Model comparison
gpt-oss-20b vs Qwen2.5-Coder-32B
gpt-oss-20b and Qwen2.5-Coder-32B score almost the same on the Noometry Index (32.5 vs 33.4), so choose on price, context window or the category you care about most.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. gpt-oss-20b scores higher in 5 categories and Qwen2.5-Coder-32B in 3 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in coding, where gpt-oss-20b leads 37.6 to 22.6.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
- gpt-oss-20b accepts more context: 131K tokens versus 33K.
Side by side
| gpt-oss-20b | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 32.5 | 33.4 |
| Released | 2025-08-05 | 2024-09-18 |
| Weights | Open | Open |
| Context window | 131K | 33K |
| Max output | 16K | 29K |
| Input $ / M tokens | $0.018 | $0.66 |
| Output $ / M tokens | $0.09 | $1 |
| Results tracked | 34 | 31 |
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Category by category
Coding gpt-oss-20b leads
gpt-oss-20b: 37.6 (#192), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1306 | 1276 |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 566.05 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
gpt-oss-20b: 9.3 (#154), Qwen2.5-Coder-32B: —
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| Terminal-Bench | 3.4% | — |
Reasoning Qwen2.5-Coder-32B leads
gpt-oss-20b: 19.3 (#261), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1274 | 1251 |
| Epoch Capabilities Index | 137.82 | 119.49 |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | 1.4% | — |
| Chess Puzzles | 4% | — |
| LiveBench Reasoning | — | 42.1% |
| DTBench | 68% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 14.5% | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math gpt-oss-20b leads
gpt-oss-20b: 39.4 (#103), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1317 | 1251 |
| OTIS Mock AIME 2024-2025 | 65.3% | — |
| Omni-MATH | 56.5% | — |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |
Knowledge gpt-oss-20b leads
gpt-oss-20b: 34.6 (#195), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1258 | 1221 |
| GPQA Diamond | 60.8% | — |
| MMLU-Pro | 74% | — |
| GPQA (HELM) | 59.4% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multilingual gpt-oss-20b leads
gpt-oss-20b: 42.2 (#197), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1268 | 1205 |
| LMArena Chinese | 1314 | 1222 |
| LMArena Russian | 1278 | 1228 |
| LMArena German | 1255 | — |
| LMArena Japanese | 1244 | — |
| LMArena Korean | 1236 | — |
| LMArena Spanish | 1267 | — |
Instruction Following Too close to call
gpt-oss-20b: 61.8 (#240), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1236 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
| IFEval | 73.2% | — |
Long Context Too close to call
gpt-oss-20b: 37.9 (#209), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1250 | 1251 |
Writing & Preference Qwen2.5-Coder-32B leads
gpt-oss-20b: 35.5 (#265), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | gpt-oss-20b | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1287 | 1230 |
| LMArena Creative Writing | 1201 | 1174 |
| LMArena Multi-Turn | 1268 | 1222 |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is gpt-oss-20b better than Qwen2.5-Coder-32B?
gpt-oss-20b and Qwen2.5-Coder-32B score almost the same on the Noometry Index (32.5 vs 33.4), so choose on price, context window or the category you care about most.
Which is cheaper, gpt-oss-20b or Qwen2.5-Coder-32B?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.
Is gpt-oss-20b or Qwen2.5-Coder-32B better for coding?
gpt-oss-20b scores higher on coding benchmarks: 37.6 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
gpt-oss-20b does, with 131K tokens against 33K.
How many benchmarks do gpt-oss-20b and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and Qwen2.5-Coder-32B has 31.