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Command A: An Enterprise-Ready Large Language Model

arXiv 2504.00698 ↗First posted 1 Apr 2025cs.CL cs.AI cs.LGcited by 59

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Abstract (arXiv)

In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised and multilingual-capable model, with support for 23 languages of global business, and a novel hybrid architecture balancing efficiency with top of the range performance. It offers best-in-class Retrieval Augmented Generation (RAG) capabilities with grounding and tool use to automate sophisticated business processes. These abilities are achieved through a decentralised training approach, including self-refinement algorithms and model merging techniques. We also include results for Command R7B which shares capability and architectural similarities to Command A. Weights for both models have been released for research purposes. This technical report details our original training pipeline and presents an extensive evaluation of our models across a suite of enterprise-relevant tasks and public benchmarks, demonstrating excellent performance and efficiency.

Key facts. Cited by 59 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Apr 2025.

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Citing papers per year: 2025: 34; 2026: 25.

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38 more
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  26. 2025Mina: A Multilingual LLM-Powered Legal Assistant Agent for Bangladesh for Empowering Access to Justicecited by 2
  27. 2026The Appeal and Reality of Recycling LoRAs with Adaptive Mergingcited by 2
  28. 2026Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmarkcited by 2
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  31. 2026Still Fresh? Evaluating Temporal Drift in Retrieval Benchmarkscited by 1
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  34. 2026CALIBER: Calibrating Confidence Before and After Reasoning in Language Modelscited by 1
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  36. 2026Last Translation Benchmarkcited by 1
  37. 2025PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Languagecited by 0
  38. 2025PerCoR: Evaluating Commonsense Reasoning in Persian via Multiple-Choice Sentence Completioncited by 0

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