GPT-5.2 vs Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads GPT-5.2 on coding, 62.4 to 55.9 on the Vibe Coding Index, and Qwen3.8 Max (0902) costs 2.3x less per output token ($6.00 vs $14.00 per million). Updated October 22, 2026; 79 benchmarks have a result for both.
GPT-5.2 or Qwen3.8 Max (0902): which to pick
Pick Qwen3.8 Max (0902) for coding: it scores 62.4 on the Vibe Coding Index against 55.9 for GPT-5.2.
Pick GPT-5.2 for work measured by Chess puzzles or CorpFin (Vals AI), where it leads.
- Shared results
- 79
- GPT-5.2 only
- 35
- Qwen3.8 Max (0902) only
- 34
- 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
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) scores 62.4 against 55.9.
3 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 30.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 Max (0902) has a AA Agentic Index result, 56.0.
Directional only
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 91.7%.
Same board, both measured
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 86.2%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1195.
Same board, both measured
Long documents
Listed context window
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Max (0902)
GPT-5.2 $0.0088, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.8 Max (0902)
GPT-5.2 $1.91, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.2 55.9 from 3 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
- Widest category
- Agentic work, 20.9 points apart on average over 5 shared benchmarks.
- Widest single gap
- ProofBench: GPT-5.2 15.0% against Qwen3.8 Max (0902) 58.0%, 43.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Qwen3.8 Max (0902) $6.00 per million output tokens, 2.3x apart.
- Context window
- GPT-5.2 takes 400K 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-5.2 | Bars | Qwen3.8 Max (0902) | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 5 | ||
| Reasoning | 1 | 11 | ||
| Math | 0 | 6 | ||
| Knowledge | 4 | 7 | ||
| Web design | 0 | 7 | ||
| General | 2 | 27 | ||
| All shared boards | 7 | 72 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | GPT-5.2 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2 measured |
| SWE-bench Multilingual | 66.7% | Not measured | Only GPT-5.2 measured |
| LiveBench coding | Not measured | 60.3% | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev | 1417 | 1674 | Qwen3.8 Max (0902) |
| AA Coding Index | Not measured | 76.2 | Only Qwen3.8 Max (0902) measured |
| LiveCodeBench (Vals AI) | 85.4% | 87.8% | Qwen3.8 Max (0902) |
| SWE-bench Verified (Vals AI) | 75.8% | 85.6% | Qwen3.8 Max (0902) |
| Terminal-Bench 4 (Vals AI) | Not measured | 34.3% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench (Vals AI) | 53.5% | 64.7% | Qwen3.8 Max (0902) |
| Code migration (Vals AI) | Not measured | 24.0% | Only Qwen3.8 Max (0902) measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 Max (0902) measured |
| IOI (Vals AI) | Not measured | 68.9% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.4% | Only Qwen3.8 Max (0902) measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 42.5% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 12.8% | Only Qwen3.8 Max (0902) measured |
| SkillsBench (Vals AI) | Not measured | 42.0% | Only Qwen3.8 Max (0902) measured |
| CyberBench (Vals AI) | Not measured | 28.6% | Only Qwen3.8 Max (0902) measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| SciCode | Not measured | 53.2% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 measured |
| FrontierSWE | Not measured | 17.8% | Only Qwen3.8 Max (0902) measured |
| GSO-Bench | 27.5% | Not measured | Only GPT-5.2 measured |
| WeirdML | 72.2% | Not measured | Only GPT-5.2 measured |
| ALE-Bench | 1293.5 | Not measured | Only GPT-5.2 measured |
| LMArena Text: coding | 1442 | 1502 | Qwen3.8 Max (0902) |
| LMArena Text: software and IT | 1434 | 1498 | Qwen3.8 Max (0902) |
| LMArena WebDev: React | Not measured | 1692 | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev: HTML | 1427 | 1649 | Qwen3.8 Max (0902) |
| LMArena WebDev: from an image | Not measured | 1637 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 9 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.2 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 45.4 | Qwen3.8 Max (0902) |
| Epoch Capabilities Index | 153.7 | 157.4 | Qwen3.8 Max (0902) |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-5.2 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 56.0 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building web apps | 1086 | 1300 | Qwen3.8 Max (0902) |
