Model comparison
GPT-5 Nano vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.0 to 33.4 on the Noometry Index. GPT-5 Nano costs 22× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. GPT-5 Nano scores higher in 0 categories and Qwen3.8 Max in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.1 to 29.1.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 20% for GPT-5 Nano and 74.7% for Qwen3.8 Max.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- Qwen3.8 Max accepts more context: 1M tokens versus 400K.
Side by side
| GPT-5 Nano | Qwen3.8 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.4 | 56.0 |
| Released | 2025-08-07 | 2026-08-02 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.05 | $2 |
| Output $ / M tokens | $0.40 | $6 |
| Results tracked | 48 | 38 |
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Category by category
Coding Qwen3.8 Max leads
GPT-5 Nano: 33.3 (#248), Qwen3.8 Max: 52.3 (#30)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| LMArena Coding | 1351 | 1499 |
| DeepSWE | — | 57.5% |
| SWE-bench Verified (bash only) | 34.8% | — |
| LMArena WebDev | — | 1671 |
| FrontierSWE | — | 15.8% |
| SciCode | — | 52.9% |
| WeirdML | 38.1% | — |
| ALE-Bench | 718.67 | — |
Agentic & Tool Use Qwen3.8 Max leads
GPT-5 Nano: 25.6 (#99), Qwen3.8 Max: 42.6 (#17)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| Berkeley Function Calling Leaderboard | 51.5% | — |
| τ²-bench Banking | — | 55.1% |
| GDP.pdf | — | 23.2% |
Reasoning Qwen3.8 Max leads
GPT-5 Nano: 16.4 (#291), Qwen3.8 Max: 53.4 (#26)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| Chess Puzzles | 27% | 40% |
| LMArena Hard Prompts | 1329 | 1493 |
| Mystery Game Puzzles | 9% | 38% |
| DTBench | 62.7% | 92% |
| LMCA | 7.9% | 46.2% |
| Epoch Capabilities Index | 139.39 | 156.69 |
| ARC-AGI-2 | 2.6% | — |
| Kagi LLM Benchmark | 62.2% | — |
| NYT Connections (extended) | — | 88.3% |
| ARC-AGI-1 | 20.7% | — |
| CritPt | — | 20% |
| ForecastBench | 59.1 | — |
Math Qwen3.8 Max leads
GPT-5 Nano: 29.1 (#221), Qwen3.8 Max: 73.1 (#15)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 20% | 74.7% |
| FrontierMath Tier 4 | 2.4% | 46.3% |
| OTIS Mock AIME 2024-2025 | 81.1% | 100% |
| ProofBench | 12% | 58% |
| LMArena Math | 1318 | 1498 |
| Omni-MATH | 54.6% | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Qwen3.8 Max leads
GPT-5 Nano: 35.8 (#166), Qwen3.8 Max: 61.7 (#25)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 69.4% | 92.7% |
| SimpleQA Verified | 11.7% | 47.3% |
| LMArena Expert | 1321 | 1504 |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
Multimodal Qwen3.8 Max leads
GPT-5 Nano: 30.7 (#102), Qwen3.8 Max: 36.9 (#70)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | 1159 | 1314 |
| VPCT | 37.2% | — |
| Furniture Assembly | — | 20% |
Multilingual Qwen3.8 Max leads
GPT-5 Nano: 45.5 (#167), Qwen3.8 Max: 56.6 (#18)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1313 | 1468 |
| LMArena Chinese | 1355 | 1537 |
| LMArena German | 1329 | 1489 |
| LMArena Korean | 1269 | 1467 |
| LMArena Russian | 1296 | 1478 |
| LMArena Spanish | 1360 | 1495 |
| LMArena French | — | 1502 |
| LMArena Japanese | — | 1466 |
Instruction Following Qwen3.8 Max leads
GPT-5 Nano: 75.2 (#72), Qwen3.8 Max: 77.7 (#19)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1306 | 1478 |
| IFEval | 93.2% | — |
Long Context Qwen3.8 Max leads
GPT-5 Nano: 31.3 (#275), Qwen3.8 Max: 45.5 (#30)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1313 | 1486 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Qwen3.8 Max leads
GPT-5 Nano: 39.4 (#244), Qwen3.8 Max: 67.0 (#31)
| Benchmark | GPT-5 Nano | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1320 | 1480 |
| LMArena Creative Writing | 1250 | 1478 |
| LMArena Multi-Turn | 1311 | 1487 |
| EQ-Bench Creative Writing | 705 | — |
| WildBench | 80.6% | — |
Frequently asked questions
Is GPT-5 Nano better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.0 to 33.4 on the Noometry Index. GPT-5 Nano costs 22× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Nano or Qwen3.8 Max?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Qwen3.8 Max lists at $2 and $6.
Is GPT-5 Nano or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 52.3 versus 33.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 Max does, with 1M tokens against 400K.
How many benchmarks do GPT-5 Nano and Qwen3.8 Max share?
27 benchmarks have published results for both models. GPT-5 Nano has 48 scored results on Noometry and Qwen3.8 Max has 38.