cohere

aid: cohere
accessModel:
  pricing: paid
  onboarding: self-serve
  trial: false
  try_now: false
  public: false
  label: Paid · Self-serve signup
  confidence: high
  source:
  - plans
  - authentication
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/cohere.png
url: https://raw.githubusercontent.com/api-evangelist/cohere/refs/heads/main/apis.yml
apis:
- aid: cohere:chat-api
  name: Cohere Chat API
  tags:
  - Artificial Intelligence
  - Chat
  - Conversational AI
  - Large Language Models
  - Text Generation
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/chat
  properties:
  - url: https://docs.cohere.com/reference/chat
    type: Documentation
  - url: openapi/cohere-chat-api-openapi.yml
    type: OpenAPI
  - url: asyncapi/cohere-asyncapi.yml
    type: AsyncAPI
  - url: graphql/cohere-graphql.md
    type: GraphQL
  description: The Cohere Chat API enables developers to integrate large language model text generation capabilities into
    their applications through a conversational interface. It supports multi-turn conversations, tool use with JSON schema
    definitions, retrieval-augmented generation, and streaming responses. The API is available via the v2 endpoint and works
    with Cohere's Command family of models.
- aid: cohere:embed-api
  name: Cohere Embed API
  tags:
  - Artificial Intelligence
  - Embeddings
  - Natural Language Processing
  - Semantic Search
  - Vector Search
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/embed
  properties:
  - url: https://docs.cohere.com/reference/embed
    type: Documentation
  - url: openapi/cohere-embed-api-openapi.yml
    type: OpenAPI
  description: The Cohere Embed API generates vector embeddings from text and images, enabling semantic search, clustering,
    and classification use cases. It supports multilingual content and can process both text and image inputs using the Embed
    v3 model family. Developers can use these embeddings to build retrieval systems, recommendation engines, and other applications
    that require understanding semantic similarity between content.
- aid: cohere:rerank-api
  name: Cohere Rerank API
  tags:
  - Artificial Intelligence
  - Information Retrieval
  - Relevance
  - Reranking
  - Search
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/rerank
  properties:
  - url: https://docs.cohere.com/reference/rerank
    type: Documentation
  - url: openapi/cohere-rerank-api-openapi.yml
    type: OpenAPI
  description: The Cohere Rerank API takes a query and a list of text documents and returns them ordered by relevance with
    assigned relevance scores. It is commonly used as a second-stage ranker in retrieval-augmented generation pipelines to
    improve the quality of search results before passing them to a language model. The API supports multilingual reranking
    and can significantly improve the precision of search and retrieval systems.
- aid: cohere:classify-api
  name: Cohere Classify API
  tags:
  - Artificial Intelligence
  - Classification
  - Natural Language Processing
  - Text Analysis
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/classify
  properties:
  - url: https://docs.cohere.com/reference/classify
    type: Documentation
  - url: openapi/cohere-classify-api-openapi.yml
    type: OpenAPI
  description: The Cohere Classify API performs text classification by assigning labels to input text based on provided examples.
    It can be used for sentiment analysis, content moderation, topic categorization, and other classification tasks. Developers
    provide a set of labeled examples along with texts to classify, and the API returns predicted labels with confidence scores
    for each input.
- aid: cohere:embed-jobs-api
  name: Cohere Embed Jobs API
  tags:
  - Artificial Intelligence
  - Batch Processing
  - Embeddings
  - Vector Search
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/list-embed-jobs
  properties:
  - url: https://docs.cohere.com/reference/list-embed-jobs
    type: Documentation
  - url: openapi/cohere-embed-jobs-api-openapi.yml
    type: OpenAPI
  description: The Cohere Embed Jobs API allows developers to create and manage batch embedding jobs for processing large
    volumes of text data asynchronously. Rather than embedding texts one at a time, developers can submit datasets for bulk
    embedding and monitor job progress. This is useful for initializing vector databases, processing large document collections,
    and other scenarios where embedding large amounts of content is needed.
