--- title: Command R 08-2024 — RAG LLM | ModelsLab description: Access Cohere Command R (08-2024) API for 128K context RAG, tool use and reasoning. Generate precise responses via ModelsLab endpoints now. url: https://modelslab.com/cohere-command-r-08-2024 canonical: https://modelslab.com/cohere-command-r-08-2024 type: website component: Seo/ModelPage generated_at: 2026-05-05T20:10:01.104802Z --- Available now on ModelsLab · Language Model Cohere: Command R (08-2024) Reason Deeper. Retrieve Smarter --- [Try Cohere: Command R (08-2024)](/models/open_router/cohere-command-r-08-2024) [API Documentation](https://docs.modelslab.com) Unlock Command R Power --- 128K Context ### Handle Long Documents Process 128,000 tokens for complex tasks with full conversation history. RAG Optimized ### Multilingual Retrieval Enhance Cohere: Command R (08-2024) with customizable citations in 23 languages. Tool Use ### Function Calling Built-in Execute sequential tools for dynamic reasoning and structured data analysis. Examples See what Cohere: Command R (08-2024) can create --- Copy any prompt below and try it yourself in the [playground](/models/open_router/cohere-command-r-08-2024). Tech Summary “Summarize the key features of Cohere: Command R (08-2024) model, including context length, RAG capabilities, and supported languages. Use bullet points and cite sources.” Code Fix “Review this Python function for errors and optimize it for efficiency: def calculate\_fib(n): if n <= 1: return n else: return calculate\_fib(n-1) + calculate\_fib(n-2). Provide corrected code.” Data Analysis “Analyze this sales dataset: Q1: 1200, Q2: 1500, Q3: 1100, Q4: 1800. Identify trends, forecast Q5, and suggest improvements in a structured report.” Reasoning Chain “Solve: A train leaves at 3 PM traveling 60 mph. Another at 5 PM at 80 mph. When does the second catch up if first has 200 mile head start? Show steps.” For Developers A few lines of code. RAG queries. One call. --- ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed. - **Serverless:** scales to zero, scales to millions - **Pay per token,** no minimums - **Python and JavaScript SDKs,** plus REST API [API Documentation ](https://docs.modelslab.com) PythonJavaScriptcURL Copy ```

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

``` FAQ Common questions about Cohere: Command R (08-2024) --- [Read the docs ](https://docs.modelslab.com) ### What is Cohere: Command R (08-2024)? Cohere: Command R (08-2024) is a 32B parameter LLM optimized for reasoning, RAG, and tool use with 128K context. It improves on prior versions in math, code, and multilingual tasks. ### How does cohere command r 08 2024 API compare? Cohere: Command R (08-2024) API offers 50% higher throughput and lower latency than Command R. Access via ModelsLab for on-demand inference up to 4K output tokens. ### What is the context window for Cohere: Command R (08-2024) model? It supports 128,000 tokens for prompts and responses. Fine-tuned versions cap user prompts at 16K tokens. ### Does Cohere: Command R (08-2024) alternative support vision? Vision input is supported alongside text. It excels in function calling and multilingual RAG. ### What are strengths of cohere command r 08 2024 api? Key strengths include enhanced tool use, instruction following, and safety modes. Benchmarks show 70% on HumanEval and 67% on MMLU. ### Can I fine-tune Cohere: Command R (08-2024) LLM? Fine-tuning is available with your dataset in supported regions. Custom models limit prompts to 16K tokens and responses to 4K. Ready to create? --- Start generating with Cohere: Command R (08-2024) on ModelsLab. [Try Cohere: Command R (08-2024)](/models/open_router/cohere-command-r-08-2024) [API Documentation](https://docs.modelslab.com) --- *This markdown version is optimized for AI agents and LLMs.* **Links:** - [Website](https://modelslab.com) - [API Documentation](https://docs.modelslab.com) - [Blog](https://modelslab.com/blog) --- *Generated by ModelsLab - 2026-05-06*