GPT-5 Nano vs Qwen3 VL 235B A22B Instruct
GPT-5 Nano leads Qwen3 VL 235B A22B Instruct on the Artificial Analysis Intelligence Index, 13.0 to 9.9, and GPT-5 Nano costs 4.7x less per output token ($0.40 vs $1.90 per million). Updated October 22, 2026; 45 benchmarks have a result for both.
GPT-5 Nano or Qwen3 VL 235B A22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5 Nano for output tokens 4.7x cheaper ($0.40 vs $1.90 per million) and a larger context window (400K against 262K tokens).
- Shared results
- 45
- GPT-5 Nano only
- 43
- Qwen3 VL 235B A22B Instruct only
- 4
- 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 9.9.
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
GPT-5 Nano $0.0003, Qwen3 VL 235B A22B Instruct $0.0012 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 VL 235B A22B Instruct not benchmarked.
- Widest category
- Agentic work, 17.5 points apart on average over 1 shared benchmark.
- Widest single gap
- LMArena Text: Japanese: GPT-5 Nano 1227 against Qwen3 VL 235B A22B Instruct 1385, 26.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Nano $0.40 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 4.7x apart.
- Context window
- GPT-5 Nano takes 400K tokens, Qwen3 VL 235B A22B Instruct 262K.
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 VL 235B A22B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 1 | 22 | ||
| All shared boards | 4 | 41 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | GPT-5 Nano | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| 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% | 64.6% | GPT-5 Nano |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | 11.4% | 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 | 1438 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: software and IT | 1345 | 1442 | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 Nano | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | 9.9 | GPT-5 Nano |
| Epoch Capabilities Index | 139.2 | Not measured | Only GPT-5 Nano measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Nano | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 36.5% | 54.1% | Qwen3 VL 235B A22B Instruct |
| Berkeley Function Calling Leaderboard (BFCL) | 51.4% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GPT-5 Nano | Qwen3 VL 235B A22B Instruct | 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% | 77.2% | Qwen3 VL 235B A22B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | 11.9% | Qwen3 VL 235B A22B Instruct |
| GPQA Diamond (Epoch AI run) | 69.4% | Not measured | Only GPT-5 Nano measured |
| 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 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: expert | 1321 | 1442 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: hard prompts in English | 1338 | 1439 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: diagrams | 1141 | 1255 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: homework | 1157 | 1277 | Qwen3 VL 235B A22B Instruct |
| Kagi LLM Benchmark | 62.2% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-5 Nano | Qwen3 VL 235B A22B Instruct | 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 | 88.3 | Qwen3 VL 235B A22B Instruct |
| AIME 2025 (Artificial Analysis run) | 83.7% | 88.3% | Qwen3 VL 235B A22B Instruct |
| OTIS Mock AIME | 81.1% | Not measured | Only GPT-5 Nano measured |
| 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 | 1425 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: mathematical work | 1326 | 1419 | Qwen3 VL 235B A22B Instruct |
| MathArena | 12.8% | 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 VL 235B A22B Instruct | 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% | 83.6% | Qwen3 VL 235B A22B Instruct |
| SimpleQA Verified | 11.7% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: science | 1318 | 1444 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: medicine and healthcare | 1335 | 1431 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: legal and government | 1298 | 1438 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: business and finance | 1311 | 1433 | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5 Nano | Qwen3 VL 235B A22B Instruct | 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 index122
| Benchmark | GPT-5 Nano | Qwen3 VL 235B A22B Instruct | 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% | 56.5% | GPT-5 Nano |
| Long context reasoning (AA-LCR) | 45.0% | 63.7% | Qwen3 VL 235B A22B Instruct |
| DTBench | 62.7% | Not measured | Only GPT-5 Nano measured |
| LMCA | 7.9% | Not measured | Only GPT-5 Nano measured |
| Fiction.LiveBench | 44.4% | Not measured | Only GPT-5 Nano measured |
| LMArena Text | 1320 | 1420 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: instruction following | 1306 | 1406 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: creative writing | 1249 | 1366 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision | 1159 | 1247 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: English | 1321 | 1429 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: non-English | 1313 | 1405 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Chinese | 1356 | 1463 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: German | 1327 | 1424 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Japanese | 1227 | 1385 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Korean | 1269 | 1394 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: French | Not measured | 1452 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: Spanish | 1360 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Russian | 1296 | 1408 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Polish | 1314 | 1419 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: multi-turn | 1311 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: longer queries | 1312 | 1419 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: writing and language | 1283 | 1392 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: entertainment and media | 1268 | 1368 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: English | 1170 | 1256 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: Chinese | 1176 | 1283 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: reading text | 1152 | 1253 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: humor | 1110 | 1209 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: captioning | Not measured | 1281 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: recognizing things | Not measured | 1249 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3 VL 235B A22B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Nano | Qwen3 VL 235B A22B Instruct | 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 VL 235B A22B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | September 23, 2025 |
| Context window | 400K | 262K |
| Input $/Mtok | $0.05 | $0.21 |
| Output $/Mtok | $0.40 | $1.90 |
| 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 VL 235B A22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 4.7x cheaper ($0.40 vs $1.90 per million) and a larger context window (400K against 262K tokens).
Which is cheaper, GPT-5 Nano or Qwen3 VL 235B A22B Instruct?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for GPT-5 Nano only.
Which has the bigger context window, GPT-5 Nano or Qwen3 VL 235B A22B Instruct?
GPT-5 Nano, with 400K tokens against 262K.
How many benchmarks is the GPT-5 Nano vs Qwen3 VL 235B A22B Instruct comparison based on?
45 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 VL 235B A22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.