Model comparison
GPT-5.4 nano vs Qwen2.5-Coder-32B
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 33.4 on the Noometry Index.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. GPT-5.4 nano scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GPT-5.4 nano leads 43.6 to 22.6.
- GPT-5.4 nano is cheaper at $0.20 / $1.25 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
- GPT-5.4 nano accepts more context: 400K tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 nano | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.9 | 33.4 |
| Released | 2026-03-17 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 400K | 33K |
| Max output | 128K | 29K |
| Input $ / M tokens | $0.20 | $0.66 |
| Output $ / M tokens | $1.25 | $1 |
| Results tracked | 40 | 31 |
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Category by category
Coding GPT-5.4 nano leads
GPT-5.4 nano: 43.6 (#84), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1405 | 1276 |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| SciCode | 46.9% | — |
| WeirdML | 49.2% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 1,005 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Reasoning GPT-5.4 nano leads
GPT-5.4 nano: 23.7 (#173), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1381 | 1251 |
| Epoch Capabilities Index | 145.81 | 119.49 |
| ARC-AGI-2 | 5.7% | — |
| Kagi LLM Benchmark | 39.7% | — |
| ARC-AGI-1 | 51.5% | — |
| CritPt | 9.3% | — |
| Chess Puzzles | 30% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 9% | — |
| DTBench | 80.3% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 36.9% | — |
| ForecastBench | 57.3 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math GPT-5.4 nano leads
GPT-5.4 nano: 40.9 (#88), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1406 | 1251 |
| FrontierMath (Tiers 1-3) | 44.9% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 5% | — |
| LiveBench Math | — | 46.6% |
| FrontierMath (Feb 2025 set) | 25.9% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
| GSM8K | — | 93% |
Knowledge GPT-5.4 nano leads
GPT-5.4 nano: 41.9 (#103), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1396 | 1221 |
| GPQA Diamond | 78.5% | — |
| SimpleQA Verified | 11.7% | — |
| Vectara Hallucination Rate | 3.1% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
GPT-5.4 nano: 36.7 (#78), Qwen2.5-Coder-32B: —
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1196 | — |
Multilingual GPT-5.4 nano leads
GPT-5.4 nano: 48.6 (#140), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1359 | 1205 |
| LMArena Chinese | 1392 | 1222 |
| LMArena Russian | 1363 | 1228 |
| LMArena French | 1396 | — |
| LMArena German | 1367 | — |
| LMArena Japanese | 1343 | — |
| LMArena Korean | 1320 | — |
| LMArena Spanish | 1371 | — |
Instruction Following GPT-5.4 nano leads
GPT-5.4 nano: 71.9 (#144), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1362 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context GPT-5.4 nano leads
GPT-5.4 nano: 41.6 (#137), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1366 | 1251 |
Writing & Preference GPT-5.4 nano leads
GPT-5.4 nano: 55.7 (#142), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | GPT-5.4 nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1372 | 1230 |
| LMArena Creative Writing | 1314 | 1174 |
| LMArena Multi-Turn | 1382 | 1222 |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is GPT-5.4 nano better than Qwen2.5-Coder-32B?
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 33.4 on the Noometry Index.
Which is cheaper, GPT-5.4 nano or Qwen2.5-Coder-32B?
GPT-5.4 nano is cheaper. It lists at $0.20 per million input tokens and $1.25 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.
Is GPT-5.4 nano or Qwen2.5-Coder-32B better for coding?
GPT-5.4 nano scores higher on coding benchmarks: 43.6 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 nano does, with 400K tokens against 33K.
How many benchmarks do GPT-5.4 nano and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. GPT-5.4 nano has 40 scored results on Noometry and Qwen2.5-Coder-32B has 31.