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Command A

Parameters

111B

Context Length

256K

Modality

Text

Architecture

Dense

KV Cache Rate

128 KB/tok

License

CC-BY-NC

Release Date

13 Mar 2025

Knowledge Cutoff

Jun 2024

API Pricing (per 1M)

Input: $2.50 · Output: $10.00

Hardware Sizing

Precision

Context

Workload Memory: 210.25 GB

FP16 · 8K context

Base Weights: 206.75 GB

KV Cache: 1.00 GB

Runtime & Graphs: 2.50 GB

Minimum Required Hardware

8x NVIDIA H100 80GB

210.3 / 576.0 GB usable · TP 8

Determine requirements for your specific workload:

Open in VRAM Calculator

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteContext: 256Kx N layersNormPre-AttentionMulti-Head Attention+NormPre-FFNFeed-Forward NetworkSwiGLU+Final NormOutput Logits

Evaluation Benchmarks

Rank

#183

BenchmarkScoreRank

0.229

33

General Text

Text Arena

1354

142

Intelligence Index

Artificial Analysis

0.07

288

Summarization

Archived
ProLLM Summarization

0.858

8

Coding

Archived
Aider Coding

0.12

22

Rankings

Overall Rank

#183

Coding Rank

#148

About Command A

Cohere Command A is a large-scale generative model engineered for high-performance enterprise workflows, particularly those involving tool use, agents, and retrieval-augmented generation (RAG). Developed to provide a high-throughput alternative for production environments, the model maintains a significant parameter count of 111 billion while optimizing for deployment on common dual-GPU hardware configurations. Its design focuses on business-critical accuracy and speed, supporting a standard context window of 256,000 tokens to facilitate the processing of extensive corporate documentation and long-form conversational histories.

The model's architecture is a decoder-only transformer that utilizes several sophisticated structural innovations to balance local and global context. It features a hybrid attention mechanism where three-quarters of the layers employ sliding window attention for efficient local modeling, while every fourth layer uses a full global attention mechanism to maintain long-range dependencies. Technical specifications include the use of Grouped Query Attention (GQA) for optimized inference throughput, SwiGLU activation functions for improved gradient flow, and the omission of bias terms to stabilize the training process. Positional information is handled via Rotary Positional Embeddings (RoPE) in local attention layers, whereas global layers utilize a position-agnostic approach.

Optimized for global enterprise deployment, Command A is trained across 23 languages, including major business languages such as English, French, Spanish, Chinese, and Arabic. The model is specifically aligned for conversational tool use, allowing it to interact with external APIs, databases, and search engines with high precision. This alignment, achieved through supervised fine-tuning and preference optimization, makes it particularly effective for multi-step agentic reasoning and financial data manipulation where extracting numerical details from complex contexts is required.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Position Embedding

Absolute Position Embedding

Activation Function

SwigLU

About Command


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