GPT-5.2 vs Qwen3.7 Max
GPT-5.2 leads Qwen3.7 Max on coding, 55.9 to 54.1 on the Vibe Coding Index, and Qwen3.7 Max costs 3.2x less per output token ($4.425 vs $14.00 per million). Updated October 21, 2026; 46 benchmarks have a result for both.
GPT-5.2 or Qwen3.7 Max: which to pick
GPT-5.2 and Qwen3.7 Max are level for coding, 55.9 against 54.1 on the Vibe Coding Index, so price and context decide.
Pick Qwen3.7 Max for output tokens 3.2x cheaper ($4.425 vs $14.00 per million) and a larger context window (1M against 400K tokens). Pick GPT-5.2 for a lower cost per solved task ($1.91 against $4.69) and work measured by SWE-bench Verified (Vals AI) or Vibe Code Bench (Vals AI), where it leads.
- Shared results
- 46
- GPT-5.2 only
- 68
- Qwen3.7 Max only
- 13
- 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
No call
Level: 55.9 against 54.1, inside 2 points.
3 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 against 29.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Max has a AA Agentic Index result, 22.5.
Directional only
Reasoning
GPQA Diamond
GPT-5.2
GPT-5.2 leads, 91.7% against 90.2%.
Same board, both measured
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 86.2%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1195.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Max
GPT-5.2 $0.0088, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.2
GPT-5.2 $1.91, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.2 55.9 from 3 benchmarks, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Agentic work, 11.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- SimpleBench: GPT-5.2 45.8% against Qwen3.7 Max 70.4%, 24.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Qwen3.7 Max $4.425 per million output tokens, 3.2x apart.
- Context window
- GPT-5.2 takes 400K 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-5.2 | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 3 | 4 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 0 | 6 | ||
| Reasoning | 4 | 4 | ||
| Math | 2 | 2 | ||
| Knowledge | 3 | 3 | ||
| Web design | 0 | 8 | ||
| General | 2 | 3 | ||
| All shared boards | 15 | 31 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index34
| Benchmark | GPT-5.2 | Qwen3.7 Max | 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 |
| LMArena WebDev | 1417 | 1515 | Qwen3.7 Max |
| AA Coding Index | Not measured | 66.0 | Only Qwen3.7 Max measured |
| LiveCodeBench (Vals AI) | 85.4% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | 75.8% | 68.8% | GPT-5.2 |
| Vibe Code Bench (Vals AI) | 53.5% | 47.7% | GPT-5.2 |
| Code migration (Vals AI) | Not measured | 13.1% | Only Qwen3.7 Max measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 61.0% | Only Qwen3.7 Max measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | 50.8% | Qwen3.7 Max |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 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 | 1189.4 | GPT-5.2 |
| LMArena Text: coding | 1442 | Not measured | Only GPT-5.2 measured |
| LMArena Text: software and IT | 1434 | Not measured | Only GPT-5.2 measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | 1427 | 1512 | Qwen3.7 Max |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 29.5 | GPT-5.2 |
| Epoch Capabilities Index | 153.7 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Qwen3.7 Max measured |
| Design Arena: agents building web apps | 1086 | 1208 | Qwen3.7 Max |
| Design Arena: agents building full-stack apps | 1043 | 1191 | Qwen3.7 Max |
| Design Arena: agents building mobile apps | 1093 | 1155 | Qwen3.7 Max |
| Design Arena: agents building Android apps | 1058 | 1159 | Qwen3.7 Max |
| Design Arena: agents building Godot games | 1142 | 1187 | Qwen3.7 Max |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1158 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1197 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | Not measured | 47.8% | Only Qwen3.7 Max measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.8% | Only Qwen3.7 Max measured |
| Legal Research Bench (Vals AI) | Not measured | 25.5% | Only Qwen3.7 Max measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 57.0% | Only Qwen3.7 Max measured |
| τ²-Bench Telecom | 84.8% | 94.7% | Qwen3.7 Max |
| 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 |
| Berkeley Function Calling Leaderboard (BFCL) | 55.9% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | GPT-5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 90.2% | GPT-5.2 |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 91.4% | 90.9% | GPT-5.2 |
| 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% | 70.4% | Qwen3.7 Max |
| Chess puzzles | 48.0% | 26.0% | GPT-5.2 |
| Mystery game puzzles | 23.0% | 32.0% | Qwen3.7 Max |
| EBR-bench | 23.0% | 9.5% | GPT-5.2 |
| VPCT | 84.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1428 | Not measured | Only GPT-5.2 measured |
| LMArena Text: expert | 1445 | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts in English | 1431 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: diagrams | 1265 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: homework | 1276 | Not measured | Only GPT-5.2 measured |
