Cohere Command R+
RAG-Optimized Open Frontier
Cohere's flagship open-weight model, released April 2024. Differentiated by being explicitly trained for retrieval- augmented generation (RAG) workflows — given retrieved documents in context, it integrates them more accurately and cites them more reliably than generic LLMs. The default model behind much enterprise RAG infrastructure in 2024-25.
Intelligence
Below avg
Cost
High
$3.00 in / $15.00 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Command R+ is the proof that "specialized for enterprise" can be a viable LLM-business positioning distinct from "race to the frontier." The continued existence of Cohere as a meaningful enterprise vendor (vs being absorbed by a hyperscaler) validates the segment.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Generative
Produces images, video, audio, or other media.
Agent Workflows
Built for tool use and autonomous tasks.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Not available
Product / App
Not available
Open Source
Released
Enterprise
—
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Industry-leading RAG with in-line citations baked into the response — Cohere's signature differentiator
- 104B params, 88.2% MMLU at release — competitive with GPT-4 Turbo on enterprise tasks
- Beats Claude 3 Sonnet, Mistral Large, and GPT-4 Turbo on Cohere's internal RAG / tool-use benchmarks
- Strong on Berkeley Function Calling and ToolTalk Hard — designed for multi-step agent flows
- Multilingual coverage in 10 major business languages (EN, FR, DE, ES, IT, JA, KO, AR, PT, ZH)
- Optimised for the enterprise sales motion — RAG citations matter more here than benchmark headlines
Best use cases
- Enterprise RAG where citation accuracy and hallucination reduction matter most
- Multi-step tool-use agents in regulated industries
- Multilingual customer-facing assistants beyond English
- Workflows needing predictable, citation-backed enterprise output
Tools to try
Not ideal for
- Frontier-leaderboard reasoning by 2025 — newer Cohere Command-A and competitors lead
- Consumer / chatbot use cases where personality and creativity matter
- Coding-agent benchmarks (Claude Sonnet / GPT-5 stronger here)