Command R (08-2024)
Where it stands
Quality in independent tests against price, next to 135 other models. Up means more capable, left means cheaper.
About this model
Command R (08-2024) is an AI model from Cohere, made available through the API on August 30, 2024. It can read up to 128,000 tokens in one request, roughly 240 pages of text, and write up to 4,000 tokens in a single answer.
Through the API it costs $0.15 per million input tokens and $0.60 per million output tokens. Its pricing puts it among the more affordable models we track, which makes it a good fit for high-volume tasks.
It can call tools and functions, so developers can connect it to search, databases or other software.
Quality scores
Rankings →Where to use it
Companies that serve this model through an API: 1. Prices per million tokens, cheapest first.
Availability and prices come from OpenRouter’s public API and are refreshed daily. Uptime is the share of successful requests over the last 24 hours. Some providers run a compressed version of open models (fp8, int4), which is cheaper but can be slightly less accurate.
Use it in your code
Call Command R (08-2024) through OpenRouter’s OpenAI-compatible API: create a key on openrouter.ai, then run one of these examples.
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "cohere/command-r-08-2024", "messages": [{"role": "user", "content": "Hello! What can you do?"}]}'import os
from openai import OpenAI
client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=os.environ["OPENROUTER_API_KEY"])
reply = client.chat.completions.create(
model="cohere/command-r-08-2024",
messages=[{"role": "user", "content": "Hello! What can you do?"}],
)
print(reply.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://openrouter.ai/api/v1", apiKey: process.env.OPENROUTER_API_KEY });
const reply = await client.chat.completions.create({
model: "cohere/command-r-08-2024",
messages: [{ role: "user", content: "Hello! What can you do?" }],
});
console.log(reply.choices[0].message.content);The same code works with every model on this site: only the model ID changes. Many labs also offer their own API and SDK.
What it costs in practice
- 1,000 chat replies$0.255
- Summarise a 100-page document$0.0081
- An app with 10,000 requests a day for a month$117
Similar-priced alternatives
| Model | Inputi | Outputi | Contexti | |
|---|---|---|---|---|
Perceptron Mk1.5Perceptron | $0.15 | $1.50 | 36K | Compare with ⇄ |
Qwen3.8 Omni FlashAlibaba Qwen | $0.15 | $0.47 | 1M | Compare with ⇄ |
GLM 5.3 FlashZ.ai (Zhipu) | $0.15 | $0.50 | 1M | Compare with ⇄ |
Mistral Small 4Mistral AI | $0.15 | $0.60 | 256K | Compare with ⇄ |
Solar Pro 3Upstage | $0.15 | $0.60 | 128K | Compare with ⇄ |
MiMo-V2.6-FlashXiaomi | $0.14 | $0.28 | 1M | Compare with ⇄ |
News about Cohere
See all →Frequently asked questions
- How much does Command R (08-2024) cost?
- Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens through the API. For example, 1,000 chat replies of about 500 tokens in and 300 out cost around $0.255.
- What is the context window of Command R (08-2024)?
- Command R (08-2024) can read up to 128,000 tokens in one request, about 240 pages of text. It can write up to 4,000 tokens in a single answer.
- Is Command R (08-2024) open source?
- No. Command R (08-2024) is a closed model: its weights are not published, so you use it through Cohere’s own apps and API, or through cloud providers.
- Who makes Command R (08-2024)?
- Command R (08-2024) is made by Cohere. It became available through the API on August 30, 2024.
- How good is Command R (08-2024)?
- Command R (08-2024) has an overall index of 119.3 in Epoch AI’s independent tests, ranking #129 of 136 models we track.
- Can Command R (08-2024) read images?
- No. Command R (08-2024) only accepts text as input.
- What are the alternatives to Command R (08-2024)?
- Models with a similar price from other companies include Perceptron Mk1.5, Qwen3.8 Omni Flash and GLM 5.3 Flash. You can compare them side by side on this page.