question answering benchmark
Cohere's internal North evaluation measures how well a model answers enterprise questions using MCP-connected cloud file systems.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelCO | Score65.00% | 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 cohere agentic question answering 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
Command A+ currently leads Cohere Agentic Question Answering with 65.00%. 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 Cohere Agentic Question Answering measures and how its scores work.
Cohere's internal North evaluation for enterprise question answering with MCP-connected cloud file systems.
It measures how well a model answers enterprise questions, with results reported as a Score ratio using LLM-as-a-judge techniques.
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 Cohere Agentic Question Answering.
Command A+ is currently ranked first with 65.00%.
The current leaders are Command A+ (65.00%).
Command A+ has the lowest matched official input price at $2.5 input / $10 output per 1M tokens.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures how well a model answers enterprise questions, with results reported as a Score ratio using LLM-as-a-judge techniques.
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.