Command A

Command A Description

Command A is a 111-billion-parameter dense transformer model developed by Cohere, tailored for enterprise AI applications. It excels in agentic workflows, retrieval-augmented generation (RAG), and multilingual tasks, delivering precise, data-grounded insights across 23 languages. Command A is optimized for efficiency and professional use cases such as coding, automation, and conversational intelligence.

Technical Specification

Command A leverages a dense transformer architecture optimized for tool integration and RAG workflows. It supports a broad multilingual spectrum covering 23 languages including Arabic, Chinese (Simplified and Traditional), Russian, and Vietnamese. The model runs efficiently on two A100/H100 GPUs, achieving 150% higher throughput than its predecessor.

Performance Benchmarks

Based on Cohere’s reported metrics:

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

These metrics highlight strong reasoning (MMLU, IFEval), mathematical problem-solving (MATH), and moderate coding accuracy and business function calling capabilities (Taubench, BFCL). Command A supports a 256K token context window for extended document and workflow handling.

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.

Key Capabilities

• Enterprise-grade agentic AI: Integrates external tools for autonomous workflows.
• Retrieval-Augmented Generation (RAG): Provides reliable, data-grounded outputs with citation features.
• Multilingual support: Enables translation, summarization, and automation across 23 languages.
• High throughput: Optimized for large-scale usage with increased efficiency over previous versions.
• Flexible safety modes: Offers contextual and strict safety guardrails for varied deployment needs.

• API Pricing:
• Input: $3.42875
• Output: $13.715

Optimal Use Cases

• Coding assistance including SQL query generation and code translation.
• Data-driven research and financial analysis via RAG.
• Multilingual task automation for global enterprise workflows.
• Business process automation with integrated AI tools.
• Powering sophisticated, context-rich multilingual conversational agents.

Code Samples

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.