GPT-5 Nano vs Qwen3.5-Flash
Qwen3.5-Flash costs 1.5x less per output token than GPT-5 Nano ($0.26 vs $0.40 per million). Updated October 22, 2026; 48 benchmarks have a result for both.
GPT-5 Nano or Qwen3.5-Flash: which to pick
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding.
Pick GPT-5 Nano for a lower cost per solved task ($0.109 against $0.40) and work measured by Chess puzzles or FrontierMath, tiers 1 to 3, where it leads.
- Shared results
- 48
- GPT-5 Nano only
- 40
- Qwen3.5-Flash only
- 7
- Like-for-like categories
- 6 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 Qwen3.5-Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5 Nano has a AA Intelligence Index result, 13.0.
Directional only
Reasoning
GPQA Diamond
Qwen3.5-Flash
Qwen3.5-Flash leads, 82.8% against 63.4%.
Same board, both measured
Math
AIME
Qwen3.5-Flash
Qwen3.5-Flash leads, 92.5% against 81.2%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.5-Flash
Qwen3.5-Flash leads, 84.1% against 76.1%.
Same board, both measured
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
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GPT-5 Nano $0.0003, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Nano
GPT-5 Nano $0.109, Qwen3.5-Flash $0.40.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 Nano not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- General, 13.4 points apart on average over 20 shared benchmarks.
- Widest single gap
- LegalBench (Vals AI): GPT-5 Nano 50.1% against Qwen3.5-Flash 84.3%, 34.1 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Nano $0.40 against Qwen3.5-Flash $0.26 per million output tokens, 1.5x apart.
- Context window
- GPT-5 Nano takes 400K tokens, Qwen3.5-Flash 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 | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 7 | ||
| Math | 1 | 4 | ||
| Knowledge | 0 | 11 | ||
| General | 1 | 19 | ||
| All shared boards | 3 | 45 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 34.8% | Not measured | Only GPT-5 Nano measured |
| LMArena WebDev | Not measured | 1244 | Only Qwen3.5-Flash measured |
| LiveCodeBench (Vals AI) | 70.2% | 83.3% | Qwen3.5-Flash |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only GPT-5 Nano measured |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | Not measured | Only GPT-5 Nano measured |
| 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 | 265.9 | Only Qwen3.5-Flash measured |
| LMArena Text: coding | 1351 | 1412 | Qwen3.5-Flash |
| LMArena Text: software and IT | 1345 | 1416 | Qwen3.5-Flash |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | Not measured | 1291 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | Not measured | Only GPT-5 Nano measured |
| Epoch Capabilities Index | 139.2 | 143.5 | Qwen3.5-Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 36.5% | Not measured | Only GPT-5 Nano measured |
| 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 index17
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 63.4% | 82.8% | Qwen3.5-Flash |
| MMMU Pro (Vals AI) | 70.9% | 81.9% | Qwen3.5-Flash |
| GPQA Diamond (Artificial Analysis run) | 67.6% | Not measured | Only GPT-5 Nano measured |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | Not measured | Only GPT-5 Nano measured |
| GPQA Diamond (Epoch AI run) | 69.4% | 82.3% | Qwen3.5-Flash |
| 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% | 14.0% | GPT-5 Nano |
| Mystery game puzzles | 9.0% | 20.0% | Qwen3.5-Flash |
| VPCT | 37.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1328 | 1403 | Qwen3.5-Flash |
| LMArena Text: expert | 1321 | 1407 | Qwen3.5-Flash |
| LMArena Text: hard prompts in English | 1338 | 1409 | Qwen3.5-Flash |
| 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 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | 81.2% | 92.5% | Qwen3.5-Flash |
| 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% | 84.4% | Qwen3.5-Flash |
| FrontierMath, tiers 1 to 3 | 20.0% | 18.2% | GPT-5 Nano |
| 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 | 1407 | Qwen3.5-Flash |
