Sold by: Cohere
Deployed on AWS
Embed 5 is Cohere's most powerful embedding family yet, delivering frontier retrieval on complex enterprise data that matches or exceeds the competition and makes major gains on Embed 4.
Overview
Every AI application is only as good as the information it retrieves. Embed 5 is Cohere's most powerful embedding family yet, turning reports, scanned pages, charts, contracts, and code into searchable representations of meaning, so the right content surfaces even when the words don't match. That means fewer more accurate answers, fewer wasted LLM tokens, and better end user experiences.
Embed 5 Pro is a state-of-the-art embedding model with leading performance across financial, visually rich, and multimodal content. Use Embed 5 Pro for tasks where accuracy is non-negotiable: offline indexing of company archives, research across filings and earnings calls, testing new regulations against internal policy, or matching clinical questions to guidelines.
Key specifications:
- 128K-token context window for long documents
- Embeds text, image, and mixed text-and-image pages
- Supports 100+ languages, including cross-language search
- Adjustable vector size dimensions (Matryoshka Representation Learning) with float, int8, and binary outputs
- Shared embedding space with Embed 5 Fast
- Works with OpenSearch, LangChain, Pinecone and more
Highlights
- Frontier retrieval quality. Embed 5 Pro is Cohere's highest-quality tier for enterprise search and retrieval, outperforming Voyage, Gemini, and Zembed across key benchmarks. It leads on visually rich documents - scoring 86.1 on ViDoRe V3 and ranking first in six of eight domains - and also tops the tested models on parsed PDFs.
- Domain-specific performance. Use Pro for offline indexing and quality-critical retrieval across legal, healthcare, technical, and other specialized corpora, where document structure and language demand nuance. It is particularly strong on financial content, leading on FinanceBench and FinQA and delivering high-quality retrieval across filings, earnings materials, and other complex financial documents.
- Built for enterprise documents and workloads. Embed 5 handles long documents, page images, and multilingual content, with control over vector size, precision, and deployment. Designed to help optimize retrieval costs, including the vector index that often drives more infrastructure spend than embedding generation itself.
Details
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.12xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $3.36 |
ml.p5.4xlarge Inference (Real-Time) Recommended | Model inference on the ml.p5.4xlarge instance type, real-time mode | $3.36 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.39 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $3.36 |
ml.g6.xlarge Inference (Real-Time) | Model inference on the ml.g6.xlarge instance type, real-time mode | $3.36 |
ml.g6.2xlarge Inference (Real-Time) | Model inference on the ml.g6.2xlarge instance type, real-time mode | $3.36 |
ml.g6e.xlarge Inference (Real-Time) | Model inference on the ml.g6e.xlarge instance type, real-time mode | $3.36 |
ml.g6e.2xlarge Inference (Real-Time) | Model inference on the ml.g6e.2xlarge instance type, real-time mode | $4.42 |
ml.g7e.2xlarge Inference (Real-Time) | Model inference on the ml.g7e.2xlarge instance type, real-time mode | $4.76 |
ml.g7e.4xlarge Inference (Real-Time) | Model inference on the ml.g7e.4xlarge instance type, real-time mode | $8.48 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Embed 5 Pro is a state-of-the-art embedding model from Cohere with leading performance across financial, visually rich, and multimodal content. Embed 5 Pro is built with enterprise documents and workloads in mind, enabling state-of-the-art retrieval performance across a variety of enterprise industries.
Additional details
Inputs
- Summary
This model accepts JSON requests that specifies a content object which can contain a list of texts, a list of data urls of a base64 encoded images or combination. This model supports interleaved images and texts in the same request. { ""content"": [ { ""type"": ""text"", ""text"": ""Look at my awesome car!"" }, { ""type"": ""image"", ""image"": f""data:image/png;base64,{base64_image}"" }, { ""type"": ""text"", ""text"": ""Do you want to buy it?"" }, ]
- Input MIME type
- application/json
{ ""content"": [ { ""type"": ""text"", ""text"": ""Look at my awesome car!"" }, { ""type"": ""image_url"", ""image_url"": ""data:image/png;base64,{base64_image}"" }, { ""type"": ""text"", ""text"": ""Do you want to buy it?"" } ], ""input_type"": ""search_document"", “embedding_types”: [""int8”], ""max_tokens"":8000, “output_dimensions”: 1024 }
Cohere's embedding models do NOT support batch transform.
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
inputs | A list of dicts with the key: “content” which is a list with type which is either text or image_url and then if type='text' then there is a text key with a string. If type=image_url then its a data url formatted base64 encoded image and an image key. The max pixels per image is 2,458,624, the max memory size of a single request is 20mb, and then inputs objects can have at most 96 inputs. The inputs object can have a maximum of 800,000 tokens which is calculated as follows: 1) For each image: image pixels / 784 (pixels per token) = tokens 2) For each text: text tokens 3) Total inputs object = For each input, summation of (1) and (2) The max pixels per image is 2,458,624, the max memory size of a single request is 20mb, and then inputs objects can have at most 96 inputs. | The input data type is categorical. The default value is none. | No |
texts | An array of strings for the model to embed. Maximum number of texts per call is 96. If you are using the texts parameter you cannot use the images parameter in the same call. The input data type is text. | N/A | No |
images | An array of base 64 encoded data url as strings to embed. Maximum number of images per call is 96. You cannot send both an array of texts and images at the same time. | The input data type is text. | No |
input_type | A required field that will prepend special tokens to differentiate each type from one another. The only exception for mixing types would be for search and retrieval, you should embed your corpus with the type search_document and then queries should be embedded with type search_query. | The input data type is categorical. If categorical is chosen: search_document, search_query, classification, and clustering. | Yes |
embeddings_type | Specifies the types of embeddings you want to get back. Not required. If unspecified, returns the float response type. Can be one or more of the types specified in Allowed Values. | The input data type is categorical. If categorical is chosen: float, int8, uint8, binary, and ubinary. | No |
output_dimension | Specifies the length of the output dimensions of the embeddings vector. Not required. if unspecified, returns 1536 dimensions per embedding vector. | Input data type is categorical. If categorical is chosen: {256, 512, 1024, 1536}. The default value is none. | No |
Custom attributes
The following table describes custom attributes for real-time inference endpoints.
Field name | Description | Constraints | Required |
|---|---|---|---|
max_tokens | The maximum number of tokens considered for each input object before it is truncated; the default for this model is set at 128,000 tokens. | The input data type is integer. The default value is 8192. | No |
truncate | One of NONE|LEFT|RIGHT to specify how the API will handle inputs longer than the maximum token length. Passing LEFT will discard the start of the input. RIGHT will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model. If NONE is selected, when the input exceeds the maximum input token length an error will be returned. | - | No |
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