Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1-pro
45
Winner · 0/8 categoriesTrinity-Large-Thinking
~0
0/8 categorieso1-pro· Trinity-Large-Thinking
Benchmark data for o1-pro 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.
o1-pro is priced at $150.00 input / $600.00 output per 1M tokens, versus $0.25 input / $0.90 output per 1M tokens for Trinity-Large-Thinking. Trinity-Large-Thinking has the larger context window at 512K, compared with 200K for o1-pro.
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 | o1-pro | Trinity-Large-Thinking |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 40% | — |
| BrowseComp | 50% | — |
| OSWorld-Verified | 32% | — |
| Tau2-Airline | — | 88.0% |
| Tau2-Telecom | — | 94.7% |
| PinchBench | — | 91.9% |
| BFCL v4 | — | 70.1% |
| Coding | ||
| SWE-bench Pro | 23% | — |
| SWE-bench Verified* | — | 63.2% |
| Multimodal & Grounded | ||
| MMMU-Pro | 48% | — |
| OfficeQA Pro | 49% | — |
| Reasoning | ||
| LongBench v2 | 54% | — |
| MRCRv2 | 59% | — |
| Knowledge | ||
| GPQA | 79% | — |
| FrontierScience | 63% | — |
| GPQA-D | — | 76.3% |
| MMLU-Pro (Arcee) | — | 83.4% |
| Instruction Following | ||
| IFBench | — | 52.3% |
| Multilingual | ||
| MMLU-ProX | 52% | — |
| Mathematics | ||
| AIME 2024 | 86% | — |
| 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.
o1-pro: $150.00 input / $600.00 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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