | 1338 | Qwen3.8 Max (0902) |
| Design Arena: game dev | 1367 | 1312 | GPT-5.6 Sol |
| Design Arena: data viz | 1322 | 1281 | GPT-5.6 Sol |
| Design Arena: 3D | 1384 | 1336 | GPT-5.6 Sol |
| Design Arena: all code categories | 1341 | 1293 | GPT-5.6 Sol |
| Design Arena: SVG | 1314 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: ASCII art | 1304 | 1200 | GPT-5.6 Sol |
| LiveBench design | 81.6% | 78.6% | GPT-5.6 Sol |
GeneralLike-for-likePublic boards, shown as published, kept out of the index923
| Benchmark | GPT-5.6 Sol | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.8% | 78.4% | GPT-5.6 Sol |
| Public Benefits Bench (Vals AI) | 66.5% | 67.1% | Qwen3.8 Max (0902) |
| SAGE (Vals AI) | 52.6% | 51.2% | GPT-5.6 Sol |
| MedScribe (Vals AI) | 85.2% | 85.0% | GPT-5.6 Sol |
| Vals Multimodal Index | 72.6% | 65.4% | GPT-5.6 Sol |
| IFBench | 72.7% | Not measured | Only GPT-5.6 Sol measured |
| Long context reasoning (AA-LCR) | 84.0% | 80.3% | GPT-5.6 Sol |
| DTBench | 95.7% | 92.0% | GPT-5.6 Sol |
| LMCA | 58.5% | 46.2% | GPT-5.6 Sol |
| Surface Evolver Bench | 93.1% | Not measured | Only GPT-5.6 Sol measured |
| Furniture assembly | 56.7% | 20.0% | GPT-5.6 Sol |
| LMArena Text | 1457 | 1483 | Qwen3.8 Max (0902) |
| LMArena Text: instruction following | 1483 | 1481 | GPT-5.6 Sol |
| LMArena Text: creative writing | 1449 | 1477 | Qwen3.8 Max (0902) |
| LMArena Vision | 1281 | 1313 | Qwen3.8 Max (0902) |
| LMArena Text: English | 1457 | 1491 | Qwen3.8 Max (0902) |
| LMArena Text: non-English | 1451 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Chinese | 1525 | 1539 | Qwen3.8 Max (0902) |
| LMArena Text: German | 1476 | 1478 | Qwen3.8 Max (0902) |
| LMArena Text: Japanese | 1472 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Korean | 1442 | 1460 | Qwen3.8 Max (0902) |
| LMArena Text: French | 1472 | 1498 | Qwen3.8 Max (0902) |
| LMArena Text: Spanish | 1443 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: Russian | 1471 | 1482 | Qwen3.8 Max (0902) |
| LMArena Text: Polish | 1459 | 1471 | Qwen3.8 Max (0902) |
| LMArena Text: multi-turn | 1461 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: longer queries | 1481 | 1488 | Qwen3.8 Max (0902) |
| LMArena Text: writing and language | 1462 | 1477 | Qwen3.8 Max (0902) |
| LMArena Text: entertainment and media | 1450 | 1464 | Qwen3.8 Max (0902) |
| LMArena Vision: English | 1280 | 1311 | Qwen3.8 Max (0902) |
| LMArena Vision: Chinese | 1313 | 1358 | Qwen3.8 Max (0902) |
| LMArena Vision: reading text | 1290 | 1322 | Qwen3.8 Max (0902) |
| LMArena Vision: humor | 1246 | 1304 | Qwen3.8 Max (0902) |
| LMArena Vision: creative writing | 1283 | 1312 | Qwen3.8 Max (0902) |
| 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 | Qwen3.8 Max (0902) | 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
Qwen3.8 Max (0902) costs less
Repository review
50K input + 3K output tokens
Qwen3.8 Max (0902) costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 Max (0902) 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 | Qwen3.8 Max (0902) |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | September 3, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $4.00 | $2.00 |
| Output $/Mtok | $20.00 | $6.00 |
| Coding index | 73.5 | 62.4 |
| Independent measurements behind it | 6 | 5 |
| Cost per solved task | $1.20 | $1.32 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Qwen3.8 Max (0902) for coding?
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 62.4 for Qwen3.8 Max (0902). Pick Qwen3.8 Max (0902) for output tokens 3.3x cheaper ($6.00 vs $20.00 per million) and work measured by LMArena WebDev or LiveCodeBench (Vals AI), where it leads.
Which is cheaper, GPT-5.6 Sol or Qwen3.8 Max (0902)?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, GPT-5.6 Sol costs $1.20 and Qwen3.8 Max (0902) $1.32.
Which has the bigger context window, GPT-5.6 Sol or Qwen3.8 Max (0902)?
GPT-5.6 Sol, with 1.05M tokens against 1M.
How many benchmarks is the GPT-5.6 Sol vs Qwen3.8 Max (0902) comparison based on?
110 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 Qwen3.8 Max (0902)'s on 5. Where only one model has a result, the page says so instead of naming a winner.