Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DBRX Instruct
41
Winner · 0/8 categoriesTrinity-Large-Thinking
~0
0/8 categoriesDBRX Instruct· Trinity-Large-Thinking
Benchmark data for DBRX Instruct 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 DBRX Instruct. Trinity-Large-Thinking has the larger context window at 512K, compared with 32K for DBRX Instruct.
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 | DBRX Instruct | Trinity-Large-Thinking |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 41% | — |
| BrowseComp | 31% | — |
| OSWorld-Verified | 29% | — |
| Tau2-Airline | — | 88.0% |
| Tau2-Telecom | — | 94.7% |
| PinchBench | — | 91.9% |
| BFCL v4 | — | 70.1% |
| Coding | ||
| HumanEval | 70.1% | — |
| SWE-bench Pro | 48% | — |
| SWE-bench Verified* | — | 63.2% |
| Multimodal & Grounded | ||
| MMMU-Pro | 36% | — |
| OfficeQA Pro | 35% | — |
| Reasoning | ||
| LongBench v2 | 36% | — |
| MRCRv2 | 37% | — |
| Knowledge | ||
| MMLU | 73.7% | — |
| FrontierScience | 52% | — |
| GPQA-D | — | 76.3% |
| MMLU-Pro (Arcee) | — | 83.4% |
| Instruction Following | ||
| IFBench | — | 52.3% |
| Multilingual | ||
| MMLU-ProX | 46% | — |
| Mathematics | ||
| 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.
DBRX Instruct: $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.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.