Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-OSS 20B
35
Winner · 0/8 categoriesTrinity-Large-Thinking
~0
0/8 categoriesGPT-OSS 20B· Trinity-Large-Thinking
Benchmark data for GPT-OSS 20B and Trinity-Large-Thinking is coming soon on BenchLM.
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Trinity-Large-Thinking is priced at $0.25 input / $0.90 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GPT-OSS 20B. Trinity-Large-Thinking has the larger context window at 512K, compared with 128K for GPT-OSS 20B.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | GPT-OSS 20B | Trinity-Large-Thinking |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 35% | — |
| OSWorld-Verified | 31% | — |
| Tau2-Airline | — | 88.0% |
| Tau2-Telecom | — | 94.7% |
| PinchBench | — | 91.9% |
| BFCL v4 | — | 70.1% |
| Coding | ||
| HumanEval | 23% | — |
| SWE-bench Verified | 14% | — |
| LiveCodeBench | 11% | — |
| SWE-bench Pro | 18% | — |
| React Native Evals | 64.3% | — |
| SWE-bench Verified* | — | 63.2% |
| Multimodal & Grounded | ||
| MMMU-Pro | 31% | — |
| OfficeQA Pro | 42% | — |
| Reasoning | ||
| MuSR | 27% | — |
| LongBench v2 | 48% | — |
| MRCRv2 | 48% | — |
| Knowledge | ||
| MMLU | 85.3% | — |
| GPQA | 30% | — |
| SuperGPQA | 28% | — |
| MMLU-Pro | 53% | — |
| HLE | 1% | — |
| FrontierScience | 34% | — |
| SimpleQA | 29% | — |
| GPQA-D | — | 76.3% |
| MMLU-Pro (Arcee) | — | 83.4% |
| Instruction Following | ||
| IFBench | — | 52.3% |
| Multilingual | ||
| MGSM | 61% | — |
| MMLU-ProX | 59% | — |
| Mathematics | ||
| AIME 2023 | 31% | — |
| AIME 2024 | 33% | — |
| AIME 2025 | 32% | — |
| HMMT Feb 2023 | 27% | — |
| HMMT Feb 2024 | 29% | — |
| HMMT Feb 2025 | 28% | — |
| BRUMO 2025 | 30% | — |
| MATH-500 | 59% | — |
| AIME25 (Arcee) | — | 96.3% |
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
GPT-OSS 20B: $0.00 input / $0.00 output per 1M tokens Trinity-Large-Thinking: $0.25 input / $0.90 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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