---
title: "Models Overview"
slug: "docs/models"
hidden: false
description: "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."
image: "../../assets/images/672b039-cohere_docs_preview_image_1200x630_copy.jpg"
keywords: "large language models, generative AI models"
createdAt: "Thu Apr 20 2023 18:10:10 GMT+0000 (Coordinated Universal Time)"
updatedAt: "Mon Jun 10 2024 15:57:07 GMT+0000 (Coordinated Universal Time)"
---
Cohere has a variety of models that cover many different use cases. If you need more customization, you can [train a model](/docs/fine-tuning) to tune it to your specific use case.
Cohere models are currently available on the following platforms:
- [Cohere’s proprietary platform](https://dashboard.cohere.com/playground/chat)
- [Amazon SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=87af0c85-6cf9-4ed8-bee0-b40ce65167e0)
- [Amazon Bedrock](https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=cohere.command-r-plus-v1:0)
- [Microsoft Azure](https://ai.azure.com/explore/models/?tid=694fed05-7f6d-4ab2-8c38-9afb438eab6f&selectedCollection=cohere)
- [Oracle GenAI Service](https://www.oracle.com/artificial-intelligence/generative-ai/generative-ai-service/)
At the end of each major sections 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](https://cohere.com/models/command?_gl=1*15hfaqm*_ga*MTAxNTg1NTM1MS4xNjk1MjMwODQw*_ga_CRGS116RZS*MTcxNzYwMzYxMy4zNTEuMS4xNzE3NjAzNjUxLjIyLjAuMA..), [Command R](/docs/command-r), and [Command R+](/docs/command-r-plus). Together, they are the text-generation LLMs powering conversational agents, summarization, copywriting, and similar use cases. They work through the [Chat](/reference/chat) endpoint, which can be used with or without [retrieval augmented generation](/docs/retrieval-augmented-generation-rag) (RAG).
- [Rerank](https://cohere.com/blog/rerank/?_gl=1*1t6ls4x*_ga*MTAxNTg1NTM1MS4xNjk1MjMwODQw*_ga_CRGS116RZS*MTcxNzYwMzYxMy4zNTEuMS4xNzE3NjAzNjUxLjIyLjAuMA..) is the fastest way to inject the intelligence of a language model into an existing search system. It can be accessed via the [Rerank](/reference/rerank-1) endpoint.
- [Embed](https://cohere.com/models/embed?_gl=1*1t6ls4x*_ga*MTAxNTg1NTM1MS4xNjk1MjMwODQw*_ga_CRGS116RZS*MTcxNzYwMzYxMy4zNTEuMS4xNzE3NjAzNjUxLjIyLjAuMA..) improves the accuracy of search, classification, clustering, and RAG results. It also powers the [Embed](/reference/embed) and [Classify](/reference/classify) endpoints.
## 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 which means that they are well-suited for chat applications.
| Model Name | Description | Modality | Context Length | Maximum Output Tokens | Endpoints |
|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------|----------------|-----------------------|-------------------------------------------------------------------------------------------|
| `command-r-plus-08-2024` | `command-r-plus-08-2024` is an update of the Command R+ model, delivered in August 2024. Find more information [here](https://docs.cohere.com/changelog/command-gets-refreshed) | Text | 128k | 4k | [Chat](/reference/chat) |
| `command-r-plus-04-2024` | Command R+ is an instruction-following conversational model that performs language tasks at a higher quality, more reliably, and with a longer context than previous models. It is best suited for complex RAG workflows and multi-step tool use. | Text | 128k | 4k | [Chat](/reference/chat) |
| `command-r-plus` | `command-r-plus` is an alias for `command-r-plus-04-2024`, so if you use `command-r-plus` in the API, that's the model you're pointing to. | Text | 128k | 4k | [Chat](/reference/chat) |
| `command-r-08-2024` | `command-r-08-2024` is an update of the Command R model, delivered in August 2024. Find more information [here](https://docs.cohere.com/changelog/command-gets-refreshed) | Text | 128k | 4k | [Chat](/reference/chat) |
| `command-r-03-2024` | Command R is an instruction-following conversational model that performs language tasks at a higher quality, more reliably, and with a longer context than previous models. It can be used for complex workflows like code generation, retrieval augmented generation (RAG), tool use, and agents. | Text | 128k | 4k | [Chat](/reference/chat) |
| `command-r` | `command-r` is an alias for `command-r-03-2024`, so if you use `command-r` in the API, that's the model you're pointing to. | Text | 128k | 4k | [Chat](/reference/chat) |
| `command` | An instruction-following conversational model that performs language tasks with high quality, more reliably and with a longer context than our base generative models. | Text | 4k | 4k | [Chat](/reference/chat),
[Summarize](/reference/summarize) |
| `command-nightly` | To reduce the time between major releases, we put out nightly versions of command models. For `command`, that is `command-nightly`.
