Model comparison
GPT-4o vs Qwen3.5 35B-A3B
Qwen3.5 35B-A3B is the stronger model overall, scoring 41.7 to 28.5 on the Noometry Index.
Last verified . 25 shared benchmarks.
Alibaba (Qwen)Summary
- They share 25 benchmarks with published results for both. GPT-4o scores higher in 0 categories and Qwen3.5 35B-A3B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.5 35B-A3B leads 38.4 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.4% for GPT-4o and 70% for Qwen3.5 35B-A3B.
- Qwen3.5 35B-A3B is cheaper at $0.25 / $2 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- Qwen3.5 35B-A3B accepts more context: 262K tokens versus 128K.
- Qwen3.5 35B-A3B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Qwen3.5 35B-A3B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 28.5 | 41.7 |
| Released | 2024-05-13 | 2026-02-01 |
| Weights | Proprietary | Open |
| Context window | 128K | 262K |
| Max output | 16K | 66K |
| Input $ / M tokens | $2.50 | $0.25 |
| Output $ / M tokens | $10 | $2 |
| Results tracked | 72 | 28 |
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Category by category
Coding Qwen3.5 35B-A3B leads
GPT-4o: 25.1 (#323), Qwen3.5 35B-A3B: 33.5 (#244)
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Coding | 1297 | 1410 |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| LMArena WebDev | — | 1254 |
| SciCode | — | 29.3% |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o: 20.8 (#133), Qwen3.5 35B-A3B: —
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning Qwen3.5 35B-A3B leads
GPT-4o: 9.4 (#338), Qwen3.5 35B-A3B: 23.9 (#158)
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| CritPt | 0% | 0.6% |
| Chess Puzzles | 13% | 10% |
| LMArena Hard Prompts | 1281 | 1400 |
| DTBench | 64.5% | 80% |
| LMCA | 16.6% | 29.5% |
| Epoch Capabilities Index | 128.97 | 142.52 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| ARC-AGI-1 | 4.5% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math Qwen3.5 35B-A3B leads
GPT-4o: 10.2 (#303), Qwen3.5 35B-A3B: 38.4 (#86)
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.4% | 70% |
| LMArena Math | 1285 | 1404 |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| MathArena Final-Answer Competitions | — | 56% |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Qwen3.5 35B-A3B leads
GPT-4o: 28.8 (#235), Qwen3.5 35B-A3B: 47.7 (#73)
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| GPQA Diamond | 49.2% | 83.5% |
| Vectara Hallucination Rate | 9.6% | 10.5% |
| LMArena Expert | 1250 | 1408 |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| Confabulations | 15.3% | — |
| GPQA (HELM) | 52% | — |
| MMLU | 88.1% | — |
Multimodal Not comparable
GPT-4o: 33.9 (#85), Qwen3.5 35B-A3B: —
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual Qwen3.5 35B-A3B leads
GPT-4o: 43.4 (#181), Qwen3.5 35B-A3B: 50.2 (#122)
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Non-English | 1283 | 1379 |
| LMArena Chinese | 1277 | 1458 |
| LMArena French | 1303 | 1412 |
| LMArena German | 1282 | 1367 |
| LMArena Japanese | 1257 | 1326 |
| LMArena Korean | 1234 | 1354 |
| LMArena Russian | 1286 | 1377 |
| LMArena Spanish | 1292 | 1390 |
Instruction Following Qwen3.5 35B-A3B leads
GPT-4o: 66.8 (#202), Qwen3.5 35B-A3B: 73.0 (#123)
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Instruction Following | 1278 | 1379 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
Long Context Qwen3.5 35B-A3B leads
GPT-4o: 39.4 (#174), Qwen3.5 35B-A3B: 42.4 (#122)
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Longer Query | 1289 | 1390 |
| Fiction.LiveBench | 66.7% | — |
Writing & Preference Qwen3.5 35B-A3B leads
GPT-4o: 52.8 (#160), Qwen3.5 35B-A3B: 58.1 (#118)
| Benchmark | GPT-4o | Qwen3.5 35B-A3B |
|---|---|---|
| LMArena Text | 1300 | 1395 |
| LMArena Creative Writing | 1291 | 1347 |
| LMArena Multi-Turn | 1302 | 1391 |
| Short-Story Creative Writing | 81.8% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Qwen3.5 35B-A3B?
Qwen3.5 35B-A3B is the stronger model overall, scoring 41.7 to 28.5 on the Noometry Index.
Which is cheaper, GPT-4o or Qwen3.5 35B-A3B?
Qwen3.5 35B-A3B is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; GPT-4o lists at $2.50 and $10.
Is GPT-4o or Qwen3.5 35B-A3B better for coding?
Qwen3.5 35B-A3B scores higher on coding benchmarks: 33.5 versus 25.1 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 35B-A3B does, with 262K tokens against 128K.
How many benchmarks do GPT-4o and Qwen3.5 35B-A3B share?
25 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Qwen3.5 35B-A3B has 28.