Model comparison
gpt-oss-20b vs Qwen1.5-7B
gpt-oss-20b is the stronger model overall, scoring 32.1 to 30.8 on the Noometry Index.
Last verified . 12 shared benchmarks.
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. gpt-oss-20b scores higher in 7 categories and Qwen1.5-7B in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where gpt-oss-20b leads 42.4 to 28.6.
Side by side
| gpt-oss-20b | Qwen1.5-7B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 32.1 | 30.8 |
| Released | 2025-08-05 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 131K | — |
| Max output | 16K | — |
| Input $ / M tokens | $0.018 | — |
| Output $ / M tokens | $0.09 | — |
| Results tracked | 34 | 13 |
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Category by category
Coding gpt-oss-20b leads
gpt-oss-20b: 37.1 (#185), Qwen1.5-7B: 31.6 (#271)
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| LMArena Coding | 1307 | 1107 |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |
| ALE-Bench | 566.05 | — |
Agentic & Tool Use Not comparable
gpt-oss-20b: 9.2 (#146), Qwen1.5-7B: —
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| Terminal-Bench | 3.4% | — |
Reasoning Too close to call
gpt-oss-20b: 18.8 (#251), Qwen1.5-7B: 19.5 (#237)
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| LMArena Hard Prompts | 1274 | 1066 |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | 1.4% | — |
| Chess Puzzles | 4% | — |
| DTBench | 68% | — |
| LMCA | 14.5% | — |
| Epoch Capabilities Index | 137.81 | — |
Math gpt-oss-20b leads
gpt-oss-20b: 37.6 (#93), Qwen1.5-7B: 29.0 (#222)
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| LMArena Math | 1317 | 1080 |
| OTIS Mock AIME 2024-2025 | 65.3% | — |
| Omni-MATH | 56.5% | — |
Knowledge gpt-oss-20b leads
gpt-oss-20b: 34.6 (#186), Qwen1.5-7B: 28.2 (#239)
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| LMArena Expert | 1258 | 1055 |
| GPQA Diamond | 60.8% | — |
| MMLU-Pro | 74% | — |
| GPQA (HELM) | 59.4% | — |
| MMLU | — | 62.6% |
Multilingual gpt-oss-20b leads
gpt-oss-20b: 42.4 (#192), Qwen1.5-7B: 28.6 (#266)
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| LMArena Non-English | 1269 | 1058 |
| LMArena Chinese | 1311 | 1140 |
| LMArena Russian | 1279 | 1006 |
| LMArena German | 1249 | — |
| LMArena Japanese | 1245 | — |
| LMArena Korean | 1239 | — |
| LMArena Spanish | 1267 | — |
Instruction Following gpt-oss-20b leads
gpt-oss-20b: 62.0 (#235), Qwen1.5-7B: 54.4 (#276)
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| LMArena Instruction Following | 1236 | 1058 |
| IFEval | 73.2% | — |
Long Context gpt-oss-20b leads
gpt-oss-20b: 37.9 (#203), Qwen1.5-7B: 33.1 (#261)
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| LMArena Longer Query | 1251 | 1090 |
Writing & Preference gpt-oss-20b leads
gpt-oss-20b: 35.8 (#260), Qwen1.5-7B: 29.8 (#288)
| Benchmark | gpt-oss-20b | Qwen1.5-7B |
|---|---|---|
| LMArena Text | 1288 | 1084 |
| LMArena Creative Writing | 1201 | 1035 |
| LMArena Multi-Turn | 1268 | 1061 |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |
Frequently asked questions
Is gpt-oss-20b better than Qwen1.5-7B?
gpt-oss-20b is the stronger model overall, scoring 32.1 to 30.8 on the Noometry Index.
Is gpt-oss-20b or Qwen1.5-7B better for coding?
gpt-oss-20b scores higher on coding benchmarks: 37.1 versus 31.6 in the Noometry coding category.
How many benchmarks do gpt-oss-20b and Qwen1.5-7B share?
12 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and Qwen1.5-7B has 13.