Model comparison
GPT-5.1 vs Qwen3.6 27B
GPT-5.1 is the stronger model overall, scoring 49.0 to 42.2 on the Noometry Index. Qwen3.6 27B costs 2.5× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Last verified . 9 shared benchmarks.
Alibaba (Qwen)Summary
- They share 9 benchmarks with published results for both. GPT-5.1 scores higher in 4 categories and Qwen3.6 27B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.1 leads 39.8 to 25.0.
- The biggest single-benchmark swing is DTBench: 90.1% for GPT-5.1 and 78.1% for Qwen3.6 27B.
- Qwen3.6 27B is cheaper at $0.60 / $3.60 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 262K.
- Qwen3.6 27B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1 | Qwen3.6 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.0 | 42.2 |
| Released | 2025-11-13 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $1.25 | $0.60 |
| Output $ / M tokens | $10 | $3.60 |
| Results tracked | 63 | 11 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.4 (#66), Qwen3.6 27B: 39.1 (#163)
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| SciCode | 43.3% | 37.3% |
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| LMArena WebDev | 1395 | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| LiveBench Coding | 72.5% | — |
| LMArena Coding | 1454 | — |
| ALE-Bench | 1,192 | — |
Agentic & Tool Use Not comparable
GPT-5.1: 32.7 (#60), Qwen3.6 27B: —
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| DeepResearch Bench | 42.8% | — |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.8 (#58), Qwen3.6 27B: 25.0 (#153)
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| CritPt | 4.9% | 0.9% |
| Chess Puzzles | 32% | 22% |
| Mystery Game Puzzles | 19% | 7% |
| DTBench | 90.1% | 78.1% |
| LMCA | 43.9% | 34.5% |
| Epoch Capabilities Index | 149.64 | 146.5 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 72.8% | — |
| EnigmaEval | 11.2% | — |
| LiveBench Reasoning | 95.8% | — |
| LMArena Hard Prompts | 1457 | — |
| LiveBench Data Analysis | 72.1% | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |
Math GPT-5.1 leads
GPT-5.1: 52.2 (#51), Qwen3.6 27B: 48.5 (#62)
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.6% | 91.1% |
| FrontierMath (Tiers 1-3) | — | 35.1% |
| Omni-MATH | 46.4% | — |
| LiveBench Math | 94.5% | — |
| LMArena Math | 1447 | — |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge Qwen3.6 27B leads
GPT-5.1: 50.6 (#71), Qwen3.6 27B: 52.4 (#63)
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| GPQA Diamond | 87.6% | 85.9% |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| MMLU-Pro | 57.9% | — |
| Vectara Hallucination Rate | 10.9% | — |
| GPQA (HELM) | 44.2% | — |
| LMArena Expert | 1470 | — |
Multimodal Not comparable
GPT-5.1: 44.8 (#19), Qwen3.6 27B: —
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual Not comparable
GPT-5.1: 53.8 (#56), Qwen3.6 27B: —
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| LMArena Non-English | 1431 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1450 | — |
| LMArena German | 1438 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1401 | — |
| LMArena Russian | 1435 | — |
| LMArena Spanish | 1433 | — |
Instruction Following Not comparable
GPT-5.1: 83.9 (#1), Qwen3.6 27B: —
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| LiveBench Instruction Following | 93.3% | — |
| IFEval | 93.5% | — |
| LMArena Instruction Following | 1443 | — |
Long Context Not comparable
GPT-5.1: 47.6 (#14), Qwen3.6 27B: —
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
| LMArena Longer Query | 1447 | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.5 (#55), Qwen3.6 27B: 50.3 (#181)
| Benchmark | GPT-5.1 | Qwen3.6 27B |
|---|---|---|
| LMArena Text | 1443 | — |
| LMArena Creative Writing | 1427 | — |
| WildBench | 86.3% | — |
| EQ-Bench 4 | — | 1026 |
| LMArena Multi-Turn | 1450 | — |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Qwen3.6 27B?
GPT-5.1 is the stronger model overall, scoring 49.0 to 42.2 on the Noometry Index. Qwen3.6 27B costs 2.5× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Which is cheaper, GPT-5.1 or Qwen3.6 27B?
Qwen3.6 27B is cheaper. It lists at $0.60 per million input tokens and $3.60 per million output tokens; GPT-5.1 lists at $1.25 and $10.
Is GPT-5.1 or Qwen3.6 27B better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.4 versus 39.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1 does, with 400K tokens against 262K.
How many benchmarks do GPT-5.1 and Qwen3.6 27B share?
9 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Qwen3.6 27B has 11.