Cohere has entered the competitive race of releasing LLMs with their latest offering – Command A. Their previous model, Command R+, was launched in August 2024, followed by Command R7B in December 2024. Now, with Command A, Cohere has made a strong comeback, introducing a state-of-the-art generative language model tailored for enterprise use cases. Optimized for high performance with minimal hardware demands, Command A provides a cost-effective and efficient solution for businesses. It joins Cohere’s suite of models, renowned for their scalability and robust performance across a wide range of applications. Let’s learn more about it in this article!
Table of contents
What is Cohere Command A?
Command A is a powerful 111B parameter model with a context length of 256K, allowing it to handle much longer documents compared to most leading models. It excels in areas such as tool use, retrieval-augmented generation (RAG), agents, and multilingual use cases. This model is designed to be highly efficient, requiring only two GPUs (A100s/H100s) to run, which is significantly fewer than other comparable models
New Features:
- Web Search
- Python Interpreter
- API Integration
- Database Interaction
- Retrieval-Augmented Generation (RAG)
- Agents and Complex Reasoning
- Multilingual Support (23 languages)
- Enterprise-Grade Security
Performance and Benchmarks
Cohere Command A is a top large language model (LLM) that stands out, especially for businesses. Here’s why it’s special:
Great Performance, Less Power
Command A delivers strong results using less computing power. It has 111 billion parameters and a 256k context length but only needs two GPUs (like A100s or H100s) to run. Compare that to DeepSeek V3, which needs eight GPUs for a 128k context length. This makes Command A powerful yet affordable for companies.
Super Fast
It’s 150% faster than Cohere’s earlier model, Command R+ (released in August 2024). It can handle 156 tokens per second, beating models like OpenAI’s GPT-4o and DeepSeek V3 in speed and efficiency.
Built for Business
Command A shines in tasks companies need:
- Retrieval-Augmented Generation (RAG): It uses outside data well, making it great for things like pulling financial info or answering questions from long files. Command A and GPT-4o were compared in enterprise RAG tasks. Trained annotators rated them blindly on fluency, accuracy, and usefulness.
Tool Use and Agents: It works with tools like search engines or APIs and runs fast agents for tough thinking and research tasks.
Multilingual: It supports 23 languages (like English, Spanish, Arabic, and Japanese), so it works for users worldwide and can translate too. When comapred with DeepSeek V3 on extensive human evaluation users strongly preferred Command A over DeepSeek-V3 across most languages on a range of business use cases.
Command A vs GPT 4o (Arabaic)
Affordable
| Cohere API Pricing | Input Tokens | Output Tokens |
|---|---|---|
| Command A | $2.50 / 1M | $10.00 / 1M |
How to Access Command A?
Cohere Platform
- Create an account on the Cohere Platform if you don’t already have one.
- Select the “Chat with Cohere” option
Hugging Face
Command A is also available for research use on Hugging Face. You can access it through the following steps:
Model Page: Visit the C4AI Command A model page on Hugging Face.
Installation: Install the transformers library from the source repository that includes the necessary changes for this model.
pip install transformersUsage Example
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "CohereForAI/c4ai-command-a-03-2025"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format message with the c4ai-command-a-03-2025 chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)- Try It Out: You can also try out Command A in the hosted Hugging Face Space before downloading the weights.
Note: Command A will soon be available on major cloud providers. Keep an eye on Cohere’s announcements for updates
Let’s Try Command A
I am going to try some of the features offered by Command A:
Task 1: Web Search
Prompt: “Plot the average 1 bedroom rental price in Feb 2025 for the 5 most expensive cities in India.“
Output:
Observation:
Command A performed decently but couldn’t find rental prices for New Delhi and Chennai, likely because it only checked 10 websites. For web searches, Google, Perplexity, or Kimi k1.5 are better options
Also Read: OpenAI vs Google: Who Does Deep Research Better?
Task 2: Research
Prompt: “Plot the top five companies in the solar panel industry by revenue last year.”
Output:
Observation:
The research feature is average. It’s fine for quick answers, but for detailed reports or in-depth analysis, use OpenAI’s Deep Research option.
Task 3: Learning a New Topic
Prompt: “Plot sin(x) from -2pi to 2pi, using a step size of 0.05, and explain the math like I’m five.“
Output:
The sine function, or sin(x), is a mathematical function that describes a smooth wave-like pattern. Imagine you’re on a swing, and as you swing back and forth, your height goes up and down in a smooth, repeating pattern. That’s similar to what the sine function looks like!
The sine function takes an input value, x, and gives you a value between -1 and 1. When you plot sin(x) on a graph, you get a wavy line that goes up and down, crossing the x-axis at regular intervals.
In the plot above, the x-axis represents the input values of x, ranging from -2π to 2π. The y-axis represents the corresponding sine values. The step size of 0.05 means that the graph is calculated and plotted at intervals of 0.05 along the x-axis, giving you a smooth and detailed representation of the sine wave.
Think of it like drawing a wavy line on a piece of paper, but instead of drawing it freehand, you’re using a ruler to make sure each point is exactly 0.05 units apart. The result is a beautiful, symmetrical wave that repeats itself as it goes from left to right.
Observation:
The output effectively meets both requirements: it correctly plots sin(x) from -2π to 2π using 0.05 step size with proper code, while explaining the concept with child-friendly language and a relatable swing analogy. The technical implementation and simplified explanation work together seamlessly to fulfill the prompt’s dual objectives.
Conclusion
Command A is defnetily better than the previous cohere models – offering options that every other LLM out there is offering in today’s time. Is it the best model out there? Not really. Try it out yourslef and let me know your experience in the comment section below!
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Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.
