Command A Reasoning

Product Introduction

1. Command A Reasoning is a high-performance language model developed by Cohere for enterprise-grade reasoning tasks, optimized for secure private deployments and complex agentic workflows. It delivers superior accuracy in multi-step problem-solving scenarios while operating efficiently on minimal hardware, such as a single NVIDIA H100 or A100 GPU. The model supports configurable context lengths up to 256k tokens for latency-optimized deployments, making it adaptable to diverse enterprise infrastructure requirements.
2. The core value lies in its ability to balance computational efficiency with advanced reasoning capabilities, enabling organizations to deploy AI agents for mission-critical workflows without compromising data security. It eliminates the need for separate models for reasoning and non-reasoning tasks by allowing dynamic adjustment of token budgets to control costs and performance.

Main Features

1. The model operates on a single GPU (H100/A100) for low-footprint deployments, reducing hardware costs while maintaining 128k token context length for document-intensive workflows. For latency-sensitive applications, scaling to 256k tokens across multiple GPUs ensures seamless handling of large datasets and complex agentic chains.
2. A user-controlled token budget enables precise management of computational resources, allowing enterprises to prioritize either high-accuracy reasoning (e.g., financial analysis) or high-throughput tasks (e.g., batch processing) using the same model. This feature eliminates the operational complexity of maintaining separate specialized models.
3. Native integration with Cohere’s North platform provides secure on-premises deployment of AI agents, leveraging the model’s enhanced multilingual reasoning capabilities across 23 languages for global enterprises. The architecture supports hierarchical agent systems for parallel task execution, such as Cohere’s Deep Research agent for multi-source analysis.

Problems Solved

1. Enterprises struggle with deploying large language models that require excessive computational resources while maintaining data privacy—Command A Reasoning addresses this by offering private, single-GPU deployment with enterprise-grade security protocols.
2. The product targets industries like financial services, healthcare, and manufacturing that require auditable, secure AI systems for complex tasks such as regulatory compliance checks, multi-hop data analysis, and cross-lingual documentation processing.
3. Typical use cases include generating detailed research reports through parallel agent networks, automating customer service workflows with multilingual support, and processing long technical documents (e.g., legal contracts or engineering schematics) within private infrastructure.

Unique Advantages

1. Unlike competitors like GPT-OSS-120B or Mistral Magistral Medium, Command A Reasoning achieves higher benchmark scores on agentic tasks (BFCL-v3, Tau-bench) while using 75% fewer GPUs, validated by Cohere’s internal evaluations against equivalent parameter-count models.
2. The model introduces configurable reasoning depth through token budgets, a feature absent in comparable models, enabling runtime optimization without model switching. Its hierarchical agent architecture outperforms monolithic systems in Cohere’s DeepResearch Bench RACE evaluations by 22%.
3. Competitive advantages include native compatibility with Cohere’s retrieval-augmented generation (RAG) ecosystem, certified compliance with CSEA and GDPR safety protocols, and demonstrated 38% higher satisfaction scores in enterprise productivity tasks compared to previous Command A models.

Frequently Asked Questions (FAQ)

1. What hardware is required for deployment? Command A Reasoning runs on a single H100/A100 GPU for basic operations, with scalable configurations supporting up to 256k token context lengths across multiple GPUs. Minimum VRAM requirements start at 80GB for 128k context deployments.
2. How does the token budget control costs? Users set a computational threshold that dynamically adjusts the model’s reasoning depth—lower budgets prioritize faster response times for simple queries, while higher budgets activate multi-step verification for critical tasks like financial forecasting.
3. Does it support non-English enterprise workflows? Yes, the model achieves top-quartile performance in Cohere’s M-TauBench evaluations across Japanese, Korean, Arabic, Spanish, and French, with specialized tuning for industry-specific terminology in sectors like multinational retail or aviation.
4. How does it compare to open-source alternatives? Internal benchmarks show 19% higher accuracy than GPT-OSS-120B on agentic workflows and 31% faster inference latency than DeepSeek-R1 0528 when processing 100k+ token documents, while maintaining full data isolation.
5. What safety measures are implemented? The model uses five-layer content filtering aligned with NIST AI RMF standards, reducing harmful output generation by 93% compared to base models while maintaining <5% over-refusal rates on valid enterprise queries.