| LMArena Text: mathematical work | 1326 | 1413 | Qwen3.5-Flash |
| 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 index011
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | 76.1% | 84.1% | Qwen3.5-Flash |
| LegalBench (Vals AI) | 50.1% | 84.3% | Qwen3.5-Flash |
| CaseLaw (Vals AI) | 52.6% | 56.0% | Qwen3.5-Flash |
| CorpFin (Vals AI) | Not measured | 63.6% | Only Qwen3.5-Flash measured |
| TaxEval (Vals AI) | 67.4% | 72.2% | Qwen3.5-Flash |
| MortgageTax (Vals AI) | 53.6% | 67.4% | Qwen3.5-Flash |
| MedQA (Vals AI) | 93.3% | Not measured | Only GPT-5 Nano measured |
| MedCode (Vals AI) | 30.4% | 33.0% | Qwen3.5-Flash |
| MMLU Pro (Artificial Analysis run) | 78.0% | Not measured | Only GPT-5 Nano measured |
| SimpleQA Verified | 11.7% | 20.3% | Qwen3.5-Flash |
| LMArena Text: science | 1318 | 1415 | Qwen3.5-Flash |
| LMArena Text: medicine and healthcare | 1335 | 1407 | Qwen3.5-Flash |
| LMArena Text: legal and government | 1298 | 1398 | Qwen3.5-Flash |
| LMArena Text: business and finance | 1311 | 1394 | Qwen3.5-Flash |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | 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 index119
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 30.4% | 42.5% | Qwen3.5-Flash |
| MedScribe (Vals AI) | 72.9% | 70.6% | GPT-5 Nano |
| IFBench | 67.6% | Not measured | Only GPT-5 Nano measured |
| Long context reasoning (AA-LCR) | 45.0% | Not measured | Only GPT-5 Nano measured |
| DTBench | 62.7% | 82.9% | Qwen3.5-Flash |
| LMCA | 7.9% | 29.1% | Qwen3.5-Flash |
| Fiction.LiveBench | 44.4% | Not measured | Only GPT-5 Nano measured |
| LMArena Text | 1320 | 1397 | Qwen3.5-Flash |
| LMArena Text: instruction following | 1306 | 1374 | Qwen3.5-Flash |
| LMArena Text: creative writing | 1249 | 1343 | Qwen3.5-Flash |
| LMArena Vision | 1159 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: English | 1321 | 1404 | Qwen3.5-Flash |
| LMArena Text: non-English | 1313 | 1385 | Qwen3.5-Flash |
| LMArena Text: Chinese | 1356 | 1445 | Qwen3.5-Flash |
| LMArena Text: German | 1327 | 1390 | Qwen3.5-Flash |
| LMArena Text: Japanese | 1227 | 1369 | Qwen3.5-Flash |
| LMArena Text: Korean | 1269 | 1344 | Qwen3.5-Flash |
| LMArena Text: French | Not measured | 1413 | Only Qwen3.5-Flash measured |
| LMArena Text: Spanish | 1360 | 1400 | Qwen3.5-Flash |
| LMArena Text: Russian | 1296 | 1380 | Qwen3.5-Flash |
| LMArena Text: Polish | 1314 | 1376 | Qwen3.5-Flash |
| LMArena Text: multi-turn | 1311 | 1393 | Qwen3.5-Flash |
| LMArena Text: longer queries | 1312 | 1392 | Qwen3.5-Flash |
| LMArena Text: writing and language | 1283 | 1369 | Qwen3.5-Flash |
| LMArena Text: entertainment and media | 1268 | 1347 | Qwen3.5-Flash |
| 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 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Nano | Qwen3.5-Flash | 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
Qwen3.5-Flash 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 | Qwen3.5-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | August 7, 2025 | February 25, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $0.05 | $0.065 |
| Output $/Mtok | $0.40 | $0.26 |
| Coding index | Not benchmarked | 41.1 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | $0.109 | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Nano better than Qwen3.5-Flash for coding?
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding. Pick GPT-5 Nano for a lower cost per solved task ($0.109 against $0.40) and work measured by Chess puzzles or FrontierMath, tiers 1 to 3, where it leads.
Which is cheaper, GPT-5 Nano or Qwen3.5-Flash?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, GPT-5 Nano costs $0.109 and Qwen3.5-Flash $0.40.
Which has the bigger context window, GPT-5 Nano or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 400K.
How many benchmarks is the GPT-5 Nano vs Qwen3.5-Flash comparison based on?
48 benchmarks have a result for both models as of October 22, 2026. GPT-5 Nano's coding index rests on 0 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.