GPT-5.2 vs Qwen3.7 Plus
GPT-5.2 leads Qwen3.7 Plus on coding, 55.9 to 33.2 on the Vibe Coding Index, and Qwen3.7 Plus costs 10.9x less per output token ($1.28 vs $14.00 per million). Updated October 21, 2026; 62 benchmarks have a result for both.
GPT-5.2 or Qwen3.7 Plus: which to pick
Pick GPT-5.2 for coding: it scores 55.9 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus.
Pick Qwen3.7 Plus for output tokens 10.9x cheaper ($1.28 vs $14.00 per million) and a larger context window (1M against 400K tokens).
- Shared results
- 62
- GPT-5.2 only
- 52
- Qwen3.7 Plus 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
GPT-5.2
GPT-5.2 scores 55.9 against 33.2.
3 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 against 25.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Plus has a AA Agentic Index result, 17.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.2 has a GPQA Diamond result, 91.7%.
Directional only
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.2 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
Qwen3.7 Plus
Qwen3.7 Plus leads, 1269 against 1195.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Plus
Qwen3.7 Plus takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Plus
GPT-5.2 $0.0088, Qwen3.7 Plus $0.0010 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- GPT-5.2 55.9 from 3 benchmarks, Qwen3.7 Plus 33.2 from 2 benchmarks.
- Widest category
- Math, 11.2 points apart on average over 4 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: GPT-5.2 67.4% against Qwen3.7 Plus 34.4%, 33.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Qwen3.7 Plus $1.28 per million output tokens, 10.9x apart.
- Context window
- GPT-5.2 takes 400K tokens, Qwen3.7 Plus 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 Plus | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | 1 tie | |
| Intelligence | 2 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 4 | 5 | ||
| Math | 2 | 2 | ||
| Knowledge | 1 | 4 | ||
| Web design | 1 | 7 | ||
| General | 5 | 24 | ||
| All shared boards | 16 | 45 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | GPT-5.2 | Qwen3.7 Plus | 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 | Not measured | Only GPT-5.2 measured |
| AA Coding Index | Not measured | 55.9 | Only Qwen3.7 Plus measured |
| LiveCodeBench (Vals AI) | 85.4% | Not measured | Only GPT-5.2 measured |
| SWE-bench Verified (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| Vibe Code Bench (Vals AI) | 53.5% | 46.4% | GPT-5.2 |
| Code migration (Vals AI) | Not measured | 12.9% | Only Qwen3.7 Plus measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 52.8% | Only Qwen3.7 Plus measured |
| SkillsBench (Vals AI) | Not measured | 54.3% | Only Qwen3.7 Plus measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| SciCode | Not measured | 46.1% | Only Qwen3.7 Plus measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | 47.0% | Tie |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 measured |
| FrontierCode | Not measured | 10.2% | Only Qwen3.7 Plus 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 | 1473 | Qwen3.7 Plus |
| LMArena Text: software and IT | 1434 | 1473 | Qwen3.7 Plus |
| LMArena WebDev: HTML | 1427 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 25.2 | GPT-5.2 |
| Epoch Capabilities Index | 153.7 | 147.0 | GPT-5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 17.5 | Only Qwen3.7 Plus measured |
| Design Arena: agents building web apps | 1086 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | 1043 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | 1093 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1058 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| Finance Agent (Vals AI) | Not measured | 38.2% | Only Qwen3.7 Plus measured |
| Tax Agent Bench (Vals AI) | Not measured | 10.5% | Only Qwen3.7 Plus measured |
| Legal Research Bench (Vals AI) | Not measured | 16.4% | Only Qwen3.7 Plus measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 49.3% | Only Qwen3.7 Plus measured |
| τ²-Bench Telecom | 84.8% | 93.0% | Qwen3.7 Plus |
| 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 |
| OSWorld 2.0 | Not measured | 2.8% | Only Qwen3.7 Plus 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 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index45
| Benchmark | GPT-5.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.2 measured |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | 90.0% | GPT-5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | 35.6% | GPT-5.2 |
| GPQA Diamond (Epoch AI run) | 91.4% | 87.9% | 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% | Not measured | Only GPT-5.2 measured |
| Chess puzzles | 48.0% | Not measured | Only GPT-5.2 measured |
| Mystery game puzzles | 23.0% | 17.0% | GPT-5.2 |
| 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 | 1460 | Qwen3.7 Plus |
| LMArena Text: expert | 1445 | 1466 | Qwen3.7 Plus |
| LMArena Text: hard prompts in English | 1431 | 1460 | Qwen3.7 Plus |
| LMArena Vision: diagrams | 1265 | 1292 | Qwen3.7 Plus |
