command-a-plus-05-2026

About

What command-a-plus-05-2026 is

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 All three quantizations show negligible differences in benchmark quality and performance. Our recommended quantization for most uses is 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. As a result, you should get an output that looks like this, where the thinking is generated between the <STARTTHINKING and <ENDTHINKING: You can also use the model directly using transformers pipeline abstraction: 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. Then the vllm server can be started with the following command: 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 train

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys command-a-plus-05-2026 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (command-a-plus-05-2026 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"command-a-plus-05-2026","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

Explore

More language models