Command-R+

Cohere’s Most Advanced RAG-Centric LLM

What is Command-R+?

Command-R+ is the next-generation, open-weight large language model from Cohere, designed specifically to excel in retrieval-augmented generation (RAG). Built on a dense transformer architecture and fine-tuned for enterprise-grade applications, it offers higher performance, longer context support, and more reliable outputs than its predecessor, Command-R.

Optimized for accuracy, scalability, and retrieval integration, Command-R+ delivers top-tier performance in grounded generation tasks such as enterprise search, document summarization, QA, and custom domain-specific applications.

Key Features of Command-R+

RAG-Optimized for Real-World Deployment

Built to integrate directly with retrieval systems like vector databases, knowledge graphs, and document stores.

Extended Context Support (128K tokens)

Ideal for long documents, multi-turn queries, or complex retrieval flows requiring persistent context awareness.

Enhanced Instruction & QA Abilities

Delivers robust, factual, and context-aware answers grounded in external knowledge or document sources.

Fully Open-Weight & Research-Friendly

Available for full inspection, fine-tuning, and customization under a permissive open license—backed by Cohere’s mission for open AI.

Superior RAG Accuracy with Fewer Hallucinations

Fine-tuned to reduce hallucinations in QA and summarization tasks by relying on external retrieved content.

High Throughput + Low Latency Inference

Designed for production environments—scalable on modern GPU infrastructure with minimal compute requirements for its capability tier.

Use Cases of Command-R+

Build internal knowledge agents or support tools that deliver verified answers from your own data.

Enhance semantic and contextual search by combining Command-R+ with vector stores like Pinecone, Weaviate, or Elasticsearch.

Apply Command-R+ to domains requiring precise, grounded, and explainable language generation from long documents.

Use open weights to explore performance, tune for scientific tasks, or audit and adapt AI behavior in critical domains.

Enable agents that cite, reference, or extract from documents in customer support, e-commerce, or enterprise knowledge bases.

Command-R+

vs

Other Leading RAG-Compatible LLMs

Feature	GPT-4-turbo	Claude 3 Opus	Mistral 7B	Command-R+
Model Type	Mixture of Experts	Mixture of Experts	Dense Transformer	Dense Transformer
Open Weights	No	No	Yes	Yes
Max Context Length	~128K (approx)	~200K	32K	128K
RAG Optimization	Moderate	High	Low	Very High
Fine-Tuning Support	Closed	Closed	Yes	Yes
Hallucination Risk	Low	Low	Moderate	Very Low
Best Use Case	General AI + Tools	QA + Reasoning	Lightweight NLP	Reliable RAG AI