Model comparison
GPT-6 Sol vs Qwen3.8 27B
GPT-6 Sol is the stronger model overall, scoring 49.2 to 45.3 on the Noometry Index. Qwen3.8 27B costs 3.6× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
Alibaba (Qwen)Summary
- They share 16 benchmarks with published results for both. GPT-6 Sol scores higher in 6 categories and Qwen3.8 27B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 31.5.
- The biggest single-benchmark swing is NYT Connections (extended): 90.1% for GPT-6 Sol and 54.5% for Qwen3.8 27B.
- Qwen3.8 27B is cheaper at $0.99 / $1.49 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 262K.
- Qwen3.8 27B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Qwen3.8 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.2 | 45.3 |
| Released | 2026-09-22 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 33K |
| Input $ / M tokens | $2 | $0.99 |
| Output $ / M tokens | $10 | $1.49 |
| Results tracked | 22 | 28 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 59.6 (#11), Qwen3.8 27B: 50.3 (#42)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| LMArena WebDev | 1692 | 1592 |
| LMArena Coding | 1453 | 1487 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| SciCode | — | 46.6% |
Agentic & Tool Use Not comparable
GPT-6 Sol: 33.5 (#49), Qwen3.8 27B: —
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| GDP.pdf | 28% | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 49.3 (#34), Qwen3.8 27B: 31.5 (#71)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| NYT Connections (extended) | 90.1% | 54.5% |
| LMArena Hard Prompts | 1420 | 1459 |
| CritPt | — | 5.4% |
| EBR-Bench | 53.3% | — |
| DTBench | — | 88% |
| LMCA | — | 41.4% |
| Surface Evolver Bench | — | 45% |
| Epoch Capabilities Index | — | 149.38 |
Math Too close to call
GPT-6 Sol: 35.8 (#135), Qwen3.8 27B: 35.7 (#139)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1404 | 1453 |
| ProofBench | — | 16% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 43.0 (#87), Qwen3.8 27B: 40.9 (#105)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1449 | 1482 |
| Vectara Hallucination Rate | 6.5% | — |
Multimodal GPT-6 Sol leads
GPT-6 Sol: 47.6 (#9), Qwen3.8 27B: 40.5 (#36)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1270 |
| Furniture Assembly | 58.3% | — |
Multilingual Qwen3.8 27B leads
GPT-6 Sol: 51.0 (#113), Qwen3.8 27B: 54.0 (#54)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1389 | 1431 |
| LMArena Chinese | 1416 | 1504 |
| LMArena Russian | 1407 | 1421 |
| LMArena Spanish | 1377 | 1444 |
| LMArena French | — | 1468 |
| LMArena German | — | 1435 |
| LMArena Japanese | — | 1392 |
| LMArena Korean | — | 1389 |
Instruction Following Qwen3.8 27B leads
GPT-6 Sol: 74.4 (#97), Qwen3.8 27B: 75.8 (#54)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1436 |
Long Context Qwen3.8 27B leads
GPT-6 Sol: 43.2 (#100), Qwen3.8 27B: 44.3 (#66)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1415 | 1448 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 72.3 (#15), Qwen3.8 27B: 65.9 (#41)
| Benchmark | GPT-6 Sol | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1396 | 1441 |
| LMArena Creative Writing | 1392 | 1382 |
| EQ-Bench Creative Writing | 2125 | 1671 |
| LMArena Multi-Turn | 1405 | 1443 |
Frequently asked questions
Is GPT-6 Sol better than Qwen3.8 27B?
GPT-6 Sol is the stronger model overall, scoring 49.2 to 45.3 on the Noometry Index. Qwen3.8 27B costs 3.6× 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 Qwen3.8 27B?
Qwen3.8 27B is cheaper. It lists at $0.99 per million input tokens and $1.49 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Qwen3.8 27B better for coding?
GPT-6 Sol scores higher on coding benchmarks: 59.6 versus 50.3 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 Qwen3.8 27B share?
16 benchmarks have published results for both models. GPT-6 Sol has 22 scored results on Noometry and Qwen3.8 27B has 28.