Model comparison
GPT-5.1 vs Qwen3.5-Flash
GPT-5.1 is the stronger model overall, scoring 49.0 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 20× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
Alibaba (Qwen)Summary
- They share 31 benchmarks with published results for both. GPT-5.1 scores higher in 8 categories and Qwen3.5-Flash in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.1 leads 52.2 to 37.4.
- The biggest single-benchmark swing is SimpleQA Verified: 48% for GPT-5.1 and 20.3% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- Qwen3.5-Flash accepts more context: 1M tokens versus 400K.
Side by side
| GPT-5.1 | Qwen3.5-Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 49.0 | 42.5 |
| Released | 2025-11-13 | 2026-02-23 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1M |
| Max output | 128K | 66K |
| Input $ / M tokens | $1.25 | $0.10 |
| Output $ / M tokens | $10 | $0.40 |
| Results tracked | 63 | 32 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.4 (#66), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena WebDev | 1395 | 1244 |
| LMArena Coding | 1454 | 1412 |
| ALE-Bench | 1,192 | 221.8 |
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| SciCode | 43.3% | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| LiveBench Coding | 72.5% | — |
Agentic & Tool Use Not comparable
GPT-5.1: 32.7 (#60), Qwen3.5-Flash: —
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | 1,473 | 462.69 |
| Terminal-Bench | 47.6% | — |
| DeepResearch Bench | 42.8% | — |
| LMArena Search | 1199 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.8 (#58), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 32% | 21% |
| LMArena Hard Prompts | 1457 | 1403 |
| Mystery Game Puzzles | 19% | 20% |
| DTBench | 90.1% | 82.9% |
| LMCA | 43.9% | 29.1% |
| Epoch Capabilities Index | 149.64 | 143.98 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 72.8% | — |
| CritPt | 4.9% | — |
| EnigmaEval | 11.2% | — |
| LiveBench Reasoning | 95.8% | — |
| LiveBench Data Analysis | 72.1% | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |
Math GPT-5.1 leads
GPT-5.1: 52.2 (#51), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.6% | 84.4% |
| LMArena Math | 1447 | 1407 |
| FrontierMath (Feb 2025 set) | 31% | 6.2% |
| FrontierMath Tier 4 (v1) | 12.5% | 0% |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 46.4% | — |
| LiveBench Math | 94.5% | — |
Knowledge GPT-5.1 leads
GPT-5.1: 50.6 (#71), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 87.6% | 82.3% |
| SimpleQA Verified | 48% | 20.3% |
| Vectara Hallucination Rate | 10.9% | 10.5% |
| LMArena Expert | 1470 | 1407 |
| Humanity's Last Exam | 23.7% | — |
| MMLU-Pro | 57.9% | — |
| GPQA (HELM) | 44.2% | — |
Multimodal Not comparable
GPT-5.1: 44.8 (#19), Qwen3.5-Flash: —
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual GPT-5.1 leads
GPT-5.1: 53.8 (#56), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1431 | 1385 |
| LMArena Chinese | 1495 | 1446 |
| LMArena French | 1450 | 1412 |
| LMArena German | 1438 | 1390 |
| LMArena Japanese | 1453 | 1368 |
| LMArena Korean | 1401 | 1344 |
| LMArena Russian | 1435 | 1379 |
| LMArena Spanish | 1433 | 1400 |
Instruction Following GPT-5.1 leads
GPT-5.1: 83.9 (#1), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1443 | 1374 |
| LiveBench Instruction Following | 93.3% | — |
| IFEval | 93.5% | — |
Long Context GPT-5.1 leads
GPT-5.1: 47.6 (#14), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1447 | 1392 |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.5 (#55), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | GPT-5.1 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1443 | 1397 |
| LMArena Creative Writing | 1427 | 1343 |
| LMArena Multi-Turn | 1450 | 1393 |
| WildBench | 86.3% | — |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Qwen3.5-Flash?
GPT-5.1 is the stronger model overall, scoring 49.0 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 20× 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 Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GPT-5.1 lists at $1.25 and $10.
Is GPT-5.1 or Qwen3.5-Flash better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.4 versus 34.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 400K.
How many benchmarks do GPT-5.1 and Qwen3.5-Flash share?
31 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Qwen3.5-Flash has 32.