GPT-5 Nano vs Qwen3.7 Max
Qwen3.7 Max leads GPT-5 Nano on the Artificial Analysis Intelligence Index, 29.5 to 13.0, and GPT-5 Nano costs 11.1x less per output token ($0.40 vs $4.425 per million). Updated October 22, 2026; 31 benchmarks have a result for both.
GPT-5 Nano or Qwen3.7 Max: which to pick
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding.
Pick GPT-5 Nano for output tokens 11.1x cheaper ($0.40 vs $4.425 per million) and a lower cost per solved task ($0.109 against $4.69).
- Shared results
- 31
- GPT-5 Nano only
- 56
- Qwen3.7 Max only
- 28
- 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
Only Qwen3.7 Max has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 13.0.
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
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 63.4%.
Same board, both measured
Math
AIME
No call
Only GPT-5 Nano has a AIME result, 81.2%.
Directional only
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 76.1%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1101.
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
GPT-5 Nano
GPT-5 Nano $0.0003, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Nano
GPT-5 Nano $0.109, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 Nano not benchmarked, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Agentic work, 58.2 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-5 Nano 36.5% against Qwen3.7 Max 94.7%, 58.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Nano $0.40 against Qwen3.7 Max $4.425 per million output tokens, 11.1x apart.
- Context window
- GPT-5 Nano 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 Nano | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| Web design | 0 | 6 | ||
| General | 0 | 5 | ||
| All shared boards | 0 | 31 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-5 Nano | Qwen3.7 Max | Leader |
|---|---|---|---|
| SWE-bench Verified | 34.8% | Not measured | Only GPT-5 Nano measured |
| LMArena WebDev | Not measured | 1515 | Only Qwen3.7 Max measured |
| AA Coding Index | Not measured | 66.0 | Only Qwen3.7 Max measured |
| LiveCodeBench (Vals AI) | 70.2% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | Not measured | 68.8% | Only Qwen3.7 Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 47.7% | Only Qwen3.7 Max measured |
| 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) | 78.9% | Not measured | Only GPT-5 Nano measured |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | 50.8% | Qwen3.7 Max |
| Terminal-Bench | 21.8% | Not measured | Only GPT-5 Nano measured |
| WeirdML | 38.1% | Not measured | Only GPT-5 Nano measured |
| ALE-Bench | Not measured | 1189.4 | Only Qwen3.7 Max measured |
| LMArena Text: coding | 1351 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: software and IT | 1345 | Not measured | Only GPT-5 Nano measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | Not measured | 1512 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5 Nano | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | 139.2 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Nano | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Qwen3.7 Max measured |
| Design Arena: agents building web apps | Not measured | 1208 | Only Qwen3.7 Max measured |
| Design Arena: agents building full-stack apps | Not measured | 1191 | Only Qwen3.7 Max measured |
| Design Arena: agents building mobile apps | Not measured | 1155 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1159 | Only Qwen3.7 Max measured |
| Design Arena: agents building Godot games | Not measured | 1187 | Only Qwen3.7 Max measured |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1108 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1171 | 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 | 36.5% | 94.7% | Qwen3.7 Max |
| Berkeley Function Calling Leaderboard (BFCL) | 51.4% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-5 Nano | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 63.4% | 90.2% | Qwen3.7 Max |
| MMMU Pro (Vals AI) | 70.9% | Not measured | Only GPT-5 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 67.6% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 69.4% | 90.9% | Qwen3.7 Max |
| ARC-AGI | 20.7% | Not measured | Only GPT-5 Nano measured |
| ARC-AGI-2 | 2.6% | Not measured | Only GPT-5 Nano measured |
| SimpleBench | Not measured | 70.4% | Only Qwen3.7 Max measured |
| Chess puzzles | 22.0% | 26.0% | Qwen3.7 Max |
| Mystery game puzzles | 9.0% | 32.0% | Qwen3.7 Max |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| VPCT | 37.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1328 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: expert | 1321 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts in English | 1338 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: diagrams | 1141 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: homework | 1157 | Not measured | Only GPT-5 Nano measured |
| Kagi LLM Benchmark | 62.2% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-5 Nano | Qwen3.7 Max | Leader |
|---|---|---|---|
| AIME | 81.2% | Not measured | Only GPT-5 Nano measured |
| MATH 500 (Vals AI) | 93.8% | Not measured | Only GPT-5 Nano measured |
| MGSM (Vals AI) | 89.3% | Not measured | Only GPT-5 Nano measured |
