Model comparison
GPT-6 Sol vs Qwen3.5-Flash
GPT-6 Sol is the stronger model overall, scoring 61.8 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 23× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 29 shared benchmarks.
Alibaba (Qwen)Summary
- They share 29 benchmarks with published results for both. GPT-6 Sol scores higher in 8 categories and Qwen3.5-Flash in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 37.4.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 89.8% for GPT-6 Sol and 18.2% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 1M.
Side by side
| GPT-6 Sol | Qwen3.5-Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 61.8 | 42.5 |
| Released | 2026-09-22 | 2026-02-23 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1M |
| Max output | 128K | 66K |
| Input $ / M tokens | $2 | $0.10 |
| Output $ / M tokens | $10 | $0.40 |
| Results tracked | 45 | 32 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| LMArena WebDev | 1688 | 1244 |
| LMArena Coding | 1447 | 1412 |
| ALE-Bench | 2,462 | 221.8 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| SciCode | 57.6% | — |
Agentic & Tool Use Not comparable
GPT-6 Sol: 37.2 (#36), Qwen3.5-Flash: —
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | 14,428 | 462.69 |
| APEX-Agents | 54.3% | — |
| GDP.pdf | 26.4% | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1403 |
| Mystery Game Puzzles | 56% | 20% |
| DTBench | 97.3% | 82.9% |
| LMCA | 59.1% | 29.1% |
| Epoch Capabilities Index | 162.72 | 143.98 |
| ARC-AGI-2 | 89.6% | — |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| Chess Puzzles | — | 21% |
| EBR-Bench | 53.3% | — |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 89.8% | 18.2% |
| OTIS Mock AIME 2024-2025 | 100% | 84.4% |
| LMArena Math | 1402 | 1407 |
| FrontierMath Tier 4 | 90% | — |
| ProofBench | 83% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 94.3% | 82.3% |
| SimpleQA Verified | 60.7% | 20.3% |
| Vectara Hallucination Rate | 6.5% | 10.5% |
| LMArena Expert | 1439 | 1407 |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Qwen3.5-Flash: —
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual Too close to call
GPT-6 Sol: 50.5 (#118), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1385 | 1385 |
| LMArena Chinese | 1405 | 1446 |
| LMArena French | 1410 | 1412 |
| LMArena German | 1390 | 1390 |
| LMArena Japanese | 1385 | 1368 |
| LMArena Korean | 1341 | 1344 |
| LMArena Russian | 1401 | 1379 |
| LMArena Spanish | 1384 | 1400 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.5 (#94), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1412 | 1374 |
Long Context Too close to call
GPT-6 Sol: 43.1 (#108), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1411 | 1392 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | GPT-6 Sol | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1395 | 1397 |
| LMArena Creative Writing | 1378 | 1343 |
| LMArena Multi-Turn | 1412 | 1393 |
| EQ-Bench Creative Writing | 2125 | — |
Frequently asked questions
Is GPT-6 Sol better than Qwen3.5-Flash?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 23× 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 Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Qwen3.5-Flash better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 34.2 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 1M.
How many benchmarks do GPT-6 Sol and Qwen3.5-Flash share?
29 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Qwen3.5-Flash has 32.