Model comparison
GPT-4 Turbo vs Qwen3 32B
Qwen3 32B is the stronger model overall, scoring 38.8 to 30.3 on the Noometry Index.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GPT-4 Turbo scores higher in 0 categories and Qwen3 32B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 32B leads 37.9 to 8.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.7% for GPT-4 Turbo and 66.9% for Qwen3 32B.
- Qwen3 32B is cheaper at $0.70 / $2.80 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
- Qwen3 32B accepts more context: 131K tokens versus 128K.
- Qwen3 32B has downloadable open weights; the other is API-only.
Side by side
| GPT-4 Turbo | Qwen3 32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 30.3 | 38.8 |
| Released | 2023-11-06 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 4K | 16K |
| Input $ / M tokens | $10 | $0.70 |
| Output $ / M tokens | $30 | $2.80 |
| Results tracked | 36 | 26 |
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Category by category
Coding Qwen3 32B leads
GPT-4 Turbo: 33.4 (#247), Qwen3 32B: 37.3 (#184)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| LMArena Coding | 1268 | 1358 |
| Aider Polyglot | — | 40% |
| SciCode | — | 35.4% |
| WeirdML | 18% | — |
| BigCodeBench Instruct | 48.2% | — |
| BigCodeBench Complete | 58.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73.3% | — |
Agentic & Tool Use Not comparable
GPT-4 Turbo: —, Qwen3 32B: 32.3 (#58)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 48.7% |
| METR Time Horizons | 36.7% | — |
Reasoning Qwen3 32B leads
GPT-4 Turbo: 14.9 (#311), Qwen3 32B: 19.6 (#233)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| Chess Puzzles | 6% | 5% |
| LMArena Hard Prompts | 1251 | 1335 |
| DTBench | 61.6% | 67.5% |
| LMCA | 9.8% | 17.3% |
| Epoch Capabilities Index | 127.25 | 138.51 |
| SimpleBench | 25.1% | — |
| Kagi LLM Benchmark | — | 54.9% |
| CritPt | — | 0.3% |
| ForecastBench | 59.4 | — |
Math Qwen3 32B leads
GPT-4 Turbo: 8.6 (#316), Qwen3 32B: 37.9 (#89)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.7% | 66.9% |
| LMArena Math | 1272 | 1399 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| MATH Level 5 | 46.7% | — |
Knowledge Qwen3 32B leads
GPT-4 Turbo: 24.3 (#263), Qwen3 32B: 40.0 (#112)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 46.6% | 65.7% |
| LMArena Expert | 1223 | 1362 |
| Confabulations | 28.4% | — |
| Vectara Hallucination Rate | — | 5.9% |
| MMLU | 81.3% | — |
Multimodal Not comparable
GPT-4 Turbo: 30.0 (#104), Qwen3 32B: —
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| LMArena Vision | 1090 | — |
Multilingual Qwen3 32B leads
GPT-4 Turbo: 40.7 (#211), Qwen3 32B: 45.8 (#162)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1245 | 1317 |
| LMArena Chinese | 1242 | 1357 |
| LMArena German | 1259 | 1341 |
| LMArena Russian | 1259 | 1311 |
| LMArena French | 1275 | — |
| LMArena Japanese | 1194 | — |
| LMArena Korean | 1187 | — |
| LMArena Spanish | 1260 | — |
Instruction Following Qwen3 32B leads
GPT-4 Turbo: 66.0 (#211), Qwen3 32B: 69.1 (#174)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1250 | 1305 |
Long Context Qwen3 32B leads
GPT-4 Turbo: 38.0 (#200), Qwen3 32B: 43.7 (#83)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| LMArena Longer Query | 1254 | 1327 |
| Fiction.LiveBench | — | 74.2% |
Writing & Preference Qwen3 32B leads
GPT-4 Turbo: 47.9 (#201), Qwen3 32B: 53.1 (#158)
| Benchmark | GPT-4 Turbo | Qwen3 32B |
|---|---|---|
| LMArena Text | 1272 | 1340 |
| LMArena Creative Writing | 1269 | 1297 |
| LMArena Multi-Turn | 1267 | 1331 |
Frequently asked questions
Is GPT-4 Turbo better than Qwen3 32B?
Qwen3 32B is the stronger model overall, scoring 38.8 to 30.3 on the Noometry Index.
Which is cheaper, GPT-4 Turbo or Qwen3 32B?
Qwen3 32B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GPT-4 Turbo lists at $10 and $30.
Is GPT-4 Turbo or Qwen3 32B better for coding?
Qwen3 32B scores higher on coding benchmarks: 37.3 versus 33.4 in the Noometry coding category.
Which has the bigger context window?
Qwen3 32B does, with 131K tokens against 128K.
How many benchmarks do GPT-4 Turbo and Qwen3 32B share?
19 benchmarks have published results for both models. GPT-4 Turbo has 36 scored results on Noometry and Qwen3 32B has 26.