Model comparison
GPT-4o vs Qwen3-30B-A3B
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 28.6 on the Noometry Index.
Last verified . 28 shared benchmarks.
Alibaba (Qwen)Summary
- They share 28 benchmarks with published results for both. GPT-4o 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 10.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.4% for GPT-4o and 70.3% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- GPT-4o accepts more context: 128K tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Qwen3-30B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 28.6 | 38.9 |
| Released | 2024-05-13 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 128K | 41K |
| Max output | 16K | 16K |
| Input $ / M tokens | $2.50 | $0.12 |
| Output $ / M tokens | $10 | $0.50 |
| Results tracked | 72 | 32 |
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Category by category
Coding Qwen3-30B-A3B leads
GPT-4o: 24.8 (#328), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| WeirdML | 25.1% | 29.8% |
| LMArena Coding | 1297 | 1416 |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| SciCode | — | 33.3% |
| GSO | 0% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Qwen3-30B-A3B leads
GPT-4o: 21.0 (#141), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning Qwen3-30B-A3B leads
GPT-4o: 9.4 (#343), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| CritPt | 0% | 0.3% |
| Chess Puzzles | 13% | 8% |
| LMArena Hard Prompts | 1281 | 1398 |
| DTBench | 64.5% | 69.3% |
| LMCA | 16.6% | 22.4% |
| Epoch Capabilities Index | 128.97 | 139.63 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| Kagi LLM Benchmark | — | 54.9% |
| ARC-AGI-1 | 4.5% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math Qwen3-30B-A3B leads
GPT-4o: 10.6 (#312), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.4% | 70.3% |
| LMArena Math | 1285 | 1394 |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| MathArena Final-Answer Competitions | — | 47.8% |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Qwen3-30B-A3B leads
GPT-4o: 28.8 (#242), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 49.2% | 70.1% |
| Confabulations | 15.3% | 12.3% |
| LMArena Expert | 1250 | 1396 |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| GPQA (HELM) | 52% | — |
| MMLU | 88.1% | — |
Multimodal Not comparable
GPT-4o: 34.5 (#91), Qwen3-30B-A3B: —
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual Qwen3-30B-A3B leads
GPT-4o: 43.2 (#186), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1283 | 1372 |
| LMArena Chinese | 1277 | 1433 |
| LMArena French | 1304 | 1418 |
| LMArena German | 1282 | 1380 |
| LMArena Japanese | 1257 | 1337 |
| LMArena Korean | 1234 | 1331 |
| LMArena Russian | 1286 | 1370 |
| LMArena Spanish | 1292 | 1404 |
Instruction Following Qwen3-30B-A3B leads
GPT-4o: 66.6 (#207), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1278 | 1363 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
Long Context GPT-4o leads
GPT-4o: 39.4 (#179), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| Fiction.LiveBench | 66.7% | 40.6% |
| LMArena Longer Query | 1289 | 1379 |
Writing & Preference Qwen3-30B-A3B leads
GPT-4o: 52.6 (#166), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | GPT-4o | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1300 | 1384 |
| LMArena Creative Writing | 1292 | 1317 |
| Short-Story Creative Writing | 81.8% | 75.3% |
| LMArena Multi-Turn | 1302 | 1378 |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Qwen3-30B-A3B?
Qwen3-30B-A3B is the stronger model overall, scoring 38.9 to 28.6 on the Noometry Index.
Which is cheaper, GPT-4o 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 lists at $2.50 and $10.
Is GPT-4o or Qwen3-30B-A3B better for coding?
Qwen3-30B-A3B scores higher on coding benchmarks: 37.5 versus 24.8 in the Noometry coding category.
Which has the bigger context window?
GPT-4o does, with 128K tokens against 41K.
How many benchmarks do GPT-4o and Qwen3-30B-A3B share?
28 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Qwen3-30B-A3B has 32.