- aid: cohere:datasets-api
  name: Cohere Datasets API
  tags:
  - Artificial Intelligence
  - Data Management
  - Datasets
  - Fine-Tuning
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/list-datasets
  properties:
  - url: https://docs.cohere.com/reference/list-datasets
    type: Documentation
  - url: openapi/cohere-datasets-api-openapi.yml
    type: OpenAPI
  description: The Cohere Datasets API provides endpoints for uploading, managing, and retrieving datasets used with other
    Cohere services such as fine-tuning and embed jobs. Developers can create datasets from files, list existing datasets,
    retrieve dataset metadata, and delete datasets they no longer need. The API supports various data formats and validates
    uploaded data against expected schemas.
- aid: cohere:models-api
  name: Cohere Models API
  tags:
  - Artificial Intelligence
  - Machine Learning
  - Models
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/list-models
  properties:
  - url: https://docs.cohere.com/reference/list-models
    type: Documentation
  - url: openapi/cohere-models-api-openapi.yml
    type: OpenAPI
  description: The Cohere Models API allows developers to list and retrieve information about available Cohere models, including
    the Command, Embed, and Rerank model families. It provides details such as model names, versions, supported endpoints,
    context lengths, and capabilities. This API is useful for programmatically discovering which models are available and
    selecting the appropriate model for a given task.
- aid: cohere:tokenize-api
  name: Cohere Tokenize API
  tags:
  - Artificial Intelligence
  - Natural Language Processing
  - Text Processing
  - Tokenization
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/tokenize
  properties:
  - url: https://docs.cohere.com/reference/tokenize
    type: Documentation
  - url: openapi/cohere-tokenize-api-openapi.yml
    type: OpenAPI
  description: The Cohere Tokenize API splits input text into tokens using the tokenizer associated with a specified model.
    It returns both the token strings and their corresponding token IDs. This is useful for understanding how text will be
    processed by Cohere models, estimating token counts for billing purposes, and debugging input formatting issues.
- aid: cohere:detokenize-api
  name: Cohere Detokenize API
  tags:
  - Artificial Intelligence
  - Natural Language Processing
  - Text Processing
  - Tokenization
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  baseURL: https://api.cohere.com
  humanURL: https://docs.cohere.com/reference/detokenize
  properties:
  - url: https://docs.cohere.com/reference/detokenize
    type: Documentation
  - url: openapi/cohere-detokenize-api-openapi.yml
    type: OpenAPI
  description: The Cohere Detokenize API converts a sequence of token IDs back into their corresponding text string using
    the tokenizer for a specified model. It is the inverse operation of the Tokenize API and is useful for inspecting model
    outputs at the token level, debugging tokenization behavior, and reconstructing text from token representations.
common:
- type: AgenticAccess
  url: agentic-access/cohere-agentic-access.yml
- type: DomainSecurity
  url: security/cohere-domain-security.yml
- type: Authentication
  url: authentication/cohere-authentication.yml
- url: https://cohere.com/blog
  type: Blog
- type: GitHubOrganization
  url: https://github.com/cohere-ai
- type: LinkedIn
  url: https://www.linkedin.com/company/cohere-ai
- type: JSONLD
  url: json-ld/cohere-context.jsonld
- type: JSONSchema
  url: json-schema/cohere-chat-message-schema.json
- type: JSONSchema
  url: json-schema/cohere-embedding-schema.json
- type: JSONSchema
  url: json-schema/cohere-model-schema.json
- type: JSONSchema
  url: json-schema/cohere-dataset-schema.json
- type: Features
  data:
  - Command at $1/$2 per MTok input/output
  - Command-light at $0.30/$0.60
  - Command R at $0.50/$1.50
  - Command R+ 08-2024 at $2.50/$10
  - Command R+ 04-2024 at $3/$15
  - Aya Expanse multilingual at $0.50/$1.50
  - Embed v3 with English/multilingual
  - Rerank for relevance scoring
  - 'Production keys: 10K req/min Chat, 2K Embed, 10K Rerank'
  - 'Trial keys: 20/min Chat, 100/min Embed'
  - Connectors for RAG over data sources
  - Tool use and function calling
  - Compass for unstructured data search
  - Available on AWS Bedrock, Azure, Oracle Cloud
  - Fine-tuning for Command and Aya
  - OpenAI-compatible Chat Completions endpoint
  sources:
  - https://cohere.com/pricing
  updated: '2026-05-04'
- type: LlmsText
  url: https://docs.cohere.com/llms.txt
modified: '2026-05-29'
description: Generates a text response to a user message and streams it down, token by token.