GPT-5 Nano vs Qwen3 32B
GPT-5 Nano leads Qwen3 32B on the Artificial Analysis Intelligence Index, 13.0 to 7.3, at about the same output price ($0.40 vs $0.40 per million). Updated October 22, 2026; 42 benchmarks have a result for both.
GPT-5 Nano or Qwen3 32B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5 Nano for a larger context window (400K against 131K tokens) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 42
- GPT-5 Nano only
- 45
- Qwen3 32B only
- 3
- Like-for-like categories
- 7 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-5 Nano
GPT-5 Nano leads, 13.0 against 7.3.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5 Nano has a GPQA Diamond result, 63.4%.
Directional only
Math
AIME
No call
Only GPT-5 Nano has a AIME result, 81.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 Nano has a MMLU Pro result, 76.1%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5 Nano has a Design Arena: websites result, 1101.
Directional only
Long documents
Listed context window
GPT-5 Nano
GPT-5 Nano takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
GPT-5 Nano $0.0003, Qwen3 32B $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 Nano has the number, $0.109.
Directional only
At a glance
- Coding index
- GPT-5 Nano not benchmarked, Qwen3 32B not benchmarked.
- Widest category
- Math, 12.9 points apart on average over 5 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5 Nano 45.0% against Qwen3 32B 0.0%, 45.0 points apart on a 0 to 100 scale.
- Output price
- Both cost $0.40 per million output tokens.
- Context window
- GPT-5 Nano takes 400K tokens, Qwen3 32B 131K.
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 32B | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 4 | 3 | ||
| Math | 3 | 2 | ||
| Knowledge | 0 | 5 | ||
| General | 3 | 14 | ||
| All shared boards | 16 | 26 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | GPT-5 Nano | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 40.0% | Only Qwen3 32B measured |
| SWE-bench Verified | 34.8% | Not measured | Only GPT-5 Nano measured |
| LiveCodeBench (Vals AI) | 70.2% | Not measured | Only GPT-5 Nano measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | 54.6% | GPT-5 Nano |
| SciCode | Not measured | 36.0% | Only Qwen3 32B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | 3.0% | GPT-5 Nano |
| Terminal-Bench | 21.8% | Not measured | Only GPT-5 Nano measured |
| WeirdML | 38.1% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: coding | 1351 | 1357 | Qwen3 32B |
| LMArena Text: software and IT | 1345 | 1366 | Qwen3 32B |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 Nano | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | 7.3 | GPT-5 Nano |
| Epoch Capabilities Index | 139.2 | 138.4 | GPT-5 Nano |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 Nano | Qwen3 32B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 36.5% | 29.8% | GPT-5 Nano |
| Berkeley Function Calling Leaderboard (BFCL) | 51.4% | 48.7% | GPT-5 Nano |
| BrowseComp-Plus | Not measured | 10.7% | Only Qwen3 32B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index43
| Benchmark | GPT-5 Nano | Qwen3 32B | Leader |
|---|---|---|---|
| GPQA Diamond | 63.4% | Not measured | Only GPT-5 Nano measured |
| MMMU Pro (Vals AI) | 70.9% | Not measured | Only GPT-5 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 67.6% | 66.8% | GPT-5 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | 7.4% | GPT-5 Nano |
| GPQA Diamond (Epoch AI run) | 69.4% | 65.7% | GPT-5 Nano |
| ARC-AGI | 20.7% | Not measured | Only GPT-5 Nano measured |
| ARC-AGI-2 | 2.6% | Not measured | Only GPT-5 Nano measured |
| Chess puzzles | 22.0% | Not measured | Only GPT-5 Nano measured |
| Mystery game puzzles | 9.0% | Not measured | Only GPT-5 Nano measured |
| VPCT | 37.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1328 | 1334 | Qwen3 32B |
| LMArena Text: expert | 1321 | 1362 | Qwen3 32B |
| LMArena Text: hard prompts in English | 1338 | 1350 | Qwen3 32B |
| 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% | 48.7% | GPT-5 Nano |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GPT-5 Nano | Qwen3 32B | 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 | 73.0 | GPT-5 Nano |
| AIME 2025 (Artificial Analysis run) | 83.7% | 73.0% | GPT-5 Nano |