| LMArena Vision: homework | 1276 | 1295 | Qwen3.7 Plus |
| 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 Plus | 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% | 93.3% | GPT-5.2 |
| FrontierMath, tiers 1 to 3 | 67.4% | 34.4% | GPT-5.2 |
| FrontierMath, tier 4 | 31.7% | Not measured | Only GPT-5.2 measured |
| ProofBench | 15.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: math | 1440 | 1467 | Qwen3.7 Plus |
| LMArena Text: mathematical work | 1442 | 1470 | Qwen3.7 Plus |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | GPT-5.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | Not measured | Only GPT-5.2 measured |
| LegalBench (Vals AI) | 82.8% | Not measured | Only GPT-5.2 measured |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | Not measured | Only GPT-5.2 measured |
| TaxEval (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| MortgageTax (Vals AI) | 67.1% | 66.2% | GPT-5.2 |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | Not measured | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| SimpleQA Verified | 37.1% | Not measured | Only GPT-5.2 measured |
| LMArena Text: science | 1425 | 1464 | Qwen3.7 Plus |
| LMArena Text: medicine and healthcare | 1425 | 1464 | Qwen3.7 Plus |
| LMArena Text: legal and government | 1427 | 1451 | Qwen3.7 Plus |
| LMArena Text: business and finance | 1414 | 1435 | Qwen3.7 Plus |
| 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 index17
| Benchmark | GPT-5.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1195 | 1269 | Qwen3.7 Plus |
| Design Arena: UI components | 1190 | 1261 | Qwen3.7 Plus |
| Design Arena: game dev | 1195 | 1262 | Qwen3.7 Plus |
| Design Arena: data viz | 1202 | 1276 | Qwen3.7 Plus |
| Design Arena: 3D | 1079 | 1239 | Qwen3.7 Plus |
| Design Arena: all code categories | 1170 | 1269 | Qwen3.7 Plus |
| Design Arena: SVG | 1139 | 1208 | Qwen3.7 Plus |
| Design Arena: ASCII art | 1152 | 1143 | GPT-5.2 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index524
| Benchmark | GPT-5.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 49.3% | 39.3% | GPT-5.2 |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.2 measured |
| Vals Multimodal Index | Not measured | 53.9% | Only Qwen3.7 Plus measured |
| IFBench | 75.4% | 78.0% | Qwen3.7 Plus |
| Long context reasoning (AA-LCR) | 82.7% | 73.0% | GPT-5.2 |
| DTBench | 90.9% | 84.0% | GPT-5.2 |
| LMCA | 43.9% | 37.6% | GPT-5.2 |
| 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 | 1454 | Qwen3.7 Plus |
| LMArena Text: instruction following | 1409 | 1439 | Qwen3.7 Plus |
| LMArena Text: creative writing | 1376 | 1438 | Qwen3.7 Plus |
| LMArena Vision | 1246 | 1278 | Qwen3.7 Plus |
| LMArena Text: English | 1422 | 1458 | Qwen3.7 Plus |
| LMArena Text: non-English | 1405 | 1445 | Qwen3.7 Plus |
| LMArena Text: Chinese | 1447 | 1509 | Qwen3.7 Plus |
| LMArena Text: German | 1444 | 1471 | Qwen3.7 Plus |
| LMArena Text: Japanese | 1420 | 1412 | GPT-5.2 |
| LMArena Text: Korean | 1374 | 1413 | Qwen3.7 Plus |
| LMArena Text: French | 1454 | 1472 | Qwen3.7 Plus |
| LMArena Text: Spanish | 1413 | 1457 | Qwen3.7 Plus |
| LMArena Text: Russian | 1415 | 1457 | Qwen3.7 Plus |
| LMArena Text: Polish | 1426 | 1449 | Qwen3.7 Plus |
| LMArena Text: multi-turn | 1419 | 1459 | Qwen3.7 Plus |
| LMArena Text: longer queries | 1413 | 1454 | Qwen3.7 Plus |
| LMArena Text: writing and language | 1392 | 1437 | Qwen3.7 Plus |
| LMArena Text: entertainment and media | 1378 | 1422 | Qwen3.7 Plus |
| LMArena Vision: English | 1243 | 1280 | Qwen3.7 Plus |
| LMArena Vision: Chinese | 1305 | 1332 | Qwen3.7 Plus |
| LMArena Vision: reading text | 1257 | 1288 | Qwen3.7 Plus |
| LMArena Vision: humor | 1227 | 1261 | Qwen3.7 Plus |
| 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 | 1258 | Qwen3.7 Plus |
| LMArena Document | 1405 | 1444 | Qwen3.7 Plus |
| 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-5.2 | Qwen3.7 Plus | 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 Plus costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Plus costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 Plus 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 Plus |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | December 10, 2025 | June 3, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.75 | $0.32 |
| Output $/Mtok | $14.00 | $1.28 |
| Coding index | 55.9 | 33.2 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Qwen3.7 Plus for coding?
Pick GPT-5.2 for coding: it scores 55.9 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus. Pick Qwen3.7 Plus for output tokens 10.9x cheaper ($1.28 vs $14.00 per million) and a larger context window (1M against 400K tokens).
Which is cheaper, GPT-5.2 or Qwen3.7 Plus?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 or Qwen3.7 Plus?
Qwen3.7 Plus, with 1M tokens against 400K.
How many benchmarks is the GPT-5.2 vs Qwen3.7 Plus comparison based on?
62 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 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.