| Design Arena: agents building full-stack apps | 1043 | 1305 | Qwen3.8 Max (0902) |
| Design Arena: agents building mobile apps | 1093 | 1224 | Qwen3.8 Max (0902) |
| Design Arena: agents building Android apps | 1059 | 1196 | Qwen3.8 Max (0902) |
| Design Arena: agents building Godot games | 1142 | 1234 | Qwen3.8 Max (0902) |
| Design Arena: PowerPoint slides | Not measured | 1233 | Only Qwen3.8 Max (0902) measured |
| Design Arena: spreadsheets | Not measured | 967 | Only Qwen3.8 Max (0902) measured |
| LiveBench research | Not measured | 69.9% | Only Qwen3.8 Max (0902) measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 Max (0902) measured |
| LiveBench long agentic | Not measured | 42.3% | Only Qwen3.8 Max (0902) measured |
| Finance Agent (Vals AI) | Not measured | 50.6% | Only Qwen3.8 Max (0902) measured |
| Tax Agent Bench (Vals AI) | Not measured | 32.1% | Only Qwen3.8 Max (0902) measured |
| Legal Research Bench (Vals AI) | Not measured | 47.6% | Only Qwen3.8 Max (0902) measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 60.1% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 12.9% | Only Qwen3.8 Max (0902) measured |
| τ²-Bench Telecom | 84.8% | Not measured | Only GPT-5.2 measured |
| APEX-Agents | Not measured | 63.3% | Only Qwen3.8 Max (0902) measured |
| METR time horizons | 75.3% | Not measured | Only GPT-5.2 measured |
| Remote Labor Index | 2.5% | Not measured | Only GPT-5.2 measured |
| GDPval | 49.7% | Not measured | Only GPT-5.2 measured |
| DeepResearch Bench | 41.1% | Not measured | Only GPT-5.2 measured |
| Vending-Bench 2 | $3,591 | Not measured | Only GPT-5.2 measured |
| LMArena Search | Not measured | 1261 | Only Qwen3.8 Max (0902) measured |
| Berkeley Function Calling Leaderboard (BFCL) | 55.9% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index111
| Benchmark | GPT-5.2 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 93.7% | Qwen3.8 Max (0902) |
| LiveBench reasoning | Not measured | 88.2% | Only Qwen3.8 Max (0902) measured |
| MysteryMechanism (Vals AI) | Not measured | 23.9% | Only Qwen3.8 Max (0902) measured |
| MMMU Pro (Vals AI) | 86.7% | 88.0% | Qwen3.8 Max (0902) |
| GPQA Diamond (Artificial Analysis run) | 90.3% | 92.8% | Qwen3.8 Max (0902) |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | 43.1% | Qwen3.8 Max (0902) |
| GPQA Diamond (Epoch AI run) | 91.4% | 92.7% | Qwen3.8 Max (0902) |
| ARC-AGI | 86.2% | Not measured | Only GPT-5.2 measured |
| ARC-AGI-2 | 52.9% | Not measured | Only GPT-5.2 measured |
| SimpleBench | 45.8% | Not measured | Only GPT-5.2 measured |
| Chess puzzles | 48.0% | 46.0% | GPT-5.2 |
| Mystery game puzzles | 23.0% | 38.0% | Qwen3.8 Max (0902) |
| EBR-bench | 23.0% | Not measured | Only GPT-5.2 measured |
| VPCT | 84.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1428 | 1496 | Qwen3.8 Max (0902) |
| LMArena Text: expert | 1445 | 1507 | Qwen3.8 Max (0902) |
| LMArena Text: hard prompts in English | 1431 | 1503 | Qwen3.8 Max (0902) |
| LMArena Vision: diagrams | 1265 | 1323 | Qwen3.8 Max (0902) |
| LMArena Vision: homework | 1276 | 1336 | Qwen3.8 Max (0902) |
| Kagi LLM Benchmark | 73.3% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 2 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-5.2 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AIME | 96.9% | Not measured | Only GPT-5.2 measured |
| MGSM (Vals AI) | 94.0% | Not measured | Only GPT-5.2 measured |
| AA Math Index | 99.0 | Not measured | Only GPT-5.2 measured |
| AIME 2025 (Artificial Analysis run) | 99.0% | Not measured | Only GPT-5.2 measured |
| OTIS Mock AIME | 96.1% | 100.0% | Qwen3.8 Max (0902) |
| FrontierMath, tiers 1 to 3 | 67.4% | 74.7% | Qwen3.8 Max (0902) |
| FrontierMath, tier 4 | 31.7% | 46.3% | Qwen3.8 Max (0902) |
| ProofBench | 15.0% | 58.0% | Qwen3.8 Max (0902) |
| LMArena Text: math | 1440 | 1501 | Qwen3.8 Max (0902) |
| LMArena Text: mathematical work | 1442 | 1491 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index47
| Benchmark | GPT-5.2 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | 88.6% | Qwen3.8 Max (0902) |
| LegalBench (Vals AI) | 82.8% | 83.6% | Qwen3.8 Max (0902) |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | 65.8% | GPT-5.2 |
| TaxEval (Vals AI) | 75.8% | 75.5% | GPT-5.2 |
| MortgageTax (Vals AI) | 67.1% | 64.0% | GPT-5.2 |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | 40.7% | GPT-5.2 |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| SimpleQA Verified | 37.1% | 47.3% | Qwen3.8 Max (0902) |
| LMArena Text: science | 1425 | 1512 | Qwen3.8 Max (0902) |
