Model comparison
GPT-5 Nano vs Qwen2.5-Coder-32B
GPT-5 Nano and Qwen2.5-Coder-32B score almost the same on the Noometry Index (33.5 vs 33.4), so choose on price, context window or the category you care about most.
Last verified . 14 shared benchmarks.
Alibaba (Qwen)Summary
- They share 14 benchmarks with published results for both. GPT-5 Nano scores higher in 4 categories and Qwen2.5-Coder-32B in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in instruction following, where GPT-5 Nano leads 75.0 to 61.4.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 34.8% for GPT-5 Nano and 9% for Qwen2.5-Coder-32B.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
- GPT-5 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 Nano | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.5 | 33.4 |
| Released | 2025-08-07 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 400K | 33K |
| Max output | 128K | 29K |
| Input $ / M tokens | $0.05 | $0.66 |
| Output $ / M tokens | $0.40 | $1 |
| Results tracked | 49 | 31 |
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Category by category
Coding GPT-5 Nano leads
GPT-5 Nano: 33.6 (#254), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| SWE-bench Verified (bash only) | 34.8% | 9% |
| LMArena Coding | 1351 | 1276 |
| Aider Polyglot | — | 16.4% |
| WeirdML | 38.1% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 718.67 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
GPT-5 Nano: 25.8 (#106), Qwen2.5-Coder-32B: —
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| Berkeley Function Calling Leaderboard | 51.5% | — |
Reasoning Qwen2.5-Coder-32B leads
GPT-5 Nano: 16.3 (#306), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1328 | 1251 |
| Epoch Capabilities Index | 139.38 | 119.49 |
| ARC-AGI-2 | 2.6% | — |
| Kagi LLM Benchmark | 62.2% | — |
| ARC-AGI-1 | 20.7% | — |
| Chess Puzzles | 27% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 9% | — |
| DTBench | 62.7% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 7.9% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Qwen2.5-Coder-32B leads
GPT-5 Nano: 29.4 (#241), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1317 | 1251 |
| FrontierMath (Tiers 1-3) | 20% | — |
| FrontierMath Tier 4 | 2.4% | — |
| OTIS Mock AIME 2024-2025 | 81.1% | — |
| ProofBench | 12% | — |
| Omni-MATH | 54.6% | — |
| LiveBench Math | — | 46.6% |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 93% |
Knowledge GPT-5 Nano leads
GPT-5 Nano: 35.9 (#178), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1321 | 1221 |
| GPQA Diamond | 69.4% | — |
| SimpleQA Verified | 11.7% | — |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
GPT-5 Nano: 31.3 (#108), Qwen2.5-Coder-32B: —
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1159 | — |
| VPCT | 37.2% | — |
Multilingual GPT-5 Nano leads
GPT-5 Nano: 45.3 (#172), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1313 | 1205 |
| LMArena Chinese | 1356 | 1222 |
| LMArena Russian | 1296 | 1228 |
| LMArena German | 1327 | — |
| LMArena Japanese | 1226 | — |
| LMArena Korean | 1269 | — |
| LMArena Spanish | 1360 | — |
Instruction Following GPT-5 Nano leads
GPT-5 Nano: 75.0 (#79), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1306 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
| IFEval | 93.2% | — |
Long Context Qwen2.5-Coder-32B leads
GPT-5 Nano: 31.3 (#281), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1312 | 1251 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Qwen2.5-Coder-32B leads
GPT-5 Nano: 39.1 (#249), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | GPT-5 Nano | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1320 | 1230 |
| LMArena Creative Writing | 1249 | 1174 |
| LMArena Multi-Turn | 1311 | 1222 |
| EQ-Bench Creative Writing | 705 | — |
| WildBench | 80.6% | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is GPT-5 Nano better than Qwen2.5-Coder-32B?
GPT-5 Nano and Qwen2.5-Coder-32B score almost the same on the Noometry Index (33.5 vs 33.4), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-5 Nano or Qwen2.5-Coder-32B?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.
Is GPT-5 Nano or Qwen2.5-Coder-32B better for coding?
GPT-5 Nano scores higher on coding benchmarks: 33.6 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 33K.
How many benchmarks do GPT-5 Nano and Qwen2.5-Coder-32B share?
14 benchmarks have published results for both models. GPT-5 Nano has 49 scored results on Noometry and Qwen2.5-Coder-32B has 31.