Model comparison
GPT-5 Nano vs Qwen3.6 Flash
Qwen3.6 Flash is the stronger model overall, scoring 38.4 to 33.4 on the Noometry Index. GPT-5 Nano costs 3.1× less per token, which makes it the better buy when Qwen3.6 Flash's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. GPT-5 Nano scores higher in 0 categories and Qwen3.6 Flash in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.6 Flash leads 28.4 to 16.4.
- The biggest single-benchmark swing is LMCA: 7.9% for GPT-5 Nano and 31% for Qwen3.6 Flash.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.19 / $1.13 for Qwen3.6 Flash.
- Qwen3.6 Flash accepts more context: 1M tokens versus 400K.
Side by side
| GPT-5 Nano | Qwen3.6 Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.4 | 38.4 |
| Released | 2025-08-07 | 2026-04-27 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1M |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.05 | $0.19 |
| Output $ / M tokens | $0.40 | $1.13 |
| Results tracked | 48 | 13 |
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Category by category
Coding Not comparable
GPT-5 Nano: 33.3 (#248), Qwen3.6 Flash: —
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| ALE-Bench | 718.67 | 326.4 |
| SWE-bench Verified (bash only) | 34.8% | — |
| WeirdML | 38.1% | — |
| LMArena Coding | 1351 | — |
Agentic & Tool Use Not comparable
GPT-5 Nano: 25.6 (#99), Qwen3.6 Flash: —
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| Berkeley Function Calling Leaderboard | 51.5% | — |
Reasoning Qwen3.6 Flash leads
GPT-5 Nano: 16.4 (#291), Qwen3.6 Flash: 28.4 (#90)
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| Chess Puzzles | 27% | 20% |
| Mystery Game Puzzles | 9% | 18% |
| DTBench | 62.7% | 77.1% |
| LMCA | 7.9% | 31% |
| Epoch Capabilities Index | 139.39 | 143.26 |
| ARC-AGI-2 | 2.6% | — |
| SimpleBench | — | 35.2% |
| Kagi LLM Benchmark | 62.2% | — |
| ARC-AGI-1 | 20.7% | — |
| LMArena Hard Prompts | 1329 | — |
| ForecastBench | 59.1 | — |
Math Qwen3.6 Flash leads
GPT-5 Nano: 29.1 (#221), Qwen3.6 Flash: 38.0 (#88)
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 20% | 22.5% |
| OTIS Mock AIME 2024-2025 | 81.1% | 84.4% |
| FrontierMath (Feb 2025 set) | 8.3% | 10.3% |
| FrontierMath Tier 4 (v1) | 2.1% | 0% |
| FrontierMath Tier 4 | 2.4% | — |
| ProofBench | 12% | — |
| Omni-MATH | 54.6% | — |
| LMArena Math | 1318 | — |
| MATH Level 5 | 95.2% | — |
Knowledge Qwen3.6 Flash leads
GPT-5 Nano: 35.8 (#166), Qwen3.6 Flash: 42.2 (#90)
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| GPQA Diamond | 69.4% | 83.3% |
| SimpleQA Verified | 11.7% | 15.9% |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
| LMArena Expert | 1321 | — |
Multimodal Not comparable
GPT-5 Nano: 30.7 (#102), Qwen3.6 Flash: —
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| LMArena Vision | 1159 | — |
| VPCT | 37.2% | — |
Multilingual Not comparable
GPT-5 Nano: 45.5 (#167), Qwen3.6 Flash: —
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| LMArena Non-English | 1313 | — |
| LMArena Chinese | 1355 | — |
| LMArena German | 1329 | — |
| LMArena Korean | 1269 | — |
| LMArena Russian | 1296 | — |
| LMArena Spanish | 1360 | — |
Instruction Following Not comparable
GPT-5 Nano: 75.2 (#72), Qwen3.6 Flash: —
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| IFEval | 93.2% | — |
| LMArena Instruction Following | 1306 | — |
Long Context Not comparable
GPT-5 Nano: 31.3 (#275), Qwen3.6 Flash: —
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| Fiction.LiveBench | 44.4% | — |
| LMArena Longer Query | 1313 | — |
Writing & Preference Not comparable
GPT-5 Nano: 39.4 (#244), Qwen3.6 Flash: —
| Benchmark | GPT-5 Nano | Qwen3.6 Flash |
|---|---|---|
| LMArena Text | 1320 | — |
| LMArena Creative Writing | 1250 | — |
| EQ-Bench Creative Writing | 705 | — |
| WildBench | 80.6% | — |
| LMArena Multi-Turn | 1311 | — |
Frequently asked questions
Is GPT-5 Nano better than Qwen3.6 Flash?
Qwen3.6 Flash is the stronger model overall, scoring 38.4 to 33.4 on the Noometry Index. GPT-5 Nano costs 3.1× less per token, which makes it the better buy when Qwen3.6 Flash's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Nano or Qwen3.6 Flash?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Qwen3.6 Flash lists at $0.19 and $1.13.
Which has the bigger context window?
Qwen3.6 Flash does, with 1M tokens against 400K.
How many benchmarks do GPT-5 Nano and Qwen3.6 Flash share?
12 benchmarks have published results for both models. GPT-5 Nano has 48 scored results on Noometry and Qwen3.6 Flash has 13.