Command-R-Plus

Advanced RAG-Centric AI by Cohere

What is Command-R+?

Command-R+ (also known as Command-R-Plus) is Cohere’s flagship open-weight language model, purpose-built for retrieval-augmented generation (RAG). It builds on the architecture of Command-R but features enhanced capabilities, including support for 128K token context, superior accuracy, and lower hallucination rates in document-grounded tasks.

Whether you’re powering enterprise search, QA bots, summarizers, or agentic systems, Command-R+ gives you production-grade RAG performance—with fully open weights for unrestricted development and research.

Key Features of Command-R+

128K Context Window

Ideal for document-heavy tasks like legal QA, research synthesis, and full-report summarization.

RAG-Optimized Design

Engineered to integrate seamlessly with retrieval systems—vector databases, search APIs, and knowledge graphs.

Grounded Generation Accuracy

Reduced hallucination risk through fine-tuning on retrieval-augmented instruction tasks.

Open-Weight, Permissive License

All model weights, tokenizer, and configs are available—allowing local deployment, research, and fine-tuning.

Efficient Inference at Scale

Optimized for speed and stability across enterprise GPUs and deployment environments.

Customizability for Vertical Domains

Fine-tune or adapt the model to your unique data in industries like healthcare, legal, fintech, or education.

Key Features of Command-R+

Answer internal queries from HR manuals, IT policies, or support knowledge bases with accurate, grounded AI.

Use with retrieval systems to build fast, intelligent search and semantic QA tools.

Ingest and process long documents, from legal contracts to clinical notes, with multi-turn comprehension.

Power AI agents that can cite, extract, and reason over retrieved documents in real time.

Analyze model behavior, benchmark performance, or build domain-specific models with full transparency.

Command-R+

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Other Leading Models

Feature	GPT-4 Turbo	Claude 3 Opus	Mistral 7B	Command-R+
Model Type	MoE	MoE	Dense Transformer	Dense Transformer
Open Weights	❌	❌	✅	✅
Max Context Length	~128K	~200K	32K	128K
RAG Optimization	Medium	High	Low	Very High
Fine-Tuning Support	❌	❌	✅	✅
Hallucination Risk	Medium	Low	Medium	Very Low
Best Use Case	General Tools	QA + Docs	Light NLP Apps	Enterprise RAG AI