| LMArena Text: medicine and healthcare | 1425 | 1503 | Qwen3.8 Max (0902) |
| LMArena Text: legal and government | 1427 | 1492 | Qwen3.8 Max (0902) |
| LMArena Text: business and finance | 1414 | 1480 | Qwen3.8 Max (0902) |
| FACTS Benchmark Suite | 48.5% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GPT-5.2 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Design Arena: websites | 1195 | 1275 | Qwen3.8 Max (0902) |
| Design Arena: UI components | 1190 | 1338 | Qwen3.8 Max (0902) |
| Design Arena: game dev | 1195 | 1312 | Qwen3.8 Max (0902) |
| Design Arena: data viz | 1202 | 1281 | Qwen3.8 Max (0902) |
| Design Arena: 3D | 1079 | 1336 | Qwen3.8 Max (0902) |
| Design Arena: all code categories | 1170 | 1293 | Qwen3.8 Max (0902) |
| Design Arena: SVG | 1139 | Not measured | Only GPT-5.2 measured |
| Design Arena: ASCII art | 1152 | 1200 | Qwen3.8 Max (0902) |
| LiveBench design | Not measured | 78.6% | Only Qwen3.8 Max (0902) measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index227
| Benchmark | GPT-5.2 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 78.4% | Only Qwen3.8 Max (0902) measured |
| Public Benefits Bench (Vals AI) | Not measured | 67.1% | Only Qwen3.8 Max (0902) measured |
| SAGE (Vals AI) | 49.3% | 51.2% | Qwen3.8 Max (0902) |
| MedScribe (Vals AI) | 84.4% | 85.0% | Qwen3.8 Max (0902) |
| Vals Multimodal Index | Not measured | 65.4% | Only Qwen3.8 Max (0902) measured |
| IFBench | 75.4% | Not measured | Only GPT-5.2 measured |
| Long context reasoning (AA-LCR) | 82.7% | 80.3% | GPT-5.2 |
| DTBench | 90.9% | 92.0% | Qwen3.8 Max (0902) |
| LMCA | 43.9% | 46.2% | Qwen3.8 Max (0902) |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Furniture assembly | 38.3% | 20.0% | GPT-5.2 |
| LMArena Text | 1416 | 1483 | Qwen3.8 Max (0902) |
| LMArena Text: instruction following | 1409 | 1481 | Qwen3.8 Max (0902) |
| LMArena Text: creative writing | 1376 | 1477 | Qwen3.8 Max (0902) |
| LMArena Vision | 1246 | 1313 | Qwen3.8 Max (0902) |
| LMArena Text: English | 1422 | 1491 | Qwen3.8 Max (0902) |
| LMArena Text: non-English | 1405 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Chinese | 1447 | 1539 | Qwen3.8 Max (0902) |
| LMArena Text: German | 1444 | 1478 | Qwen3.8 Max (0902) |
| LMArena Text: Japanese | 1420 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Korean | 1374 | 1460 | Qwen3.8 Max (0902) |
| LMArena Text: French | 1454 | 1498 | Qwen3.8 Max (0902) |
| LMArena Text: Spanish | 1413 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: Russian | 1415 | 1482 | Qwen3.8 Max (0902) |
| LMArena Text: Polish | 1426 | 1471 | Qwen3.8 Max (0902) |
| LMArena Text: multi-turn | 1419 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: longer queries | 1413 | 1488 | Qwen3.8 Max (0902) |
| LMArena Text: writing and language | 1392 | 1477 | Qwen3.8 Max (0902) |
| LMArena Text: entertainment and media | 1378 | 1464 | Qwen3.8 Max (0902) |
| LMArena Vision: English | 1243 | 1311 | Qwen3.8 Max (0902) |
| LMArena Vision: Chinese | 1305 | 1358 | Qwen3.8 Max (0902) |
| LMArena Vision: reading text | 1257 | 1322 | Qwen3.8 Max (0902) |
| LMArena Vision: humor | 1227 | 1304 | Qwen3.8 Max (0902) |
| LMArena Vision: captioning | 1267 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1225 | 1312 | Qwen3.8 Max (0902) |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 9 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | 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-5.2 | Qwen3.8 Max (0902) |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | December 10, 2025 | September 3, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.75 | $2.00 |
| Output $/Mtok | $14.00 | $6.00 |
| Coding index | 55.9 | 62.4 |
| Independent measurements behind it | 3 | 5 |
| Cost per solved task | $1.91 | $1.32 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Qwen3.8 Max (0902) for coding?
Pick Qwen3.8 Max (0902) for coding: it scores 62.4 on the Vibe Coding Index against 55.9 for GPT-5.2. Pick GPT-5.2 for work measured by Chess puzzles or CorpFin (Vals AI), where it leads.
Which is cheaper, GPT-5.2 or Qwen3.8 Max (0902)?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, GPT-5.2 costs $1.91 and Qwen3.8 Max (0902) $1.32.
Which has the bigger context window, GPT-5.2 or Qwen3.8 Max (0902)?
Qwen3.8 Max (0902), with 1M tokens against 400K.
How many benchmarks is the GPT-5.2 vs Qwen3.8 Max (0902) comparison based on?
79 benchmarks have a result for both models as of October 22, 2026. GPT-5.2's coding index rests on 3 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.