| OTIS Mock AIME | 81.1% | 66.9% | GPT-5 Nano |
| FrontierMath, tiers 1 to 3 | 20.0% | Not measured | Only GPT-5 Nano measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only GPT-5 Nano measured |
| ProofBench | 12.0% | Not measured | Only GPT-5 Nano measured |
| MATH level 5 | 95.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: math | 1317 | 1399 | Qwen3 32B |
| LMArena Text: mathematical work | 1326 | 1420 | Qwen3 32B |
| 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 32B | Leader |
|---|---|---|---|
| MMLU Pro | 76.1% | Not measured | Only GPT-5 Nano measured |
| LegalBench (Vals AI) | 50.1% | Not measured | Only GPT-5 Nano measured |
| CaseLaw (Vals AI) | 52.6% | Not measured | Only GPT-5 Nano measured |
| TaxEval (Vals AI) | 67.4% | Not measured | Only GPT-5 Nano measured |
| 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% | Not measured | Only GPT-5 Nano measured |
| MMLU Pro (Artificial Analysis run) | 78.0% | 79.8% | Qwen3 32B |
| SimpleQA Verified | 11.7% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: science | 1318 | 1372 | Qwen3 32B |
| LMArena Text: medicine and healthcare | 1335 | 1368 | Qwen3 32B |
| LMArena Text: legal and government | 1298 | 1324 | Qwen3 32B |
| LMArena Text: business and finance | 1311 | 1318 | Qwen3 32B |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5 Nano | Qwen3 32B | Leader |
|---|---|---|---|
| Design Arena: websites | 1101 | Not measured | Only GPT-5 Nano measured |
| Design Arena: UI components | 1064 | Not measured | Only GPT-5 Nano measured |
| Design Arena: game dev | 1044 | Not measured | Only GPT-5 Nano measured |
| Design Arena: data viz | 1056 | Not measured | Only GPT-5 Nano measured |
| Design Arena: 3D | 965 | Not measured | Only GPT-5 Nano measured |
| Design Arena: all code categories | 1084 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index314
| Benchmark | GPT-5 Nano | Qwen3 32B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 30.4% | Not measured | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | 72.9% | Not measured | Only GPT-5 Nano measured |
| IFBench | 67.6% | 36.3% | GPT-5 Nano |
| Long context reasoning (AA-LCR) | 45.0% | 0.0% | GPT-5 Nano |
| DTBench | 62.7% | 67.5% | Qwen3 32B |
| LMCA | 7.9% | 17.3% | Qwen3 32B |
| Fiction.LiveBench | 44.4% | 74.2% | Qwen3 32B |
| LMArena Text | 1320 | 1340 | Qwen3 32B |
| LMArena Text: instruction following | 1306 | 1305 | GPT-5 Nano |
| LMArena Text: creative writing | 1249 | 1297 | Qwen3 32B |
| LMArena Vision | 1159 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: English | 1321 | 1359 | Qwen3 32B |
| LMArena Text: non-English | 1313 | 1317 | Qwen3 32B |
| LMArena Text: Chinese | 1356 | 1357 | Qwen3 32B |
| LMArena Text: German | 1327 | 1341 | Qwen3 32B |
| 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 | 1311 | Qwen3 32B |
| LMArena Text: Polish | 1314 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: multi-turn | 1311 | 1331 | Qwen3 32B |
| LMArena Text: longer queries | 1312 | 1327 | Qwen3 32B |
| LMArena Text: writing and language | 1283 | 1298 | Qwen3 32B |
| LMArena Text: entertainment and media | 1268 | 1279 | Qwen3 32B |
| 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 32B | 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 32B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | April 28, 2025 |
| Context window | 400K | 131K |
| Input $/Mtok | $0.05 | $0.14 |
| Output $/Mtok | $0.40 | $0.40 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $0.109 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Nano better than Qwen3 32B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5 Nano for a larger context window (400K against 131K tokens) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-5 Nano or Qwen3 32B?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Qwen3 32B costs $0.14 and $0.40. Cost per solved task is known for GPT-5 Nano only.
Which has the bigger context window, GPT-5 Nano or Qwen3 32B?
GPT-5 Nano, with 400K tokens against 131K.
How many benchmarks is the GPT-5 Nano vs Qwen3 32B comparison based on?
42 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 32B's on 0. Where only one model has a result, the page says so instead of naming a winner.