Cohere: Command R7B Arabic
Command R7B Arabic is a language model from Cohere released on Feb 27, 2025. It reads text and writes text, with a 128K token context window and up to 4K tokens per reply.
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It costs $0.0375 per million input tokens and $0.15 per million output tokens.
It supports tool calling, and its weights are open, so you can host it yourself.
- Modalities
- In / out price
- $0.0375 / $0.15 per 1M
- Context
- 128K
- Max output
- 4K
- Released
- Feb 27, 2025
- Knowledge cutoff
- Jun 2024
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
Command R7B Arabic pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $0.0375 / 1M tokens | |
|---|---|---|
| Output | $0.15 / 1M tokens |
What Command R7B Arabic costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call Command R7B Arabic through an OpenAI compatible API.
- Tools
tools - Temperature
temperature
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Command R7B Arabic questions
How much does the Command R7B Arabic API cost?
$0.0375 per million input tokens and $0.15 per million output tokens. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of Command R7B Arabic?
128,000 tokens of input, and it can write up to 4,000 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Does Command R7B Arabic support tool calling?
Yes. It accepts tool definitions, which is what an agent needs to call your APIs.
Is Command R7B Arabic open source?
Its weights are published, so you can run it on your own hardware or a cloud of your choice. Read the license before commercial use.
When was Command R7B Arabic released?
Feb 27, 2025, with training data up to Jun 2024.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on Command R7B Arabic?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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