CohereLabs/command-a-plus-05-2026-bf16

Model Card for Command A+

Model Summary

Command A+ is an open source model with 25 billion active parameters and 218B total parameters model optimized for agentic, multilingual, and reasoning-heavy tasks with a focus on enterprise performance, while also providing support for vision inputs for processing image inputs.

Developed by: Cohere and Cohere Labs

• Point of Contact: **Cohere Labs**
• License: Apache 2.0
• Model: command-a-plus-05-2026
• Model Size: 25B active parameters, 218B total parameters
• Context length: 128K input

For more details about this model, please check out our blog post.

You can try out Command A+ before downloading the weights in our hosted Hugging Face Space.

Available quantizations

The following quantizations are available with example minimum GPU requirements

| Quantization | Blackwell | Hopper | | :---- | :---- | :---- | | BF16 (16-bit) | 4 x B200 | 8 x H100 | | FP8 (8-bit) | 2 x B200 | 4 x H100 | | W4A4 (4-bit) | 1 x B200 | 2 x H100 |

All three quantizations show negligible differences in benchmark quality and performance. Our recommended quantization for most uses is [W4A4](https://huggingface.co/CohereLabs/command-a-plus-05-2026-w4a4) which boasts superior speed and latency characteristics alongside a smaller hardware footprint.

For more details, please check out our blog post.

Usage

Transformers

Please install transformers from the source repository that includes the necessary changes for this model.

# pip install transformers
from transformers import AutoTokenizer, AutoModelForImageTextToText

model_id = "CohereLabs/command-a-plus-05-2026-bf16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id)

# Format message with the command-a-plus-05-2026-bf16 chat template
messages = [{"role": "user", "content": "What has keys but can't open locks?"}]
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=4096,
do_sample=True,
temperature=0.6,
top_p=0.95
)

gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)

As a result, you should get an output that looks like this, where the thinking is generated between the ` and `:

The user asks a riddle: "What has keys but can't open locks?" The answer is a piano (or keyboard). So respond with answer.

You can also use the model directly using transformers pipeline abstraction:

from transformers import pipeline
import torch

model_id = "CohereLabs/command-a-plus-05-2026-bf16"
tokenizer = AutoTokenizer.from_pretrained(model_id)

pipe = pipeline(
"text-generation",
model=model_id,
dtype="auto",
device_map="auto",
)

messages = [
{"role": "user", "content": "Explain the Transformer architecture"},
]

text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)

outputs = pipe(
messages,
max_new_tokens=300,
)
print(outputs[0]["generated_text"][-1])

vLLM

You can also run the model in vLLM. vllm>=0.21.0 is required for Command A+ and accurate response parsing also requires installing Cohere’s `melody` library.

uv pip install vllm>=0.21.0
uv pip install transformers uv pip install cohere_melody>=0.9.0

Then the vllm server can be started with the following command:

# This is for B200, adjust tp for your device vllm serve CohereLabs/command-a-plus-05-2026-bf16 -tp 4 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice

Model Details

Input: Text and images.

Output: Model generates text.

Model Architecture: Command A+ is a decoder-only Sparse Mixture-of-Experts Transformer Model. With 25B active parameters and 218B total parameters, it has 128 experts, out of which 8 are active per token, and a single shared expert is applied to all tokens. The attention layers interleave sliding-window attention layers with Rotational Positional Embeddings and global attention layers without positional embeddings in a 3:1 ratio, as first introduced in Command A. The sparse MoE layer is trained in a fully dropless manner and uses a token-choice router. We use additive-bias-based load balancing to encourage balanced token load across all experts, and swap out the softmax router activation function with a normalized sigmoid over the topk expert logits per token.

Languages covered: The model has been trained on 48 languages: English, Arabic, Bulgarian, Bengali, Catalan, Czech, Danish, German, Greek, Spanish, Estonian, Persian, Finnish, Filipino, French, Irish, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Icelandic, Italian, Japanese, Korean, Lithuanian, Latvian, Malay, Maltese, Dutch, Norwegian, Punjabi, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Serbian, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Chinese.

Context Length: Command A+ supports a context length of 128K & 64K output length.

Tool Use Capabilities:

Command A+ has been specifically trained with conversational tool use capabilities. This allows the model to interact with external tools like APIs, databases, or search engines.

Tool use with Command A+ is supported through chat templates in Transformers. We recommend providing tool descriptions using JSON schema.

Tool Use Example [CLICK TO EXPAND]

from transformers import AutoTokenizer

model_id = "CohereLabs/command-a-plus-05-2026-bf16"
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Define tools
tools = [{
"type": "function",
"function": {
"name": "query_daily_sales_report",
"description": "Connects to a database to retrieve overall sales volumes and sales information for a given day.",
"parameters": {
"type": "object",
"properties": {
"day": {
"description":…