Technical Specifications

Command R

Cohere Command R is a generative language model architected specifically for high-performance enterprise workloads, with an emphasis on long-context processing and tool-augmented workflows. Built on an optimized decoder-only Transformer framework, the model utilizes Grouped Query Attention (GQA) to maintain a significant 128,000-token context window while reducing the memory overhead typically associated with large-scale attention mechanisms. It is designed to facilitate the transition from experimental prototypes to production-grade deployments by offering a balance between inference efficiency and high-fidelity output.

The model undergoes a multi-stage training process including extensive pre-training on a diverse multilingual corpus and subsequent alignment via supervised fine-tuning and preference optimization. A defining architectural feature is its native training for grounded generation, which allows the model to produce responses with precise inline citations from external document sources. This makes it particularly effective for retrieval-augmented generation (RAG) pipelines, where maintaining factual consistency and source traceability is a primary requirement. Furthermore, Command R supports sophisticated multi-step tool use, enabling it to act as an agent that can reason through complex tasks by interacting with external APIs, databases, and software tools.

Optimized for global business applications, Command R provides native support for 10 languages and is trained on 23 in total, ensuring versatility across international markets. The architecture incorporates advanced components such as Rotary Positional Embeddings (RoPE) and Layer Normalization to ensure stability and coherence when handling massive input sequences. By focusing on practical utility in tasks like document summarization, complex reasoning, and structured data analysis, Command R serves as a scalable backbone for automated enterprise systems and intelligent agentic workflows.