Model comparison
GPT-4o vs Qwen3.5 27B
Qwen3.5 27B is the stronger model overall, scoring 41.4 to 28.5 on the Noometry Index.
Last verified . 22 shared benchmarks.
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. GPT-4o scores higher in 0 categories and Qwen3.5 27B in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.5 27B leads 36.8 to 10.2.
- The biggest single-benchmark swing is DTBench: 64.5% for GPT-4o and 82.4% for Qwen3.5 27B.
- Qwen3.5 27B is cheaper at $0.30 / $2.40 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- Qwen3.5 27B accepts more context: 262K tokens versus 128K.
- Qwen3.5 27B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Qwen3.5 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 28.5 | 41.4 |
| Released | 2024-05-13 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 128K | 262K |
| Max output | 16K | 66K |
| Input $ / M tokens | $2.50 | $0.30 |
| Output $ / M tokens | $10 | $2.40 |
| Results tracked | 72 | 28 |
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Category by category
Coding Qwen3.5 27B leads
GPT-4o: 25.1 (#323), Qwen3.5 27B: 38.6 (#159)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| WeirdML | 25.1% | 39.5% |
| LMArena Coding | 1297 | 1427 |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| LMArena WebDev | — | 1357 |
| GSO | 0% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| ALE-Bench | — | 349.45 |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o: 20.8 (#133), Qwen3.5 27B: —
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
| Vending-Bench 2 | — | 201.98 |
Reasoning Qwen3.5 27B leads
GPT-4o: 9.4 (#338), Qwen3.5 27B: 26.6 (#116)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| LMArena Hard Prompts | 1281 | 1414 |
| DTBench | 64.5% | 82.4% |
| LMCA | 16.6% | 34% |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| NYT Connections (extended) | — | 47.9% |
| ARC-AGI-1 | 4.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | 13% | — |
| EnigmaEval | 0.8% | — |
| Thematic Generalization | — | 45.5% |
| LiveBench Reasoning | 55.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| Epoch Capabilities Index | 128.97 | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math Qwen3.5 27B leads
GPT-4o: 10.2 (#303), Qwen3.5 27B: 36.8 (#103)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| LMArena Math | 1285 | 1428 |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| MathArena Final-Answer Competitions | — | 56.7% |
| OTIS Mock AIME 2024-2025 | 6.4% | — |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Qwen3.5 27B leads
GPT-4o: 28.8 (#235), Qwen3.5 27B: 37.4 (#149)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| Vectara Hallucination Rate | 9.6% | 12.1% |
| LMArena Expert | 1250 | 1428 |
| GPQA Diamond | 49.2% | — |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| Confabulations | 15.3% | — |
| GPQA (HELM) | 52% | — |
| MMLU | 88.1% | — |
Multimodal Qwen3.5 27B leads
GPT-4o: 33.9 (#85), Qwen3.5 27B: 38.7 (#56)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | 1137 | 1240 |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual Qwen3.5 27B leads
GPT-4o: 43.4 (#181), Qwen3.5 27B: 51.0 (#112)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | 1283 | 1390 |
| LMArena Chinese | 1277 | 1478 |
| LMArena French | 1303 | 1409 |
| LMArena German | 1282 | 1393 |
| LMArena Japanese | 1257 | 1345 |
| LMArena Korean | 1234 | 1359 |
| LMArena Russian | 1286 | 1389 |
| LMArena Spanish | 1292 | 1408 |
Instruction Following Qwen3.5 27B leads
GPT-4o: 66.8 (#202), Qwen3.5 27B: 73.7 (#114)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | 1278 | 1393 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
Long Context Qwen3.5 27B leads
GPT-4o: 39.4 (#174), Qwen3.5 27B: 43.1 (#103)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | 1289 | 1413 |
| Fiction.LiveBench | 66.7% | — |
Writing & Preference Qwen3.5 27B leads
GPT-4o: 52.8 (#160), Qwen3.5 27B: 59.5 (#106)
| Benchmark | GPT-4o | Qwen3.5 27B |
|---|---|---|
| LMArena Text | 1300 | 1409 |
| LMArena Creative Writing | 1291 | 1362 |
| LMArena Multi-Turn | 1302 | 1410 |
| Short-Story Creative Writing | 81.8% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Qwen3.5 27B?
Qwen3.5 27B is the stronger model overall, scoring 41.4 to 28.5 on the Noometry Index.
Which is cheaper, GPT-4o or Qwen3.5 27B?
Qwen3.5 27B is cheaper. It lists at $0.30 per million input tokens and $2.40 per million output tokens; GPT-4o lists at $2.50 and $10.
Is GPT-4o or Qwen3.5 27B better for coding?
Qwen3.5 27B scores higher on coding benchmarks: 38.6 versus 25.1 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 27B does, with 262K tokens against 128K.
How many benchmarks do GPT-4o and Qwen3.5 27B share?
22 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Qwen3.5 27B has 28.