Be advised that `command-nightly` is the latest, most experimental, and (possibly) unstable version of its default counterpart. Nightly releases are updated regularly, without warning, and are not recommended for production use. | Text | 128k | 128k | [Chat](/reference/chat) |
| `command-light` | A smaller, faster version of `command`. Almost as capable, but a lot faster. | Text | 4k | 4k | [Chat](/reference/chat),
[Summarize](/reference/summarize-2) |
| `command-light-nightly` | To reduce the time between major releases, we put out nightly versions of command models. For `command-light`, that is `command-light-nightly`.
Be advised that `command-light-nightly` is the latest, most experimental, and (possibly) unstable version of its default counterpart. Nightly releases are updated regularly, without warning, and are not recommended for production use. | Text | 4k | 4k | [Chat](/reference/chat) |
| `c4ai-aya-expanse-8b` | Aya Expanse is a highly performant 8B multilingual model, designed to rival monolingual performance through innovations in instruction tuning with data arbitrage, preference training, and model merging. Serves 23 languages. | Text | 8k | 4k | [Chat](/reference/chat) |
| `c4ai-aya-expanse-32b` | Aya Expanse is a highly performant 32B multilingual model, designed to rival monolingual performance through innovations in instruction tuning with data arbitrage, preference training, and model merging. Serves 23 languages. | Text | 8k | 4k | [Chat](/reference/chat) |
### 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.
| Model Name | Amazon Bedrock Model ID | Amazon SageMaker | Azure AI Studio Model ID | Oracle OCI Generative AI Service |
| :---------------------- | :------------------------------ | :-------------------- | :----------------------- | :------------------------------- |
| `command-r-plus` | `cohere.command-r-plus-v1:0` | Unique per deployment | Unique per deployment | `cohere.command-r-plus v1.2` |
| `command-r` | `cohere.command-r-v1:0` | Unique per deployment | Unique per deployment | `cohere.command-r-16k v1.2` |
| `command` | `cohere.command-text-v14` | N/A | N/A | `cohere.command v15.6` |
| `command-nightly` | N/A | N/A | N/A | N/A |
| `command-light` | `cohere.command-light-text-v14` | N/A | N/A | `cohere.command-light v15.6` |
| `command-light-nightly` | N/A | N/A | N/A | N/A |
| | | | | |
## 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, while outputs from the Classify endpoint can be used for any classification or analysis task. The Representation model comes with a variety of helper functions, such as for detecting the language of an input.
