Model comparison
GPT-5 Mini vs Qwen3 Max
Qwen3 Max is the stronger model overall, scoring 43.7 to 41.8 on the Noometry Index. GPT-5 Mini costs 3.5× less per token, which makes it the better buy when Qwen3 Max's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
Summary
- They share 31 benchmarks with published results for both. GPT-5 Mini scores higher in 4 categories and Qwen3 Max in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Mini leads 46.7 to 38.7.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 46.7% for GPT-5 Mini and 18.9% for Qwen3 Max.
- GPT-5 Mini is cheaper at $0.25 / $2 per million input/output tokens, against $1.20 / $6 for Qwen3 Max.
- GPT-5 Mini accepts more context: 400K tokens versus 262K.
Side by side
| GPT-5 Mini | Qwen3 Max | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.8 | 43.7 |
| Released | 2025-08-07 | 2025-09-23 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.25 | $1.20 |
| Output $ / M tokens | $2 | $6 |
| Results tracked | 60 | 33 |
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Category by category
Coding Qwen3 Max leads
GPT-5 Mini: 40.1 (#146), Qwen3 Max: 43.0 (#93)
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| LMArena Coding | 1406 | 1456 |
| ALE-Bench | 799.77 | 370.45 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| SWE-bench Multilingual | 39.7% | — |
| SciCode | 39.2% | — |
| WeirdML | 52.7% | — |
| AlgoTune | 1.38 | — |
Agentic & Tool Use Not comparable
GPT-5 Mini: 31.1 (#70), Qwen3 Max: —
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| Vending-Bench 2 | -31.18 | 71.56 |
| Terminal-Bench | 34.8% | — |
| Berkeley Function Calling Leaderboard | 55.5% | — |
Reasoning GPT-5 Mini leads
GPT-5 Mini: 23.9 (#168), Qwen3 Max: 22.6 (#190)
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | 72.5% |
| Chess Puzzles | 30% | 4% |
| LMArena Hard Prompts | 1380 | 1448 |
| Mystery Game Puzzles | 10% | 5% |
| DTBench | 80.5% | 82.1% |
| LMCA | 34.2% | 28.3% |
| Epoch Capabilities Index | 145.52 | 142.38 |
| ARC-AGI-2 | 4.4% | — |
| NYT Connections (extended) | — | 30.1% |
| ARC-AGI-1 | 54.3% | — |
| CritPt | 0% | — |
| EnigmaEval | 8.2% | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.7 (#69), Qwen3 Max: 38.7 (#131)
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 46.7% | 18.9% |
| OTIS Mock AIME 2024-2025 | 86.7% | 73.3% |
| LMArena Math | 1378 | 1446 |
| MATH Level 5 | 97.8% | 97.1% |
| FrontierMath Tier 4 | 12.2% | — |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge Qwen3 Max leads
GPT-5 Mini: 45.6 (#86), Qwen3 Max: 48.1 (#78)
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| GPQA Diamond | 75% | 72.6% |
| SimpleQA Verified | 21.6% | 48.7% |
| LMArena Expert | 1379 | 1455 |
| Humanity's Last Exam | 19.4% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| Vectara Hallucination Rate | 12.9% | — |
| GPQA (HELM) | 75.6% | — |
Multimodal Not comparable
GPT-5 Mini: 35.6 (#85), Qwen3 Max: —
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual Qwen3 Max leads
GPT-5 Mini: 48.9 (#137), Qwen3 Max: 53.7 (#62)
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| LMArena Non-English | 1363 | 1429 |
| LMArena Chinese | 1385 | 1478 |
| LMArena French | 1386 | 1449 |
| LMArena German | 1366 | 1463 |
| LMArena Japanese | 1341 | 1397 |
| LMArena Korean | 1308 | 1399 |
| LMArena Russian | 1362 | 1428 |
| LMArena Spanish | 1355 | 1462 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.2 (#46), Qwen3 Max: 74.8 (#87)
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| LMArena Instruction Following | 1357 | 1419 |
| IFEval | 92.7% | — |
Long Context Too close to call
GPT-5 Mini: 41.9 (#132), Qwen3 Max: 41.6 (#134)
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| Fiction.LiveBench | 69.4% | 66.7% |
| LMArena Longer Query | 1355 | 1438 |
| CL-bench | — | 14.5% |
Writing & Preference Qwen3 Max leads
GPT-5 Mini: 55.2 (#148), Qwen3 Max: 62.4 (#76)
| Benchmark | GPT-5 Mini | Qwen3 Max |
|---|---|---|
| LMArena Text | 1373 | 1439 |
| LMArena Creative Writing | 1325 | 1402 |
| LMArena Multi-Turn | 1363 | 1446 |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
Frequently asked questions
Is GPT-5 Mini better than Qwen3 Max?
Qwen3 Max is the stronger model overall, scoring 43.7 to 41.8 on the Noometry Index. GPT-5 Mini costs 3.5× less per token, which makes it the better buy when Qwen3 Max's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Mini or Qwen3 Max?
GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; Qwen3 Max lists at $1.20 and $6.
Is GPT-5 Mini or Qwen3 Max better for coding?
Qwen3 Max scores higher on coding benchmarks: 43.0 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 262K.
How many benchmarks do GPT-5 Mini and Qwen3 Max share?
31 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Qwen3 Max has 33.