Command R (35B) and Llama 3.1 70B Instruct are priced similarly on most inference providers, but Llama 3.1 70B is twice the size and it shows in general-purpose benchmarks. On MMLU, Llama 3.1 70B lands around 83–84% versus Command R's ~68%. For tasks that aren't retrieval-specific, the Llama model is a better buy at comparable cost. The calculus shifts when your workload centers on grounded retrieval. Command R was designed from the ground up for RAG — its training includes explicit grounding data and citation tasks, and the model reliably avoids confabulating sources when passage context is provided. Llama 3.1 70B can do RAG but requires more careful prompt engineering to get the same citation behavior. Check current provider rates for both on [Command R's model page](/models/cohere--command-r). For multi-document enterprise Q&A — where the model ingests 10–20 retrieved passages and must produce a factual answer with source attribution — Command R's specialized training is a genuine edge. The lower MMLU score matters less when the task is constrained to retrieved context rather than open-world knowledge. Llama 3.1 70B Instruct is the better general assistant: stronger reasoning, better instruction following, and a fully open Meta license (Llama 3.1 Community License). For agentic pipelines, complex summarization, or coding assistance where RAG grounding isn't the primary constraint, the 70B's parameter advantage is decisive. See provider options on [Llama 3.1 70B Instruct's model page](/models/meta--llama-3.1-70b-instruct). **Pick Command R** for grounded retrieval with citation requirements. **Pick Llama 3.1 70B** for general-purpose reasoning and broader task coverage.