Command R API Pricing: RAG-Optimized at $0.50/$1.50 Per Million Tokens

Command R is Cohere's budget model purpose-built for retrieval-augmented generation. At $0.50/M input and $1.50/M output, it delivers built-in grounding, tool use, and 100+ languages at a fraction of premium model costs.

Updated Aug 15, 2026 · 94 models tracked across 11 providers

TL;DR

RAG-Optimized: Command R is not just another budget model — it's specifically trained for retrieval-augmented generation. Built-in grounding means it cites sources and reduces hallucinations when working with external knowledge. This makes it more reliable for RAG than general-purpose models that simply happen to support long contexts.

Command R Pricing Breakdown

At $0.50 per million input tokens and $1.50 per million output tokens, here's how the costs scale:

Monthly Volume Input Cost Output Cost Total Cost
1M tokens$0.50$1.50$2.00
10M tokens$5.00$15.00$20.00
100M tokens$50.00$150.00$200.00
1B tokens$500.00$1,500.00$2,000.00

Command R costs more per token than the cheapest budget models (GPT-5 nano at $0.05/$0.40, or Qwen 3.7 Flash at $0.03/$0.13). But for RAG workloads, the built-in grounding and source citation often eliminate the need for additional post-processing — saving cost elsewhere in your pipeline.

Why RAG-Optimized Pricing Is Worth It

Command R isn't the cheapest budget model on paper. So why use it? Because for RAG workloads, the true cost includes more than just per-token pricing:

Factor Generic Budget Model Command R
Per-token cost Lower Higher ($0.50/$1.50)
Grounding accuracy Requires prompt engineering Built-in, trained for it
Hallucination rate on RAG Higher — needs validation Lower — cites sources
Tool use reliability Hit-or-miss at budget tier Native support
Post-processing needed Often required Minimal
Engineering time More prompt tuning Less — works out of the box

For RAG-specific workloads, Command R's slightly higher per-token cost is often offset by reduced engineering overhead, fewer hallucinations requiring human review, and more reliable grounding. The total cost of ownership can be lower than using a cheaper generic model with extensive prompt engineering and validation layers.

Command R vs Other Budget Models

Model Input $/M Output $/M Context Provider
Qwen 3.7 Flash $0.03 $0.13 1M Alibaba
GPT-5 nano $0.05 $0.40 128K OpenAI
Ministral 3 8B $0.15 $0.15 128K Mistral
Gemini 2.5 Flash-Lite $0.10 $0.40 1M Google
DeepSeek V4 Flash $0.14 $0.28 1M DeepSeek
Mistral Small 4 $0.20 $0.60 128K Mistral
Command R $0.50 $1.50 128K Cohere

Command R is not the cheapest budget model by raw per-token pricing. But no other model in this tier offers built-in RAG optimization, native grounding with source citation, and reliable tool use. If your workload is retrieval-augmented generation, Command R delivers capabilities that generic budget models simply don't have.

The Cohere Family: Command R vs Command R+ vs Command A

Model Input $/M Output $/M Context Best For
Command R $0.50 $1.50 128K RAG pipelines, grounded chatbots, tool use at scale
Command R+ $2.50 $10.00 128K Complex reasoning, high-accuracy RAG, enterprise
Command A $2.50 $10.00 256K Most capable Cohere model, largest context, agentic

Command R is 5x cheaper on input and 6.7x cheaper on output than Command R+ and Command A. For RAG workloads where grounding accuracy matters but you don't need the absolute best reasoning, Command R is the clear value choice. Step up to Command R+ or Command A only when you need complex multi-step reasoning or the 256K context window.

When to Use Command R

📚 RAG Pipelines

Retrieval-augmented generation is Command R's core strength. Feed it retrieved documents and get grounded, cited responses — no extra prompt engineering needed.

🤖 Grounded Chatbots

Build chatbots that answer from your knowledge base without hallucinating. Command R cites its sources, so users can verify claims.

🔧 Tool Use

Native function calling and tool use support. Build agentic workflows that query databases, call APIs, and chain actions reliably at budget pricing.

🌍 Multi-Language Apps

Support for 100+ languages out of the box. Build RAG systems and chatbots that serve global audiences without separate models per language.

📄 Document Q&A

Answer questions over documents with source attribution. The 128K context fits multiple documents, and grounding ensures answers come from the provided material.

🔍 Search Augmentation

Enhance search results with AI-generated summaries and answers. Command R synthesizes retrieved results into coherent, grounded responses.

Real-World Cost: 50,000 RAG Queries/Month

Suppose you're running a customer support chatbot powered by RAG. You process 50,000 queries per month, averaging 2,000 input tokens (query + retrieved context) and 500 output tokens per response:

ModelInput CostOutput CostMonthly Total
GPT-5 nano$5.00$10.00$15.00
DeepSeek V4 Flash$14.00$7.00$21.00
Gemini 2.5 Flash-Lite$10.00$10.00$20.00
Ministral 3 8B$15.00$3.75$18.75
Command R$50.00$37.50$87.50
Mistral Small 4$20.00$15.00$35.00

Command R at $87.50/month is more expensive than generic budget models for this workload. But consider the trade-offs: built-in grounding eliminates a validation layer, source citation reduces human review time, and native tool use means fewer engineering hours. For teams where reliability on RAG tasks matters, the total cost of ownership often favors Command R despite higher per-token pricing.

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Command R is one of 94 models tracked on APIpulse. Compare pricing, context windows, and features across 11 providers.

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Frequently Asked Questions

How much does Command R cost?
Command R costs $0.50 per million input tokens and $1.50 per million output tokens. This positions it as a budget-tier model, though it costs more than the cheapest models due to its specialized RAG and grounding capabilities.
What makes Command R different from other budget models?
Command R is specifically optimized for retrieval-augmented generation (RAG) with built-in grounding capabilities. Unlike general-purpose budget models, it's trained to work with retrieved documents, cite sources, and handle tool use natively. It also supports 100+ languages out of the box.
What is the context window of Command R?
Command R has a 128K token context window. This is sufficient for most RAG workflows, allowing you to pass multiple retrieved documents alongside your query without running out of space.
How does Command R compare to Command R+ and Command A?
Command R ($0.50/$1.50, 128K) is Cohere's budget option. Command R+ ($2.50/$10, 128K) offers higher accuracy and better complex reasoning at 5-7x the cost. Command A ($2.50/$10, 256K) is Cohere's most capable model with the largest context window. Command R is the best choice when you need RAG optimization at scale without the premium pricing.
Is Command R good for RAG pipelines?
Yes, Command R is purpose-built for RAG. It's trained to work with retrieved context, generate grounded responses, and cite its sources. This makes it more reliable for RAG than general-purpose models that happen to have large context windows. The built-in grounding reduces hallucinations when working with external knowledge.
Can Command R handle tool use and function calling?
Yes. Command R has native support for tool use and function calling, making it suitable for agentic workflows. It can decide when to call tools, parse tool outputs, and chain multiple tool calls together — all capabilities that many budget models lack or handle poorly.

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