Model comparison
GPT-4o mini vs Qwen3-30B-A3B
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 25.5 on the Noometry Index.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. GPT-4o mini scores higher in 1 category and Qwen3-30B-A3B in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3-30B-A3B leads 37.4 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.9% for GPT-4o mini and 70.3% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $0.15 / $0.60 for GPT-4o mini.
- GPT-4o mini accepts more context: 128K tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o mini | Qwen3-30B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 25.5 | 38.9 |
| Released | 2024-07-18 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 128K | 41K |
| Max output | 16K | 16K |
| Input $ / M tokens | $0.15 | $0.12 |
| Output $ / M tokens | $0.60 | $0.50 |
| Results tracked | 60 | 32 |
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Category by category
Coding Qwen3-30B-A3B leads
GPT-4o mini: 22.0 (#335), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| WeirdML | 11.8% | 29.8% |
| LMArena Coding | 1290 | 1416 |
| Aider Polyglot | 3.6% | — |
| SciCode | — | 33.3% |
| BigCodeBench Instruct | 46.1% | — |
| LiveBench Coding | 43.1% | — |
| BigCodeBench Complete | 57.4% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Qwen3-30B-A3B leads
GPT-4o mini: 27.5 (#101), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
| BALROG | 17.4% | — |
Reasoning Qwen3-30B-A3B leads
GPT-4o mini: 8.7 (#347), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 28.8% | 54.9% |
| Chess Puzzles | 0% | 8% |
| LMArena Hard Prompts | 1267 | 1398 |
| DTBench | 54.4% | 69.3% |
| LMCA | 10.4% | 22.4% |
| Epoch Capabilities Index | 126.56 | 139.63 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| CritPt | — | 0.3% |
| LiveBench Reasoning | 32.8% | — |
| Mystery Game Puzzles | 12% | — |
| LiveBench Data Analysis | 50% | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math Qwen3-30B-A3B leads
GPT-4o mini: 10.4 (#314), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.9% | 70.3% |
| LMArena Math | 1267 | 1394 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| MathArena Final-Answer Competitions | — | 47.8% |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |
Knowledge Qwen3-30B-A3B leads
GPT-4o mini: 17.7 (#284), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 37.7% | 70.1% |
| Confabulations | 37.2% | 12.3% |
| LMArena Expert | 1235 | 1396 |
| SimpleQA Verified | 8.3% | — |
| MMLU-Pro | 60.3% | — |
| GPQA (HELM) | 36.8% | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |
Multimodal Not comparable
GPT-4o mini: 25.9 (#122), Qwen3-30B-A3B: —
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual Qwen3-30B-A3B leads
GPT-4o mini: 42.0 (#199), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1266 | 1372 |
| LMArena Chinese | 1265 | 1433 |
| LMArena French | 1297 | 1418 |
| LMArena German | 1272 | 1380 |
| LMArena Japanese | 1216 | 1337 |
| LMArena Korean | 1195 | 1331 |
| LMArena Russian | 1275 | 1370 |
| LMArena Spanish | 1276 | 1404 |
Instruction Following Qwen3-30B-A3B leads
GPT-4o mini: 61.9 (#239), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1258 | 1363 |
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |
Long Context GPT-4o mini leads
GPT-4o mini: 39.1 (#186), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1289 | 1379 |
| Fiction.LiveBench | — | 40.6% |
Writing & Preference Qwen3-30B-A3B leads
GPT-4o mini: 39.5 (#248), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | GPT-4o mini | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1286 | 1384 |
| LMArena Creative Writing | 1268 | 1317 |
| Short-Story Creative Writing | 67.2% | 75.3% |
| LMArena Multi-Turn | 1285 | 1378 |
| EQ-Bench Creative Writing | 873 | — |
| WildBench | 79.1% | — |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than Qwen3-30B-A3B?
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 25.5 on the Noometry Index.
Which is cheaper, GPT-4o 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-4o mini lists at $0.15 and $0.60.
Is GPT-4o mini or Qwen3-30B-A3B better for coding?
Qwen3-30B-A3B scores higher on coding benchmarks: 37.5 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
GPT-4o mini does, with 128K tokens against 41K.
How many benchmarks do GPT-4o mini and Qwen3-30B-A3B share?
27 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Qwen3-30B-A3B has 32.