Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral Large 3
58
Winner · 0/8 categoriesTrinity-Large-Thinking
~0
0/8 categoriesMistral Large 3· Trinity-Large-Thinking
Benchmark data for Mistral Large 3 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.
Mistral Large 3 is priced at $2.00 input / $6.00 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 128K for Mistral Large 3.
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 | Mistral Large 3 | Trinity-Large-Thinking |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 52% | — |
| BrowseComp | 58% | — |
| OSWorld-Verified | 49% | — |
| Tau2-Airline | — | 88.0% |
| Tau2-Telecom | — | 94.7% |
| PinchBench | — | 91.9% |
| BFCL v4 | — | 70.1% |
| Coding | ||
| HumanEval | 92.3% | — |
| SWE-bench Verified | 45% | — |
| LiveCodeBench | 39% | — |
| SWE-bench Pro | 42% | — |
| SWE-bench Verified* | — | 63.2% |
| Multimodal & Grounded | ||
| MMMU-Pro | 75% | — |
| OfficeQA Pro | 76% | — |
| Reasoning | ||
| MuSR | 71% | — |
| BBH | 81% | — |
| LongBench v2 | 67% | — |
| MRCRv2 | 67% | — |
| Knowledge | ||
| MMLU | 85.5% | — |
| GPQA | 43.9% | — |
| SuperGPQA | 73% | — |
| MMLU-Pro | 73.1% | — |
| HLE | 12% | — |
| FrontierScience | 67% | — |
| SimpleQA | 24% | — |
| GPQA-D | — | 76.3% |
| MMLU-Pro (Arcee) | — | 83.4% |
| Instruction Following | ||
| IFEval | 89.4% | — |
| IFBench | — | 52.3% |
| Multilingual | ||
| MGSM | 82% | — |
| MMLU-ProX | 77% | — |
| Mathematics | ||
| AIME 2023 | 76% | — |
| AIME 2024 | 78% | — |
| AIME 2025 | 77% | — |
| HMMT Feb 2023 | 72% | — |
| HMMT Feb 2024 | 74% | — |
| HMMT Feb 2025 | 73% | — |
| BRUMO 2025 | 75% | — |
| MATH-500 | 93.6% | — |
| 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.
Mistral Large 3: $2.00 input / $6.00 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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