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# An Overview of Cohere's Models

Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case.

Cohere has a variety of models that cover many different use cases. If you need more customization, you can tune your prompts to adjust its behavior to your specific use case.

Cohere models are currently available on the following platforms:

• Cohere’s proprietary platform
• Amazon SageMaker
• Amazon Bedrock
• Microsoft Azure
• Oracle GenAI Service

At the end of each major section below, you'll find technical details about how to call a given model on a particular platform.

# What can These Models Be Used For?

In this section, we'll provide some high-level context on Cohere's offerings, and what the strengths of each are.

• The Command family of models includes Command A+, Command A, Command R7B, Command A Translate, Command A Reasoning, Command A Vision, Command R+, Command R, and Command. Together, they are the text-generation LLMs powering tool-using agents, retrieval augmented generation (RAG), translation, copywriting, and similar use cases. They work through the Chat endpoint, which can be used with or without RAG.
• Rerank is the fastest way to inject the intelligence of a language model into an existing search system. It can be accessed via the Rerank endpoint.
• Embed improves the accuracy of search, classification, clustering, and RAG results. It powers the Embed endpoint.
• Parse extracts structured, machine-readable data from unstructured enterprise documents, like forms and PDFs. It powers the Parse endpoint.
• Cohere Transcribe is Cohere's dedicated audio transcription model for automatic speech recognition (ASR). It powers the Audio Transcriptions endpoint. Cohere Transcribe Arabic is a version of the model optimized for Arabic-language audio.
• The Aya family of models are aimed at expanding the number of languages covered by generative AI. Aya Expanse covers 23 languages, and Aya Vision is fully multimodal, allowing you to pass in images and text and get a single coherent response. Both are available on the Chat endpoint.
• The North family includes purpose-built models such as North Small Translate for machine translation and North Mini Code for agentic coding. Both are available through the Chat endpoint and support production deployment through Model Vault.

# Command

Command is Cohere's default generation model that takes a user instruction (or command) and generates text following the instruction. Our Command models also have conversational capabilities, meaning they are well-suited for chat applications, and Command A Vision can interact with image inputs.

# Using Command Models on Different Platforms

In this table, we provide some important context for using Cohere Command models on Amazon Bedrock, Amazon SageMaker, and more.

# North

North is Cohere's family of purpose-built generative models. North models are available on the Cohere API for evaluation and through Model Vault for production deployment.

# Embed

These models can be used to generate embeddings from text or classify it based on various parameters. Embeddings can be used for estimating semantic similarity between two sentences, choosing a sentence which is most likely to follow another sentence, or categorizing user feedback. The Representation model comes with a variety of helper functions, such as for detecting the language of an input.

# Using Embed Models on Different Platforms

In this table, we provide some important context for using Cohere Embed models on Amazon Bedrock, Amazon SageMaker, and more.

# Rerank

The Rerank model can improve created models by re-organizing their results based on certain parameters. This can be used to improve search algorithms.

# Using Rerank Models on Different Platforms

In this table, we provide some important context for using Cohere Rerank models on Amazon Bedrock, SageMaker, and more.

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Rerank accepts full strings rather than tokens, so the token limit works a little differently. Rerank will automatically chunk documents longer than 510 tokens, and there is therefore no explicit limit to how long a document can be when using rerank. See our best practice guide for more info about formatting documents for the Rerank endpoint.

# Parse

Parse is our vision parsing model for extracting structured data from enterprise documents that can be used in AI search and agentic applications.

# Using Parse Models on Different Platforms

In this table, we provide some important context for using Cohere Parse on Amazon SageMaker and Azure Foundry.

# Audio

Cohere Transcribe is our dedicated model for audio-in, text-out automatic speech recognition (ASR) workloads. For Arabic-language transcription, use Cohere Transcribe Arabic for best-in-class performance.

# Using Audio Models on Different Platforms

Cohere Transcribe is available on Microsoft Foundry under 'coherelabs-cohere-transcribe-03-2026'. Cohere Transcribe Arabic is not yet available on other platforms.

# Aya

Aya is a family of multilingual large language models designed to expand the number of languages covered by generative AI for purposes of research and to better-serve minority linguistic communities.

The 32-billion parameter Aya Expanse offering is optimized to perform well in these 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese.

The 32-billion parameter Aya Vision model is a state-of-the-art multimodal model excelling at a variety of critical benchmarks for language, text, and image capabilities.

Tiny Aya is a compact 3.35B-parameter multilingual model supporting 70 languages. Its instruction-tuned variants are available on the Cohere API via the Chat endpoint and as open-weight models on Hugging Face.