| Kagi LLM Benchmark | 73.3% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | GPT-5.2 | Qwen3.7 Max | 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% | 95.6% | GPT-5.2 |
| FrontierMath, tiers 1 to 3 | 67.4% | 64.6% | GPT-5.2 |
| FrontierMath, tier 4 | 31.7% | 34.1% | Qwen3.7 Max |
| ProofBench | 15.0% | 26.0% | Qwen3.7 Max |
| LMArena Text: math | 1440 | Not measured | Only GPT-5.2 measured |
| LMArena Text: mathematical work | 1442 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | GPT-5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 82.8% | 84.9% | Qwen3.7 Max |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | 63.7% | GPT-5.2 |
| TaxEval (Vals AI) | 75.8% | 75.3% | GPT-5.2 |
| MortgageTax (Vals AI) | 67.1% | Not measured | Only GPT-5.2 measured |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | 38.8% | GPT-5.2 |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| SimpleQA Verified | 37.1% | 55.8% | Qwen3.7 Max |
| LMArena Text: science | 1425 | Not measured | Only GPT-5.2 measured |
| LMArena Text: medicine and healthcare | 1425 | Not measured | Only GPT-5.2 measured |
| LMArena Text: legal and government | 1427 | Not measured | Only GPT-5.2 measured |
| LMArena Text: business and finance | 1414 | Not measured | Only GPT-5.2 measured |
| 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 index08
| Benchmark | GPT-5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1195 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1190 | 1270 | Qwen3.7 Max |
| Design Arena: game dev | 1195 | 1265 | Qwen3.7 Max |
| Design Arena: data viz | 1202 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 1079 | 1266 | Qwen3.7 Max |
| Design Arena: all code categories | 1170 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | 1139 | 1217 | Qwen3.7 Max |
| Design Arena: ASCII art | 1152 | 1211 | Qwen3.7 Max |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | GPT-5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 49.3% | Not measured | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 84.4% | 79.4% | GPT-5.2 |
| IFBench | 75.4% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 82.7% | 79.0% | GPT-5.2 |
| DTBench | 90.9% | 92.3% | Qwen3.7 Max |
| LMCA | 43.9% | 44.0% | Qwen3.7 Max |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | Not measured | Only GPT-5.2 measured |
| LMArena Text: instruction following | 1409 | Not measured | Only GPT-5.2 measured |
| LMArena Text: creative writing | 1376 | Not measured | Only GPT-5.2 measured |
| LMArena Vision | 1246 | Not measured | Only GPT-5.2 measured |
| LMArena Text: English | 1422 | Not measured | Only GPT-5.2 measured |
| LMArena Text: non-English | 1405 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Chinese | 1447 | Not measured | Only GPT-5.2 measured |
| LMArena Text: German | 1444 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Japanese | 1420 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Korean | 1374 | Not measured | Only GPT-5.2 measured |
| LMArena Text: French | 1454 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Spanish | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Russian | 1415 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Polish | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: multi-turn | 1419 | Not measured | Only GPT-5.2 measured |
| LMArena Text: longer queries | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: writing and language | 1392 | Not measured | Only GPT-5.2 measured |
| LMArena Text: entertainment and media | 1378 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: English | 1243 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: Chinese | 1305 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: reading text | 1257 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: humor | 1227 | Not measured | Only GPT-5.2 measured |
| 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 | Not measured | Only GPT-5.2 measured |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | 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-5.2 | Qwen3.7 Max |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | December 10, 2025 | May 21, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.75 | $1.475 |
| Output $/Mtok | $14.00 | $4.425 |
| Coding index | 55.9 | 54.1 |
| Independent measurements behind it | 3 | 3 |
| Cost per solved task | $1.91 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Qwen3.7 Max for coding?
GPT-5.2 and Qwen3.7 Max are level for coding, 55.9 against 54.1 on the Vibe Coding Index, so price and context decide. Pick Qwen3.7 Max for output tokens 3.2x cheaper ($4.425 vs $14.00 per million) and a larger context window (1M against 400K tokens). Pick GPT-5.2 for a lower cost per solved task ($1.91 against $4.69) and work measured by SWE-bench Verified (Vals AI) or Vibe Code Bench (Vals AI), where it leads.
Which is cheaper, GPT-5.2 or Qwen3.7 Max?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GPT-5.2 costs $1.91 and Qwen3.7 Max $4.69.
Which has the bigger context window, GPT-5.2 or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 400K.
How many benchmarks is the GPT-5.2 vs Qwen3.7 Max comparison based on?
46 benchmarks have a result for both models as of October 21, 2026. GPT-5.2's coding index rests on 3 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.