Model comparison
GPT-5 vs Qwen3.5-Flash
GPT-5 is the stronger model overall, scoring 50.9 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 20× less per token, which makes it the better buy when GPT-5'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 scores higher in 8 categories and Qwen3.5-Flash in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 42.4.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 55.4% for GPT-5 and 18.2% 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.
- Qwen3.5-Flash accepts more context: 1M tokens versus 400K.
Side by side
| GPT-5 | Qwen3.5-Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 50.9 | 42.5 |
| Released | 2025-08-07 | 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 | 69 | 32 |
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Category by category
Coding GPT-5 leads
GPT-5: 50.3 (#47), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena WebDev | 1418 | 1244 |
| LMArena Coding | 1436 | 1412 |
| ALE-Bench | 1,162 | 221.8 |
| SWE-bench Verified | 73.6% | — |
| SWE-bench Verified (bash only) | 65% | — |
| Aider Polyglot | 88% | — |
| SciCode | 42.9% | — |
| GSO | 6.9% | — |
| WeirdML | 60.7% | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use Not comparable
GPT-5: 33.1 (#56), Qwen3.5-Flash: —
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| Terminal-Bench | 49.6% | — |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning GPT-5 leads
GPT-5: 38.3 (#64), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 37% | 21% |
| LMArena Hard Prompts | 1416 | 1403 |
| Mystery Game Puzzles | 23% | 20% |
| DTBench | 90.7% | 82.9% |
| LMCA | 40% | 29.1% |
| Epoch Capabilities Index | 150 | 143.98 |
| ARC-AGI-2 | 9.9% | — |
| SimpleBench | 56.7% | — |
| Kagi LLM Benchmark | 72.7% | — |
| ARC-AGI-1 | 65.7% | — |
| CritPt | 12.6% | — |
| EnigmaEval | 10.5% | — |
| EBR-Bench | 12.7% | — |
| ForecastBench | 61.4 | — |
Math GPT-5 leads
GPT-5: 55.0 (#44), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.4% | 18.2% |
| OTIS Mock AIME 2024-2025 | 91.4% | 84.4% |
| LMArena Math | 1407 | 1407 |
| FrontierMath (Feb 2025 set) | 32.4% | 6.2% |
| FrontierMath Tier 4 (v1) | 12.5% | 0% |
| FrontierMath Tier 4 | 22% | — |
| ProofBench | 18% | — |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
Knowledge GPT-5 leads
GPT-5: 56.6 (#43), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 86.2% | 82.3% |
| SimpleQA Verified | 50.1% | 20.3% |
| Vectara Hallucination Rate | 14.7% | 10.5% |
| LMArena Expert | 1419 | 1407 |
| Humanity's Last Exam | 25.3% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal Not comparable
GPT-5: 46.8 (#13), Qwen3.5-Flash: —
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1232 | — |
| GeoBench | 81% | — |
| VPCT | 66% | — |
Multilingual Too close to call
GPT-5: 51.4 (#110), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1397 | 1385 |
| LMArena Chinese | 1422 | 1446 |
| LMArena French | 1410 | 1412 |
| LMArena German | 1416 | 1390 |
| LMArena Japanese | 1409 | 1368 |
| LMArena Korean | 1360 | 1344 |
| LMArena Russian | 1406 | 1379 |
| LMArena Spanish | 1399 | 1400 |
Instruction Following GPT-5 leads
GPT-5: 73.8 (#113), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1388 | 1374 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1399 | 1392 |
| Fiction.LiveBench | 97.2% | — |
Writing & Preference GPT-5 leads
GPT-5: 63.4 (#65), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | GPT-5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1406 | 1397 |
| LMArena Creative Writing | 1365 | 1343 |
| LMArena Multi-Turn | 1426 | 1393 |
| Short-Story Creative Writing | 86% | — |
| EQ-Bench Creative Writing | 1627 | — |
| WildBench | 85.7% | — |
Frequently asked questions
Is GPT-5 better than Qwen3.5-Flash?
GPT-5 is the stronger model overall, scoring 50.9 to 42.5 on the Noometry Index. Qwen3.5-Flash costs 20× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GPT-5 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 lists at $1.25 and $10.
Is GPT-5 or Qwen3.5-Flash better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 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 and Qwen3.5-Flash share?
31 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and Qwen3.5-Flash has 32.