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Model detail

Embed v4.0

Provided by Cohere
Pay-as-you-go

A model that allows for text and images to be classified or turned into embeddings

Model specs

Model ID
embed-v4.0
Context length
128K
Max output
–
I/O modalities
Released
2025-04

02

API endpoints

One MixRoute gateway, OpenAI-compatible

  • OpenAI-compatible /v1/chat/completions POST

03

Pricing

Flat rate, unit: /1M Tokens

Input

$0.1200 /1M Tokens

Completion

$0.1200 /1M Tokens

Image input

$0.4700 /1M Tokens

05

Selection summary

Quickly judge whether it fits your workload

What is this model good for?

Retrieval

Embed v4.0 is Cohere's embedding model for retrieval and semantic search. It accepts text, image and file input and returns embeddings. Its capabilities are broken down in the section just below. Embed ten documents and check the vector dimension before you index a corpus.

What should you check before using it?

1,536-dimension vectors

Embed v4.0 accepts up to 128,000 tokens per input and returns 1,536-dimension vectors. Size your documents and queries against that per-input limit before you embed a corpus.

Why use it through MixRoute?

Swap the base URL, keep the Embed v4.0 client

Keep your existing OpenAI client and swap only the base URL. MixRoute supports calling Embed v4.0 through its OpenAI-compatible endpoint; point your client at https://api.mixroute.ai/v1 with the model ID embed-v4.0 and re-run your integration tests.

07

Capabilities & compatibility

Unconfirmed capabilities stay "Unknown," no guesses

Available:

Vision input

embed-v4.0 can process image inputs alongside text.

EX Pass a base64-encoded image in a image_url content part alongside text messages.
Available:

OpenAI SDK

Call embed-v4.0 through MixRoute's OpenAI-compatible endpoint.

EX Change base_url to https://api.mixroute.ai/v1; everything else follows the OpenAI style.

08

Token cost estimator

Live estimate from this page's pricing, not an actual bill

This model has no cache-read price; caching is not counted

09

FAQ

Before switching production traffic to Embed v4.0, run a pilot on a representative slice of your traffic and check output quality, latency at peak concurrency, and cost per request. Verify the error and retry behaviour, and confirm the request limits you depend on match your plan.

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One endpoint, a testable decision

Test this model and alternate routes with the same request format

Start from a real workload, then let quality, total cost, and failure conditions decide whether to send production traffic.

Create an API key View integration docs