Embed English v3.0 - MixRoute API

Cohere text embedding model optimized for English retrieval.

Model Information

Field	Description
`model`	embed-english-v3.0
`context`	512 tokens
`default dimensions`	1024
`endpoint`	POST /v1/embeddings

Context and output sizes describe the model specification. Effective request limits, availability, and billing depend on the MixRoute route.

Capabilities and Usage

Set input_type=search_document for indexed documents and search_query for user queries. classification and clustering select other task types.
This endpoint example embeds text. Do not assume native Cohere image/PDF input objects work in the OpenAI-compatible input field.
Use the same model and vector dimensions for documents and queries. Output vectors are returned in data[].embedding.

Request Example

Set MIXROUTE_API_KEY before making the request.

curl --request POST "https://api.mixroute.ai/v1/embeddings" \
  --header "Authorization: Bearer $MIXROUTE_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "model": "embed-english-v3.0",
  "input": [
    "An API reference for developers."
  ],
  "input_type": "search_document",
  "encoding_format": "float"
}'

Request Fields

Field	Description
`model`	Required. Use the exact model ID above.
`input`	Required text string or array of strings.
`input_type`	Required for Cohere embeddings; select the task type.
`encoding_format`	Use float for a JSON vector array.

Complete request format: embeddings.