Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4
82
Winner · 0/8 categoriesTrinity-Large-Thinking
~0
0/8 categoriesGPT-5.4· Trinity-Large-Thinking
Benchmark data for GPT-5.4 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.
GPT-5.4 is priced at $2.50 input / $15.00 output per 1M tokens, versus $0.25 input / $0.90 output per 1M tokens for Trinity-Large-Thinking. GPT-5.4 has the larger context window at 1.05M, compared with 512K for Trinity-Large-Thinking.
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-5.4 | Trinity-Large-Thinking |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 75.1% | — |
| BrowseComp | 82.7% | — |
| OSWorld-Verified | 75% | — |
| MCP Atlas | 67.2% | — |
| Toolathlon | 54.6% | — |
| Tau2-Telecom | 98.9% | 94.7% |
| Tau2-Airline | — | 88.0% |
| PinchBench | — | 91.9% |
| BFCL v4 | — | 70.1% |
| Coding | ||
| HumanEval | 95% | — |
| SWE-bench Verified | 84% | — |
| LiveCodeBench | 84% | — |
| SWE-bench Pro | 57.7% | — |
| React Native Evals | 82.6% | — |
| SWE-bench Verified* | — | 63.2% |
| Multimodal & Grounded | ||
| MMMU-Pro | 81.2% | — |
| OfficeQA Pro | 96% | — |
| MMMU-Pro w/ Python | 81.5% | — |
| OmniDocBench 1.5 | 0.1090 | — |
| Reasoning | ||
| MuSR | 94% | — |
| BBH | 97% | — |
| MRCRv2 | 97% | — |
| MRCR v2 64K-128K | 86% | — |
| MRCR v2 128K-256K | 79.3% | — |
| Graphwalks BFS 128K | 93.1% | — |
| Graphwalks Parents 128K | 89.8% | — |
| ARC-AGI-2 | 73.3% | — |
| Knowledge | ||
| MMLU | 99% | — |
| GPQA | 92.8% | — |
| SuperGPQA | 96% | — |
| MMLU-Pro | 93% | — |
| HLE | 48% | — |
| FrontierScience | 91% | — |
| HLE w/o tools | 39.8% | — |
| SimpleQA | 97% | — |
| GPQA-D | — | 76.3% |
| MMLU-Pro (Arcee) | — | 83.4% |
| Instruction Following | ||
| IFEval | 96% | — |
| IFBench | — | 52.3% |
| Multilingual | ||
| MGSM | 96% | — |
| MMLU-ProX | 94% | — |
| Mathematics | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 99% | — |
| HMMT Feb 2023 | 96% | — |
| HMMT Feb 2024 | 98% | — |
| HMMT Feb 2025 | 97% | — |
| BRUMO 2025 | 97% | — |
| MATH-500 | 99% | — |
| 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-5.4: $2.50 input / $15.00 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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