GPT-5 Nano vs Qwen3 30B A3B
GPT-5 Nano leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 13.0 to 7.6, and GPT-5 Nano costs 1.3x less per output token ($0.40 vs $0.50 per million). Updated October 22, 2026; 50 benchmarks have a result for both.
GPT-5 Nano or Qwen3 30B A3B: 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 1.3x cheaper ($0.40 vs $0.50 per million) and a larger context window (400K against 41K tokens).
- Shared results
- 50
- GPT-5 Nano only
- 37
- Qwen3 30B A3B only
- 2
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-5 Nano
GPT-5 Nano leads, 13.0 against 7.6.
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
GPT-5 Nano
GPT-5 Nano leads, 1101 against 954.
Same board, both measured
Long documents
Listed context window
GPT-5 Nano
GPT-5 Nano takes 400K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
GPT-5 Nano $0.0003, Qwen3 30B A3B $0.0004 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 30B A3B not benchmarked.
- Widest category
- Web design, 16.1 points apart on average over 5 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5 Nano 45.0% against Qwen3 30B A3B 0.0%, 45.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Nano $0.40 against Qwen3 30B A3B $0.50 per million output tokens, 1.3x apart.
- Context window
- GPT-5 Nano takes 400K tokens, Qwen3 30B A3B 41K.
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 30B A3B | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 2 | ||
| Knowledge | 3 | 2 | ||
| Web design | 5 | 0 | ||
| General | 16 | 5 | ||
| All shared boards | 41 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | 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% | 50.6% | GPT-5 Nano |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | 6.8% | GPT-5 Nano |
| Terminal-Bench | 21.8% | Not measured | Only GPT-5 Nano measured |
| WeirdML | 38.1% | 29.8% | GPT-5 Nano |
| LMArena Text: coding | 1351 | 1337 | GPT-5 Nano |
| LMArena Text: software and IT | 1345 | 1340 | GPT-5 Nano |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | 7.6 | GPT-5 Nano |
| Epoch Capabilities Index | 139.2 | 137.3 | GPT-5 Nano |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 36.5% | 26.0% | GPT-5 Nano |
| Berkeley Function Calling Leaderboard (BFCL) | 51.4% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | 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% | 61.6% | GPT-5 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | 6.2% | GPT-5 Nano |
| GPQA Diamond (Epoch AI run) | 69.4% | 61.7% | 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 | 1314 | GPT-5 Nano |
| LMArena Text: expert | 1321 | 1314 | GPT-5 Nano |
| LMArena Text: hard prompts in English | 1338 | 1329 | GPT-5 Nano |
| 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 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | 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 | 72.3 | GPT-5 Nano |
| AIME 2025 (Artificial Analysis run) | 83.7% | 72.3% | GPT-5 Nano |
| OTIS Mock AIME | 81.1% | 62.8% | 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 | 1355 | Qwen3 30B A3B |
| LMArena Text: mathematical work | 1326 | 1363 | Qwen3 30B A3B |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | 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% | 77.7% | GPT-5 Nano |
| SimpleQA Verified | 11.7% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: science | 1318 | 1321 | Qwen3 30B A3B |
| LMArena Text: medicine and healthcare | 1335 | 1318 | GPT-5 Nano |
| LMArena Text: legal and government | 1298 | 1314 | Qwen3 30B A3B |
| LMArena Text: business and finance | 1311 | 1309 | GPT-5 Nano |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 1101 | 954 | GPT-5 Nano |
| Design Arena: UI components | 1064 | 943 | GPT-5 Nano |
| Design Arena: game dev | 1044 | 898 | GPT-5 Nano |
| Design Arena: data viz | 1056 | 967 | GPT-5 Nano |
| Design Arena: 3D | 965 | Not measured | Only GPT-5 Nano measured |
| Design Arena: all code categories | 1084 | 941 | GPT-5 Nano |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index165
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | 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% | 41.5% | GPT-5 Nano |
| Long context reasoning (AA-LCR) | 45.0% | 0.0% | GPT-5 Nano |
| DTBench | 62.7% | 60.3% | GPT-5 Nano |
| LMCA | 7.9% | 15.8% | Qwen3 30B A3B |
| Fiction.LiveBench | 44.4% | 40.6% | GPT-5 Nano |
| Creative writing (Lech Mazur) | Not measured | 75.3% | Only Qwen3 30B A3B measured |
| LMArena Text | 1320 | 1317 | GPT-5 Nano |
| LMArena Text: instruction following | 1306 | 1283 | GPT-5 Nano |
| LMArena Text: creative writing | 1249 | 1271 | Qwen3 30B A3B |
| LMArena Vision | 1159 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: English | 1321 | 1335 | Qwen3 30B A3B |
| LMArena Text: non-English | 1313 | 1295 | GPT-5 Nano |
| LMArena Text: Chinese | 1356 | 1347 | GPT-5 Nano |
| LMArena Text: German | 1327 | 1307 | GPT-5 Nano |
| LMArena Text: Japanese | 1227 | 1254 | Qwen3 30B A3B |
| LMArena Text: Korean | 1269 | 1261 | GPT-5 Nano |
| LMArena Text: French | Not measured | 1352 | Only Qwen3 30B A3B measured |
| LMArena Text: Spanish | 1360 | 1317 | GPT-5 Nano |
| LMArena Text: Russian | 1296 | 1291 | GPT-5 Nano |
| LMArena Text: Polish | 1314 | 1299 | GPT-5 Nano |
| LMArena Text: multi-turn | 1311 | 1307 | GPT-5 Nano |
| LMArena Text: longer queries | 1312 | 1312 | GPT-5 Nano |
| LMArena Text: writing and language | 1283 | 1290 | Qwen3 30B A3B |
| LMArena Text: entertainment and media | 1268 | 1258 | GPT-5 Nano |
| 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 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Nano | Qwen3 30B A3B | 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 30B A3B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | April 28, 2025 |
| Context window | 400K | 41K |
| Input $/Mtok | $0.05 | $0.12 |
| Output $/Mtok | $0.40 | $0.50 |
| 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 30B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and a larger context window (400K against 41K tokens).
Which is cheaper, GPT-5 Nano or Qwen3 30B A3B?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for GPT-5 Nano only.
Which has the bigger context window, GPT-5 Nano or Qwen3 30B A3B?
GPT-5 Nano, with 400K tokens against 41K.
How many benchmarks is the GPT-5 Nano vs Qwen3 30B A3B comparison based on?
50 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 30B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.