The headline number here is parameter count: Llama 3.1 405B is roughly 5× larger than Command R+ at ~104B parameters, and that gap shows up in reasoning benchmarks. On MMLU, Llama 3.1 405B scores around 88–89%, compared to Command R+'s ~75%. But raw capability isn't the whole story — you pay a steep inference premium for the 405B, and Command R+ was purpose-built for enterprise RAG and retrieval-augmented workflows. Command R+ supports a 128K context window with first-class retrieval-grounding features baked into its prompt format — Cohere designed the model to cite sources and reduce hallucination in document QA settings. Llama 3.1 405B also supports 128K context, but lacks the native grounding layer. See current provider rates on [Command R+'s model page](/models/cohere--command-r-plus). For production RAG over large enterprise document stores — legal contracts, financial filings, internal wikis — Command R+ typically delivers better citation accuracy per dollar than the 405B. Its specialized training data makes it reliable for structured retrieval at a fraction of the inference cost. Llama 3.1 405B Instruct is the call when you need maximum reasoning depth: complex multi-step agent pipelines, long-form synthesis across heterogeneous sources, or agentic coding tasks where the model needs to reason across many files simultaneously. The performance gap on hard reasoning tasks is large enough to justify the cost for lower-volume, high-value use cases. Check provider availability on [Llama 3.1 405B Instruct's model page](/models/meta--llama-3.1-405b-instruct). **Pick Command R+** for cost-efficient RAG and retrieval-grounded enterprise Q&A. **Pick Llama 3.1 405B** for complex reasoning tasks where accuracy trumps cost.