Model comparison
gpt-oss-20b vs Qwen1.5-14B
gpt-oss-20b and Qwen1.5-14B score almost the same on the Noometry Index (32.5 vs 32.7), so choose on price, context window or the category you care about most.
Last verified . 15 shared benchmarks.
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. gpt-oss-20b scores higher in 7 categories and Qwen1.5-14B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where gpt-oss-20b leads 42.2 to 30.7.
Side by side
| gpt-oss-20b | Qwen1.5-14B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 32.5 | 32.7 |
| 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 | 17 |
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Category by category
Coding gpt-oss-20b leads
gpt-oss-20b: 37.6 (#192), Qwen1.5-14B: 33.1 (#263)
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| LMArena Coding | 1306 | 1138 |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |
| ALE-Bench | 566.05 | — |
Agentic & Tool Use Not comparable
gpt-oss-20b: 9.3 (#154), Qwen1.5-14B: —
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| Terminal-Bench | 3.4% | — |
Reasoning Qwen1.5-14B leads
gpt-oss-20b: 19.3 (#261), Qwen1.5-14B: 21.4 (#223)
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| LMArena Hard Prompts | 1274 | 1113 |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | 1.4% | — |
| Chess Puzzles | 4% | — |
| DTBench | 68% | — |
| LMCA | 14.5% | — |
| Epoch Capabilities Index | 137.82 | — |
Math gpt-oss-20b leads
gpt-oss-20b: 39.4 (#103), Qwen1.5-14B: 32.4 (#215)
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| LMArena Math | 1317 | 1125 |
| OTIS Mock AIME 2024-2025 | 65.3% | — |
| Omni-MATH | 56.5% | — |
Knowledge gpt-oss-20b leads
gpt-oss-20b: 34.6 (#195), Qwen1.5-14B: 29.8 (#232)
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| LMArena Expert | 1258 | 1094 |
| GPQA Diamond | 60.8% | — |
| MMLU-Pro | 74% | — |
| GPQA (HELM) | 59.4% | — |
| MMLU | — | 68.6% |
Multilingual gpt-oss-20b leads
gpt-oss-20b: 42.2 (#197), Qwen1.5-14B: 30.7 (#262)
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| LMArena Non-English | 1268 | 1095 |
| LMArena Chinese | 1314 | 1147 |
| LMArena German | 1255 | 1043 |
| LMArena Japanese | 1244 | 1019 |
| LMArena Russian | 1278 | 1046 |
| LMArena Spanish | 1267 | 1085 |
| LMArena French | — | 1116 |
| LMArena Korean | 1236 | — |
Instruction Following gpt-oss-20b leads
gpt-oss-20b: 61.8 (#240), Qwen1.5-14B: 56.8 (#271)
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| LMArena Instruction Following | 1236 | 1102 |
| IFEval | 73.2% | — |
Long Context gpt-oss-20b leads
gpt-oss-20b: 37.9 (#209), Qwen1.5-14B: 33.7 (#257)
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| LMArena Longer Query | 1250 | 1113 |
Writing & Preference gpt-oss-20b leads
gpt-oss-20b: 35.5 (#265), Qwen1.5-14B: 33.6 (#276)
| Benchmark | gpt-oss-20b | Qwen1.5-14B |
|---|---|---|
| LMArena Text | 1287 | 1128 |
| LMArena Creative Writing | 1201 | 1091 |
| LMArena Multi-Turn | 1268 | 1110 |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |
Frequently asked questions
Is gpt-oss-20b better than Qwen1.5-14B?
gpt-oss-20b and Qwen1.5-14B score almost the same on the Noometry Index (32.5 vs 32.7), so choose on price, context window or the category you care about most.
Is gpt-oss-20b or Qwen1.5-14B better for coding?
gpt-oss-20b scores higher on coding benchmarks: 37.6 versus 33.1 in the Noometry coding category.
How many benchmarks do gpt-oss-20b and Qwen1.5-14B share?
15 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and Qwen1.5-14B has 17.