reasoning benchmark
OfficeQA is a Databricks public benchmark for grounded reasoning over historical U.S. Treasury Bulletin documents.
Updated Sep 24, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAN | Score78.90% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis officeqa AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Claude Opus 5.5 currently leads OfficeQA with 78.90%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What OfficeQA measures and how its scores work.
OfficeQA is a benchmark involving a large corpus of historical U.S. Treasury Bulletin documents.
It evaluates end-to-end grounded reasoning, including locating relevant tables across the corpus and performing precise numerical reasoning over them.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about OfficeQA.
Claude Opus 5.5 is currently ranked first with 78.90%.
The current leaders are Claude Opus 5.5 (78.90%).
Claude Opus 5.5 has the lowest matched official input price at $4.0 input / $20 output per 1M tokens.
The fastest matched records are Claude Opus 5.5 (64.14 tok/s via Anthropic).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It evaluates end-to-end grounded reasoning, including locating relevant tables across the corpus and performing precise numerical reasoning over them.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.