Model comparison
GPT-6 Sol vs Qwen3-30B-A3B
GPT-6 Sol is the stronger model overall, scoring 49.2 to 38.5 on the Noometry Index. Qwen3-30B-A3B costs 19× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. GPT-6 Sol scores higher in 8 categories and Qwen3-30B-A3B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 21.6.
- Qwen3-30B-A3B is cheaper at $0.12 / $0.50 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 41K.
- Qwen3-30B-A3B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Qwen3-30B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.2 | 38.5 |
| Released | 2026-09-22 | 2025-04-28 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 41K |
| Max output | 128K | 16K |
| Input $ / M tokens | $2 | $0.12 |
| Output $ / M tokens | $10 | $0.50 |
| Results tracked | 22 | 32 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 59.6 (#11), Qwen3-30B-A3B: 37.0 (#188)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| LMArena Coding | 1453 | 1416 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1692 | — |
| SciCode | — | 33.3% |
| WeirdML | — | 29.8% |
Agentic & Tool Use GPT-6 Sol leads
GPT-6 Sol: 33.5 (#49), Qwen3-30B-A3B: 29.5 (#75)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |
| GDP.pdf | 28% | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 49.3 (#34), Qwen3-30B-A3B: 21.6 (#193)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| LMArena Hard Prompts | 1420 | 1398 |
| Kagi LLM Benchmark | — | 54.9% |
| NYT Connections (extended) | 90.1% | — |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 8% |
| EBR-Bench | 53.3% | — |
| DTBench | — | 69.3% |
| LMCA | — | 22.4% |
| Epoch Capabilities Index | — | 139.63 |
Math Too close to call
GPT-6 Sol: 35.8 (#135), Qwen3-30B-A3B: 36.0 (#133)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| LMArena Math | 1404 | 1394 |
| MathArena Final-Answer Competitions | — | 47.8% |
| OTIS Mock AIME 2024-2025 | — | 70.3% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 43.0 (#87), Qwen3-30B-A3B: 41.7 (#96)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| LMArena Expert | 1449 | 1395 |
| GPQA Diamond | — | 70.1% |
| Confabulations | — | 12.3% |
| Vectara Hallucination Rate | 6.5% | — |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#9), Qwen3-30B-A3B: —
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 51.0 (#113), Qwen3-30B-A3B: 49.8 (#126)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1389 | 1372 |
| LMArena Chinese | 1416 | 1434 |
| LMArena Russian | 1407 | 1369 |
| LMArena Spanish | 1377 | 1403 |
| LMArena French | — | 1419 |
| LMArena German | — | 1379 |
| LMArena Japanese | — | 1338 |
| LMArena Korean | — | 1333 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.4 (#97), Qwen3-30B-A3B: 72.2 (#137)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1364 |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.2 (#100), Qwen3-30B-A3B: 31.0 (#278)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1415 | 1379 |
| Fiction.LiveBench | — | 40.6% |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 72.3 (#15), Qwen3-30B-A3B: 55.9 (#138)
| Benchmark | GPT-6 Sol | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1396 | 1384 |
| LMArena Creative Writing | 1392 | 1318 |
| LMArena Multi-Turn | 1405 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
| EQ-Bench Creative Writing | 2125 | — |
Frequently asked questions
Is GPT-6 Sol better than Qwen3-30B-A3B?
GPT-6 Sol is the stronger model overall, scoring 49.2 to 38.5 on the Noometry Index. Qwen3-30B-A3B costs 19× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Sol 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-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Qwen3-30B-A3B better for coding?
GPT-6 Sol scores higher on coding benchmarks: 59.6 versus 37.0 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 41K.
How many benchmarks do GPT-6 Sol and Qwen3-30B-A3B share?
13 benchmarks have published results for both models. GPT-6 Sol has 22 scored results on Noometry and Qwen3-30B-A3B has 32.