Model comparison
GPT-6 Sol vs Qwen3-1.7B
GPT-6 Sol is the stronger model overall, scoring 49.2 to 26.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 18.5.
- Qwen3-1.7B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Qwen3-1.7B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.2 | 26.2 |
| Released | 2026-09-22 | 2025-04-29 |
| Weights | Proprietary | Open |
| Context window | 1.05M | — |
| Max output | 128K | — |
| Input $ / M tokens | $2 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 22 | 4 |
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Category by category
Coding Not comparable
GPT-6 Sol: 59.6 (#11), Qwen3-1.7B: —
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1692 | — |
| LMArena Coding | 1453 | — |
Agentic & Tool Use GPT-6 Sol leads
GPT-6 Sol: 33.5 (#49), Qwen3-1.7B: 24.5 (#108)
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.4% |
| GDP.pdf | 28% | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 49.3 (#34), Qwen3-1.7B: 18.5 (#263)
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| NYT Connections (extended) | 90.1% | — |
| Chess Puzzles | — | 0% |
| EBR-Bench | 53.3% | — |
| LMArena Hard Prompts | 1420 | — |
Math GPT-6 Sol leads
GPT-6 Sol: 35.8 (#135), Qwen3-1.7B: 15.4 (#288)
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 8.1% |
| LMArena Math | 1404 | — |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 43.0 (#87), Qwen3-1.7B: 19.6 (#273)
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| GPQA Diamond | — | 38% |
| Vectara Hallucination Rate | 6.5% | — |
| LMArena Expert | 1449 | — |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#9), Qwen3-1.7B: —
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| Furniture Assembly | 58.3% | — |
Multilingual Not comparable
GPT-6 Sol: 51.0 (#113), Qwen3-1.7B: —
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| LMArena Non-English | 1389 | — |
| LMArena Chinese | 1416 | — |
| LMArena Russian | 1407 | — |
| LMArena Spanish | 1377 | — |
Instruction Following Not comparable
GPT-6 Sol: 74.4 (#97), Qwen3-1.7B: —
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| LMArena Instruction Following | 1406 | — |
Long Context Not comparable
GPT-6 Sol: 43.2 (#100), Qwen3-1.7B: —
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| LMArena Longer Query | 1415 | — |
Writing & Preference Not comparable
GPT-6 Sol: 72.3 (#15), Qwen3-1.7B: —
| Benchmark | GPT-6 Sol | Qwen3-1.7B |
|---|---|---|
| LMArena Text | 1396 | — |
| LMArena Creative Writing | 1392 | — |
| EQ-Bench Creative Writing | 2125 | — |
| LMArena Multi-Turn | 1405 | — |
Frequently asked questions
Is GPT-6 Sol better than Qwen3-1.7B?
GPT-6 Sol is the stronger model overall, scoring 49.2 to 26.2 on the Noometry Index.
How many benchmarks do GPT-6 Sol and Qwen3-1.7B share?
0 benchmarks have published results for both models. GPT-6 Sol has 22 scored results on Noometry and Qwen3-1.7B has 4.