Model comparison
GPT-5 Mini vs Qwen3-Coder 480B-A35B Instruct
GPT-5 Mini is the stronger model overall, scoring 41.8 to 38.1 on the Noometry Index.
Last verified . 23 shared benchmarks.
Alibaba (Qwen)Summary
- They share 23 benchmarks with published results for both. GPT-5 Mini scores higher in 6 categories and Qwen3-Coder 480B-A35B Instruct in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Mini leads 46.7 to 37.6.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 70.3% for GPT-5 Mini and 49.5% for Qwen3-Coder 480B-A35B Instruct.
- GPT-5 Mini is cheaper at $0.25 / $2 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- GPT-5 Mini accepts more context: 400K tokens versus 262K.
- Qwen3-Coder 480B-A35B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.8 | 38.1 |
| Released | 2025-08-07 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.25 | $1.50 |
| Output $ / M tokens | $2 | $7.50 |
| Results tracked | 60 | 25 |
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Category by category
Coding GPT-5 Mini leads
GPT-5 Mini: 40.1 (#146), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 59.8% | 55.4% |
| WeirdML | 52.7% | 41.2% |
| LMArena Coding | 1406 | 1412 |
| ALE-Bench | 799.77 | 461.45 |
| AlgoTune | 1.38 | 1.44 |
| SWE-bench Verified | 64.7% | — |
| LMArena WebDev | — | 1275 |
| SWE-bench Multilingual | 39.7% | — |
| SciCode | 39.2% | — |
| GSO | — | 4.9% |
Agentic & Tool Use GPT-5 Mini leads
GPT-5 Mini: 31.1 (#70), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | 34.8% | 27.2% |
| Berkeley Function Calling Leaderboard | 55.5% | — |
| Vending-Bench 2 | -31.18 | — |
Reasoning Qwen3-Coder 480B-A35B Instruct leads
GPT-5 Mini: 23.9 (#168), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | 49.5% |
| LMArena Hard Prompts | 1380 | 1372 |
| ARC-AGI-2 | 4.4% | — |
| ARC-AGI-1 | 54.3% | — |
| CritPt | 0% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | 8.2% | — |
| Mystery Game Puzzles | 10% | — |
| DTBench | 80.5% | — |
| LMCA | 34.2% | — |
| Epoch Capabilities Index | 145.52 | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.7 (#69), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1378 | 1365 |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 86.7% | — |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge GPT-5 Mini leads
GPT-5 Mini: 45.6 (#86), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1379 | 1338 |
| GPQA Diamond | 75% | — |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| 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-Coder 480B-A35B Instruct: —
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual GPT-5 Mini leads
GPT-5 Mini: 48.9 (#137), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1363 | 1346 |
| LMArena Chinese | 1385 | 1357 |
| LMArena French | 1386 | 1398 |
| LMArena German | 1366 | 1325 |
| LMArena Japanese | 1341 | 1310 |
| LMArena Korean | 1308 | 1305 |
| LMArena Russian | 1362 | 1366 |
| LMArena Spanish | 1355 | 1360 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.2 (#46), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1357 | 1355 |
| IFEval | 92.7% | — |
Long Context Too close to call
GPT-5 Mini: 41.9 (#132), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1355 | 1378 |
| Fiction.LiveBench | 69.4% | — |
Writing & Preference Too close to call
GPT-5 Mini: 55.2 (#148), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)
| Benchmark | GPT-5 Mini | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1373 | 1357 |
| LMArena Creative Writing | 1325 | 1333 |
| LMArena Multi-Turn | 1363 | 1365 |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
Frequently asked questions
Is GPT-5 Mini better than Qwen3-Coder 480B-A35B Instruct?
GPT-5 Mini is the stronger model overall, scoring 41.8 to 38.1 on the Noometry Index.
Which is cheaper, GPT-5 Mini or Qwen3-Coder 480B-A35B Instruct?
GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is GPT-5 Mini or Qwen3-Coder 480B-A35B Instruct better for coding?
GPT-5 Mini scores higher on coding benchmarks: 40.1 versus 35.5 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-Coder 480B-A35B Instruct share?
23 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.