Model comparison
GPT-5 Mini vs Qwen3-30B-A3B
GPT-5 Mini is the stronger model overall, scoring 41.7 to 38.5 on the Noometry Index. Qwen3-30B-A3B costs 3.2× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
Alibaba (Qwen)Summary
- They share 31 benchmarks with published results for both. GPT-5 Mini scores higher in 7 categories and Qwen3-30B-A3B in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 Mini leads 41.9 to 31.0.
- The biggest single-benchmark swing is Fiction.LiveBench: 69.4% for GPT-5 Mini and 40.6% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- GPT-5 Mini accepts more context: 400K tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Mini | Qwen3-30B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.7 | 38.5 |
| Released | 2025-08-07 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 400K | 41K |
| Max output | 128K | 16K |
| Input $ / M tokens | $0.25 | $0.12 |
| Output $ / M tokens | $2 | $0.50 |
| Results tracked | 60 | 32 |
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Category by category
Coding GPT-5 Mini leads
GPT-5 Mini: 40.0 (#134), Qwen3-30B-A3B: 37.0 (#188)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| SciCode | 39.2% | 33.3% |
| WeirdML | 52.7% | 29.8% |
| LMArena Coding | 1406 | 1416 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| SWE-bench Multilingual | 39.7% | — |
| ALE-Bench | 799.77 | — |
| AlgoTune | 1.38 | — |
Agentic & Tool Use GPT-5 Mini leads
GPT-5 Mini: 31.0 (#64), Qwen3-30B-A3B: 29.5 (#75)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 55.5% | 41.4% |
| Terminal-Bench | 34.8% | — |
| Vending-Bench 2 | -31.18 | — |
Reasoning GPT-5 Mini leads
GPT-5 Mini: 24.1 (#153), Qwen3-30B-A3B: 21.6 (#193)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | 54.9% |
| CritPt | 0% | 0.3% |
| Chess Puzzles | 30% | 8% |
| LMArena Hard Prompts | 1380 | 1398 |
| DTBench | 80.5% | 69.3% |
| LMCA | 34.2% | 22.4% |
| Epoch Capabilities Index | 145.51 | 139.63 |
| ARC-AGI-2 | 4.4% | — |
| ARC-AGI-1 | 54.3% | — |
| EnigmaEval | 8.2% | — |
| Mystery Game Puzzles | 10% | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.3 (#59), Qwen3-30B-A3B: 36.0 (#133)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.7% | 70.3% |
| LMArena Math | 1377 | 1394 |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| MathArena Final-Answer Competitions | — | 47.8% |
| 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: 45.5 (#80), Qwen3-30B-A3B: 41.7 (#96)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 75% | 70.1% |
| Confabulations | 13.3% | 12.3% |
| LMArena Expert | 1377 | 1395 |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| MMLU-Pro | 83.5% | — |
| Vectara Hallucination Rate | 12.9% | — |
| GPQA (HELM) | 75.6% | — |
Multimodal Not comparable
GPT-5 Mini: 35.0 (#80), Qwen3-30B-A3B: —
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual Too close to call
GPT-5 Mini: 49.1 (#131), Qwen3-30B-A3B: 49.8 (#126)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1363 | 1372 |
| LMArena Chinese | 1387 | 1434 |
| LMArena French | 1385 | 1419 |
| LMArena German | 1366 | 1379 |
| LMArena Japanese | 1342 | 1338 |
| LMArena Korean | 1306 | 1333 |
| LMArena Russian | 1362 | 1369 |
| LMArena Spanish | 1355 | 1403 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.4 (#43), Qwen3-30B-A3B: 72.2 (#137)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1357 | 1364 |
| IFEval | 92.7% | — |
Long Context GPT-5 Mini leads
GPT-5 Mini: 41.9 (#127), Qwen3-30B-A3B: 31.0 (#278)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| Fiction.LiveBench | 69.4% | 40.6% |
| LMArena Longer Query | 1355 | 1379 |
Writing & Preference Too close to call
GPT-5 Mini: 55.4 (#143), Qwen3-30B-A3B: 55.9 (#138)
| Benchmark | GPT-5 Mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1373 | 1384 |
| LMArena Creative Writing | 1325 | 1318 |
| Short-Story Creative Writing | 83.1% | 75.3% |
| LMArena Multi-Turn | 1363 | 1378 |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
Frequently asked questions
Is GPT-5 Mini better than Qwen3-30B-A3B?
GPT-5 Mini is the stronger model overall, scoring 41.7 to 38.5 on the Noometry Index. Qwen3-30B-A3B costs 3.2× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Mini or Qwen3-30B-A3B?
Qwen3-30B-A3B is cheaper. It lists at $0.12 per million input tokens and $0.50 per million output tokens; GPT-5 Mini lists at $0.25 and $2.
Is GPT-5 Mini or Qwen3-30B-A3B better for coding?
GPT-5 Mini scores higher on coding benchmarks: 40.0 versus 37.0 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 41K.
How many benchmarks do GPT-5 Mini and Qwen3-30B-A3B share?
31 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Qwen3-30B-A3B has 32.