| Model Name | Description | Modalities | Dimensions | Context Length | Similarity Metric | Endpoints |
|-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------|------------|----------------|---------------------|----------------------------------------------------------------------|
| `embed-english-v3.0` | A model that allows for text to be classified or turned into embeddings. English only. | Text, Images | 1024 | 512 | Cosine Similarity | [Embed](/reference/embed),
[Embed Jobs](/reference/embed-jobs) |
| `embed-english-light-v3.0` | A smaller, faster version of `embed-english-v3.0`. Almost as capable, but a lot faster. English only. | Text, Images | 384 | 512 | Cosine Similarity | [Embed](/reference/embed),
[Embed Jobs](/reference/embed-jobs) |
| `embed-multilingual-v3.0` | Provides multilingual classification and embedding support. [See supported languages here.](/docs/supported-languages) | Text, Images | 1024 | 512 | Cosine Similarity | [Embed](/reference/embed), [Embed Jobs](/reference/embed-jobs) |
| `embed-multilingual-light-v3.0` | A smaller, faster version of `embed-multilingual-v3.0`. Almost as capable, but a lot faster. Supports multiple languages. | Text, Images | 384 | 512 | Cosine Similarity | [Embed](/reference/embed),
[Embed Jobs](/reference/embed-jobs) |
| | | | | | | |
| `embed-english-v2.0` | Our older embeddings model that allows for text to be classified or turned into embeddings. English only | Text | 4096 | 512 | Cosine Similarity | [Classify](/reference/classify), [Embed](/reference/embed) |
| `embed-english-light-v2.0` | A smaller, faster version of embed-english-v2.0. Almost as capable, but a lot faster. English only. | Text | 1024 | 512 | Cosine Similarity | [Classify](/reference/classify), [Embed](/reference/embed) |
| `embed-multilingual-v2.0` | Provides multilingual classification and embedding support. [See supported languages here.](/docs/supported-languages) | Text | 768 | 256 | Dot Product Similarity | [Classify](/reference/classify), [Embed](/reference/embed) |
In this table we've listed older `v2.0` models alongside the newer `v3.0` models, but we recommend you use the `v3.0` versions.
### 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.
| Model Name | Amazon Bedrock Model ID | Amazon SageMaker | Azure AI Studio Model ID | Oracle OCI Generative AI Service |
| :------------------------------ | :----------------------------- | :-------------------- | :----------------------- | :------------------------------------- |
| `embed-english-v3.0` | `cohere.embed-english-v3` | Unique per deployment | Unique per deployment | `cohere.embed-english-v3.0` |
| `embed-english-light-v3.0` | N/A | N/A | N/A | `cohere.embed-english-light-v3.0` |
| `embed-multilingual-v3.0` | `cohere.embed-multilingual-v3` | Unique per deployment | Unique per deployment | `cohere.embed-multilingual-v3.0` |
| `embed-multilingual-light-v3.0` | N/A | N/A | N/A | `cohere.embed-multilingual-light-v3.0` |
| `embed-english-v2.0` | N/A | N/A | N/A | N/A |
| `embed-english-light-v2.0` | N/A | N/A | N/A | `cohere.embed-english-light-v2.0` |
| `embed-multilingual-v2.0` | N/A | N/A | N/A | N/A |
## 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.
| Model Name | Description | Modalities | Context Length | Endpoints |
| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------- | ---------------|---------------------------- |
| `rerank-english-v3.0` | A model that allows for re-ranking English Language documents and semi-structured data (JSON). This model has a context length of 4096 tokens. | Text | 4k | [Rerank](/reference/rerank) |
| `rerank-multilingual-v3.0` | A model for documents and semi-structure data (JSON) that are not in English. Supports the same languages as embed-multilingual-v3.0. This model has a context length of 4096 tokens. | Text | 4k | [Rerank](/reference/rerank) |
| | | | | |
| `rerank-english-v2.0` | A model that allows for re-ranking English language documents. | Text | 512 | [Rerank](/reference/rerank) |
| `rerank-multilingual-v2.0` | A model for documents that are not in English. Supports the same languages as `embed-multilingual-v3.0`. | Text | 512 | [Rerank](/reference/rerank) |
### 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.
| Model Name | Amazon Bedrock Model ID | Amazon SageMaker | Azure AI Studio Model ID | Oracle OCI Generative AI Service |
| :------------------------- | :---------------------- | :-------------------- | :----------------------- | :------------------------------- |
| `rerank-english-v3.0` | Not yet available | Unique per deployment | Not yet available | N/A |
| `rerank-multilingual-v3.0` | Not yet available | Unique per deployment | Not yet available | N/A |
| `rerank-english-v2.0` | N/A | N/A | N/A | N/A |
| `rerank-multilingual-v2.0` | N/A | N/A | N/A | N/A |
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](/docs/reranking-best-practices) for more info about formatting documents for the Rerank endpoint.