Model comparison
GPT-4.5 vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 37.2 on the Noometry Index.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GPT-4.5 scores higher in 3 categories and Qwen3.5-Flash in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5-Flash leads 33.7 to 13.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for GPT-4.5 and 84.4% for Qwen3.5-Flash.
Side by side
| GPT-4.5 | Qwen3.5-Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 37.2 | 42.5 |
| Released | 2025-02-27 | 2026-02-23 |
| Weights | Proprietary | Proprietary |
| Context window | — | 1M |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.40 |
| Results tracked | 42 | 32 |
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Category by category
Coding GPT-4.5 leads
GPT-4.5: 42.2 (#109), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1396 | 1412 |
| Aider Polyglot | 44.9% | — |
| LMArena WebDev | — | 1244 |
| WeirdML | 39.4% | — |
| LiveBench Coding | 75.2% | — |
| ALE-Bench | — | 221.8 |
Agentic & Tool Use Not comparable
GPT-4.5: 27.9 (#97), Qwen3.5-Flash: —
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| Cybench | 17.5% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
GPT-4.5: 13.9 (#330), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1403 | 1403 |
| Epoch Capabilities Index | 136.74 | 143.98 |
| ARC-AGI-2 | 0.8% | — |
| SimpleBench | 34.5% | — |
| ARC-AGI-1 | 10.3% | — |
| Chess Puzzles | — | 21% |
| EnigmaEval | 3.2% | — |
| LiveBench Reasoning | 71.1% | — |
| Mystery Game Puzzles | — | 20% |
| DTBench | — | 82.9% |
| LiveBench Data Analysis | 64.3% | — |
| LMCA | — | 29.1% |
| ForecastBench | 61.7 | — |
| LiveBench | 69% | — |
Math Qwen3.5-Flash leads
GPT-4.5: 32.6 (#211), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 84.4% |
| LMArena Math | 1412 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| LiveBench Math | 69.3% | — |
| MATH Level 5 | 78.6% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
GPT-4.5: 32.5 (#211), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 68.7% | 82.3% |
| LMArena Expert | 1394 | 1407 |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | — | 20.3% |
| Confabulations | 13.6% | — |
| Vectara Hallucination Rate | — | 10.5% |
Multimodal Not comparable
GPT-4.5: 37.6 (#71), Qwen3.5-Flash: —
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1195 | — |
| VPCT | 45% | — |
Multilingual GPT-4.5 leads
GPT-4.5: 52.5 (#83), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1413 | 1385 |
| LMArena Chinese | 1421 | 1446 |
| LMArena French | 1418 | 1412 |
| LMArena German | 1457 | 1390 |
| LMArena Japanese | 1416 | 1368 |
| LMArena Korean | 1392 | 1344 |
| LMArena Russian | 1419 | 1379 |
| LMArena Spanish | — | 1400 |
Instruction Following Too close to call
GPT-4.5: 72.6 (#134), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1404 | 1374 |
| LiveBench Instruction Following | 72.3% | — |
Long Context Qwen3.5-Flash leads
GPT-4.5: 40.4 (#155), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1406 | 1392 |
| Fiction.LiveBench | 63.9% | — |
Writing & Preference Qwen3.5-Flash leads
GPT-4.5: 56.9 (#134), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | GPT-4.5 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1417 | 1397 |
| LMArena Creative Writing | 1394 | 1343 |
| LMArena Multi-Turn | 1444 | 1393 |
| Short-Story Creative Writing | 75.6% | — |
| EQ-Bench Creative Writing | 1258 | — |
| LiveBench Language | 61.5% | — |
Frequently asked questions
Is GPT-4.5 better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 37.2 on the Noometry Index.
Is GPT-4.5 or Qwen3.5-Flash better for coding?
GPT-4.5 scores higher on coding benchmarks: 42.2 versus 34.2 in the Noometry coding category.
How many benchmarks do GPT-4.5 and Qwen3.5-Flash share?
19 benchmarks have published results for both models. GPT-4.5 has 42 scored results on Noometry and Qwen3.5-Flash has 32.