Command A Description

Cohere’s Command A is a 111B-parameter model engineered for enterprise AI, excelling in agentic workflows, retrieval-augmented generation, and multilingual tasks. Built on a dense transformer architecture, it supports 23 languages and delivers precise, data-grounded insights for professional applications like coding, automation, and conversational intelligence.

Technical Specification

Command A optimizes enterprise AI for precision and efficiency.

Performance Benchmarks

Based on Cohere’s reported metrics:

• MMLU: 85.5%.
• MATH: 80.0%.
• IFEval: 90.0%.
• BFCL: 63.8%.
• Taubench: 51.7%.

Performance Metrics

Command A demonstrates solid performance in enterprise AI benchmarks, achieving 85.5% on MMLU for reasoning, 63.8% on BFCL for business function calling, and 51.7% on Taubench for coding accuracy, indicating moderate performance in SQL and code translation. It scores 80.0% on MATH and 90.0% on IFEval, reflecting strong reasoning and instruction-following capabilities. Users note effective multilingual support across 23 languages and reliable RAG for data-grounded insights.

Features

• Dense transformer architecture optimized for tool use and retrieval-augmented generation (RAG).
• Supports 23 languages: Arabic, Chinese (Simplified, Traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, Vietnamese.
• Chat endpoint with RAG, tool use, and citation capabilities.
• 150% higher throughput than Command R+ (08-2024), runs on two A100/H100 GPUs.
• Contextual and strict safety modes for flexible guardrails.
• API Pricing: - .

Optimal Use Cases

• Coding: Generates SQL queries, code translations, and software prototypes with high accuracy.
• Retrieval-Augmented Generation: Provides data-grounded insights for financial analysis and research.
• Multilingual Task Automation: Translates and summarizes across 23 languages for global workflows.
• Agentic AI Systems: Automates business processes with tool-integrated intelligence.
• Conversational Intelligence: Powers context-rich, multilingual chatbots for enterprise needs.

Code Samples

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Parameters

• model: string - Specifies the model.
• prompt: string - Text input describing the task or query for generation.
• max_tokens: integer - Maximum number of tokens to generate.
• temperature: float - Controls response randomness, range 0.0 to 5.0.
• tools: array - List of tools for agentic workflows.
• language: string - Target language for multilingual tasks, e.g., "en", "fr", "ja".
• use_rag: boolean - Enables retrieval-augmented generation if true.

Comparison with Other Models

• Vs. DeepSeek V3: Command A’s 85.5% MMLU is slightly below DeepSeek V3’s ~88.5%, and 51.7% Taubench trails its ~70%. Command A’s 256K context exceeds DeepSeek V3’s 128K, offering an edge in RAG.
• Vs. GPT-4o: Command A’s 85.5% MMLU is competitive with GPT-4o’s ~87.5%, but 51.7% Taubench lags behind GPT-4o’s ~80%. Command A’s 256K context surpasses GPT-4o’s 128K.
• Vs. Llama 3.1 8B: Command A’s 85.5% MMLU outperforms Llama 3.1 8B’s ~68.4%, and 51.7% Taubench exceeds its ~61%. Command A’s 256K context far outstrips Llama 3.1 8B’s 8K.

API Integration

Accessible via AI/ML API. Documentation: available here.