| AA Math Index | 83.7 | Not measured | Only GPT-5 Nano measured |
| AIME 2025 (Artificial Analysis run) | 83.7% | Not measured | Only GPT-5 Nano measured |
| OTIS Mock AIME | 81.1% | 95.6% | Qwen3.7 Max |
| FrontierMath, tiers 1 to 3 | 20.0% | 64.6% | Qwen3.7 Max |
| FrontierMath, tier 4 | 2.4% | 34.1% | Qwen3.7 Max |
| ProofBench | 12.0% | 26.0% | Qwen3.7 Max |
| MATH level 5 | 95.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: math | 1317 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: mathematical work | 1326 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-5 Nano | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 76.1% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 50.1% | 84.9% | Qwen3.7 Max |
| CaseLaw (Vals AI) | 52.6% | Not measured | Only GPT-5 Nano measured |
| CorpFin (Vals AI) | Not measured | 63.7% | Only Qwen3.7 Max measured |
| TaxEval (Vals AI) | 67.4% | 75.3% | Qwen3.7 Max |
| MortgageTax (Vals AI) | 53.6% | Not measured | Only GPT-5 Nano measured |
| MedQA (Vals AI) | 93.3% | Not measured | Only GPT-5 Nano measured |
| MedCode (Vals AI) | 30.4% | 38.8% | Qwen3.7 Max |
| MMLU Pro (Artificial Analysis run) | 78.0% | Not measured | Only GPT-5 Nano measured |
| SimpleQA Verified | 11.7% | 55.8% | Qwen3.7 Max |
| LMArena Text: science | 1318 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: medicine and healthcare | 1335 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: legal and government | 1298 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: business and finance | 1311 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-5 Nano | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1101 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1064 | 1267 | Qwen3.7 Max |
| Design Arena: game dev | 1044 | 1264 | Qwen3.7 Max |
| Design Arena: data viz | 1056 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 965 | 1265 | Qwen3.7 Max |
| Design Arena: all code categories | 1084 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | Not measured | 1216 | Only Qwen3.7 Max measured |
| Design Arena: ASCII art | Not measured | 1211 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-5 Nano | Qwen3.7 Max | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 30.4% | Not measured | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | 72.9% | 79.4% | Qwen3.7 Max |
| IFBench | 67.6% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 45.0% | 79.0% | Qwen3.7 Max |
| DTBench | 62.7% | 92.3% | Qwen3.7 Max |
| LMCA | 7.9% | 44.0% | Qwen3.7 Max |
| Fiction.LiveBench | 44.4% | Not measured | Only GPT-5 Nano measured |
| LMArena Text | 1320 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: instruction following | 1306 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: creative writing | 1249 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision | 1159 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: English | 1321 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: non-English | 1313 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Chinese | 1356 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: German | 1327 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Japanese | 1227 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Korean | 1269 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Spanish | 1360 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Russian | 1296 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Polish | 1314 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: multi-turn | 1311 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: longer queries | 1312 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: writing and language | 1283 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: entertainment and media | 1268 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: English | 1170 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: Chinese | 1176 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: reading text | 1152 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: humor | 1110 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Nano | 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
GPT-5 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5 Nano 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 Nano | Qwen3.7 Max |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | August 7, 2025 | May 21, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $0.05 | $1.475 |
| Output $/Mtok | $0.40 | $4.425 |
| Coding index | Not benchmarked | 54.1 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | $0.109 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Nano better than Qwen3.7 Max for coding?
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 11.1x cheaper ($0.40 vs $4.425 per million) and a lower cost per solved task ($0.109 against $4.69).
Which is cheaper, GPT-5 Nano or Qwen3.7 Max?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GPT-5 Nano costs $0.109 and Qwen3.7 Max $4.69.
Which has the bigger context window, GPT-5 Nano or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 400K.
How many benchmarks is the GPT-5 Nano vs Qwen3.7 Max comparison based on?
31 benchmarks have a result for both models as of October 22, 2026. GPT-5 Nano's coding index rests on 0 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.