Model comparison
GPT-6 Sol vs Qwen3-Coder 480B-A35B Instruct
GPT-6 Sol is the stronger model overall, scoring 49.2 to 37.7 on the Noometry Index.
Last verified . 14 shared benchmarks.
Alibaba (Qwen)Summary
- They share 14 benchmarks with published results for both. GPT-6 Sol scores higher in 9 categories and Qwen3-Coder 480B-A35B Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 24.7.
- Qwen3-Coder 480B-A35B Instruct is cheaper at $1.50 / $7.50 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 262K.
- Qwen3-Coder 480B-A35B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.2 | 37.7 |
| Released | 2026-09-22 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $2 | $1.50 |
| Output $ / M tokens | $10 | $7.50 |
| Results tracked | 22 | 25 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 59.6 (#11), Qwen3-Coder 480B-A35B Instruct: 36.2 (#199)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1692 | 1275 |
| LMArena Coding | 1453 | 1413 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| GSO | — | 4.9% |
| WeirdML | — | 41.2% |
| ALE-Bench | — | 461.45 |
| AlgoTune | — | 1.44 |
Agentic & Tool Use GPT-6 Sol leads
GPT-6 Sol: 33.5 (#49), Qwen3-Coder 480B-A35B Instruct: 23.7 (#116)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| GDP.pdf | 28% | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 49.3 (#34), Qwen3-Coder 480B-A35B Instruct: 24.7 (#141)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1420 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| NYT Connections (extended) | 90.1% | — |
| EBR-Bench | 53.3% | — |
Math Too close to call
GPT-6 Sol: 35.8 (#135), Qwen3-Coder 480B-A35B Instruct: 34.9 (#151)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1404 | 1364 |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 43.0 (#87), Qwen3-Coder 480B-A35B Instruct: 36.4 (#159)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1449 | 1337 |
| Vectara Hallucination Rate | 6.5% | — |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#9), Qwen3-Coder 480B-A35B Instruct: —
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 51.0 (#113), Qwen3-Coder 480B-A35B Instruct: 47.8 (#143)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1389 | 1345 |
| LMArena Chinese | 1416 | 1356 |
| LMArena Russian | 1407 | 1367 |
| LMArena Spanish | 1377 | 1360 |
| LMArena French | — | 1394 |
| LMArena German | — | 1324 |
| LMArena Japanese | — | 1309 |
| LMArena Korean | — | 1307 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.4 (#97), Qwen3-Coder 480B-A35B Instruct: 71.8 (#142)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1406 | 1356 |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.2 (#100), Qwen3-Coder 480B-A35B Instruct: 42.0 (#126)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1415 | 1379 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 72.3 (#15), Qwen3-Coder 480B-A35B Instruct: 55.5 (#142)
| Benchmark | GPT-6 Sol | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1396 | 1356 |
| LMArena Creative Writing | 1392 | 1333 |
| LMArena Multi-Turn | 1405 | 1365 |
| EQ-Bench Creative Writing | 2125 | — |
Frequently asked questions
Is GPT-6 Sol better than Qwen3-Coder 480B-A35B Instruct?
GPT-6 Sol is the stronger model overall, scoring 49.2 to 37.7 on the Noometry Index.
Which is cheaper, GPT-6 Sol or Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is cheaper. It lists at $1.50 per million input tokens and $7.50 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Qwen3-Coder 480B-A35B Instruct better for coding?
GPT-6 Sol scores higher on coding benchmarks: 59.6 versus 36.2 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 262K.
How many benchmarks do GPT-6 Sol and Qwen3-Coder 480B-A35B Instruct share?
14 benchmarks have published results for both models. GPT-6 Sol has 22 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.