Model comparison
GPT-6 Sol vs Qwen3-VL 235B-A22B
GPT-6 Sol is the stronger model overall, scoring 61.8 to 43.2 on the Noometry Index. Qwen3-VL 235B-A22B costs 3.3× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. GPT-6 Sol scores higher in 7 categories and Qwen3-VL 235B-A22B in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 39.0.
- Qwen3-VL 235B-A22B is cheaper at $0.70 / $2.80 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 131K.
- Qwen3-VL 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Qwen3-VL 235B-A22B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 61.8 | 43.2 |
| Released | 2026-09-22 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 33K |
| Input $ / M tokens | $2 | $0.70 |
| Output $ / M tokens | $10 | $2.80 |
| Results tracked | 45 | 18 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Qwen3-VL 235B-A22B: 42.4 (#100)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Coding | 1447 | 1439 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| SciCode | 57.6% | — |
| ALE-Bench | 2,462 | — |
Agentic & Tool Use Not comparable
GPT-6 Sol: 37.2 (#36), Qwen3-VL 235B-A22B: —
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| APEX-Agents | 54.3% | — |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Qwen3-VL 235B-A22B: 29.3 (#92)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1428 |
| ARC-AGI-2 | 89.6% | — |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
| DTBench | 97.3% | — |
| LMCA | 59.1% | — |
| Epoch Capabilities Index | 162.72 | — |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Qwen3-VL 235B-A22B: 39.0 (#118)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Math | 1402 | 1426 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Qwen3-VL 235B-A22B: 40.3 (#121)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Expert | 1439 | 1442 |
| GPQA Diamond | 94.3% | — |
| SimpleQA Verified | 60.7% | — |
| Vectara Hallucination Rate | 6.5% | — |
Multimodal GPT-6 Sol leads
GPT-6 Sol: 47.6 (#10), Qwen3-VL 235B-A22B: 39.8 (#55)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Vision | 1245 | 1247 |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual Qwen3-VL 235B-A22B leads
GPT-6 Sol: 50.5 (#118), Qwen3-VL 235B-A22B: 51.9 (#97)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Non-English | 1385 | 1405 |
| LMArena Chinese | 1405 | 1463 |
| LMArena French | 1410 | 1452 |
| LMArena German | 1390 | 1424 |
| LMArena Japanese | 1385 | 1385 |
| LMArena Korean | 1341 | 1394 |
| LMArena Russian | 1401 | 1408 |
| LMArena Spanish | 1384 | 1428 |
Instruction Following Too close to call
GPT-6 Sol: 74.5 (#94), Qwen3-VL 235B-A22B: 74.2 (#101)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1412 | 1406 |
Long Context Too close to call
GPT-6 Sol: 43.1 (#108), Qwen3-VL 235B-A22B: 43.4 (#98)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1411 | 1420 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Qwen3-VL 235B-A22B: 60.2 (#99)
| Benchmark | GPT-6 Sol | Qwen3-VL 235B-A22B |
|---|---|---|
| LMArena Text | 1395 | 1420 |
| LMArena Creative Writing | 1378 | 1366 |
| LMArena Multi-Turn | 1412 | 1428 |
| EQ-Bench Creative Writing | 2125 | — |
Frequently asked questions
Is GPT-6 Sol better than Qwen3-VL 235B-A22B?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 43.2 on the Noometry Index. Qwen3-VL 235B-A22B costs 3.3× 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-VL 235B-A22B?
Qwen3-VL 235B-A22B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Qwen3-VL 235B-A22B better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6 Sol and Qwen3-VL 235B-A22B share?
18 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Qwen3-VL 235B-A22B has 18.