Model comparison
GPT-5.5 vs Qwen3-30B-A3B
GPT-5.5 is the stronger model overall, scoring 63.4 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 52× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
Alibaba (Qwen)Summary
- They share 28 benchmarks with published results for both. GPT-5.5 scores higher in 9 categories and Qwen3-30B-A3B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.8 to 22.2.
- The biggest single-benchmark swing is WeirdML: 84.9% for GPT-5.5 and 29.8% for Qwen3-30B-A3B.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.5 | Qwen3-30B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 63.4 | 38.9 |
| Released | 2026-04-23 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 41K |
| Max output | 128K | 16K |
| Input $ / M tokens | $5 | $0.12 |
| Output $ / M tokens | $30 | $0.50 |
| Results tracked | 71 | 32 |
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Category by category
Coding GPT-5.5 leads
GPT-5.5: 58.2 (#17), Qwen3-30B-A3B: 37.5 (#194)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| SciCode | 56.1% | 33.3% |
| WeirdML | 84.9% | 29.8% |
| LMArena Coding | 1494 | 1416 |
| SWE-bench Verified | 80.6% | — |
| DeepSWE | 67% | — |
| FrontierCode | 43% | — |
| LMArena WebDev | 1513 | — |
| GSO | 40.2% | — |
| MirrorCode | 10% | — |
| ALE-Bench | 1,943 | — |
Agentic & Tool Use GPT-5.5 leads
GPT-5.5: 50.7 (#6), Qwen3-30B-A3B: 29.8 (#82)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| Terminal-Bench | 84.7% | — |
| APEX-Agents | 55.1% | — |
| Berkeley Function Calling Leaderboard | — | 41.4% |
| OSWorld 2.0 | 13% | — |
| Remote Labor Index | 6.3% | — |
| τ²-bench Banking | 44.6% | — |
| DeepResearch Bench | 54% | — |
| PostTrainBench | 27.2% | — |
| ExploitBench | 47.4% | — |
| GBAEval | 53.2% | — |
| GDP.pdf | 26% | — |
| LMArena Search | 1242 | — |
| Vending-Bench 2 | 7,524 | — |
Reasoning GPT-5.5 leads
GPT-5.5: 72.8 (#11), Qwen3-30B-A3B: 22.2 (#204)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| Kagi LLM Benchmark | 88.8% | 54.9% |
| CritPt | 27.1% | 0.3% |
| Chess Puzzles | 54% | 8% |
| LMArena Hard Prompts | 1489 | 1398 |
| DTBench | 96% | 69.3% |
| LMCA | 54.3% | 22.4% |
| Epoch Capabilities Index | 159.1 | 139.63 |
| ARC-AGI-2 | 85% | — |
| SimpleBench | 69% | — |
| NYT Connections (extended) | 96.2% | — |
| ARC-AGI-1 | 95% | — |
| EBR-Bench | 34.3% | — |
| Mystery Game Puzzles | 56% | — |
| Surface Evolver Bench | 88.1% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 60.6 | — |
Math GPT-5.5 leads
GPT-5.5: 81.7 (#11), Qwen3-30B-A3B: 37.4 (#157)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| MathArena Final-Answer Competitions | 94.3% | 47.8% |
| OTIS Mock AIME 2024-2025 | 100% | 70.3% |
| LMArena Math | 1486 | 1394 |
| FrontierMath (Tiers 1-3) | 85.3% | — |
| FrontierMath Tier 4 | 72.5% | — |
| ProofBench | 50% | — |
| FrontierMath (Feb 2025 set) | 51.7% | — |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | 35.4% | — |
Knowledge GPT-5.5 leads
GPT-5.5: 64.4 (#17), Qwen3-30B-A3B: 41.8 (#105)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 94% | 70.1% |
| LMArena Expert | 1508 | 1396 |
| SimpleQA Verified | 63% | — |
| Confabulations | — | 12.3% |
| Vectara Hallucination Rate | 9.3% | — |
Multimodal Not comparable
GPT-5.5: 46.9 (#12), Qwen3-30B-A3B: —
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Vision | 1297 | — |
| Blueprint-Bench 2 | 36.2% | — |
| Furniture Assembly | 44.2% | — |
| LMArena Document | 1486 | — |
Multilingual GPT-5.5 leads
GPT-5.5: 56.4 (#20), Qwen3-30B-A3B: 49.5 (#132)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1467 | 1372 |
| LMArena Chinese | 1533 | 1433 |
| LMArena French | 1486 | 1418 |
| LMArena German | 1480 | 1380 |
| LMArena Japanese | 1498 | 1337 |
| LMArena Korean | 1460 | 1331 |
| LMArena Russian | 1473 | 1370 |
| LMArena Spanish | 1468 | 1404 |
Instruction Following GPT-5.5 leads
GPT-5.5: 77.5 (#18), Qwen3-30B-A3B: 72.0 (#142)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1479 | 1363 |
Long Context GPT-5.5 leads
GPT-5.5: 48.3 (#12), Qwen3-30B-A3B: 31.0 (#283)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1484 | 1379 |
| Fiction.LiveBench | — | 40.6% |
| CL-bench Life | 22.2% | — |
Writing & Preference GPT-5.5 leads
GPT-5.5: 72.7 (#13), Qwen3-30B-A3B: 55.6 (#143)
| Benchmark | GPT-5.5 | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1472 | 1384 |
| LMArena Creative Writing | 1455 | 1317 |
| LMArena Multi-Turn | 1476 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
| EQ-Bench Creative Writing | 1844 | — |
| EQ-Bench 4 | 1315 | — |
Frequently asked questions
Is GPT-5.5 better than Qwen3-30B-A3B?
GPT-5.5 is the stronger model overall, scoring 63.4 to 38.9 on the Noometry Index. Qwen3-30B-A3B costs 52× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Which is cheaper, GPT-5.5 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-5.5 lists at $5 and $30.
Is GPT-5.5 or Qwen3-30B-A3B better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.2 versus 37.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.5 does, with 1.05M tokens against 41K.
How many benchmarks do GPT-5.5 and Qwen3-30B-A3B share?
28 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Qwen3-30B-A3B has 32.