Sold by: Cohere
Deployed on AWS
Cohere Parse 5 is a high-performance document vision parser.
Overview
Cohere Parse 5 is a high-performance document vision parser that delivers market-leading document understanding at its size and price range. The gateway to enterprise document intelligence, Parse 5 transforms complex, multilingual documents into structured, machine-readable content for high-throughput AI workloads.
Highlights
- Cohere Parse 5 converts document images into structured Markdown or content blocks; extracting text, tables, forms, and images with spatial bounding boxes across nine commercial languages. Limitations: image input only (pre-render PDF/PPT to images); chart/graph extraction, confidence scores, and citation output not supported; on SageMaker, images must be supplied as base64 data URIs.
- Beyond OCR: Understands tables, forms, diagrams, and images to extract richer semantic context across major global commercial languages.
- Spatially aware: Returns bounding boxes alongside extracted visual elements, enabling downstream retrieval, grounding, and automation use cases.
Details
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Latest version
Deployed on AWS
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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.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $2.51 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.51 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $2.51 |
ml.p5.4xlarge Inference (Real-Time) | Model inference on the ml.p5.4xlarge instance type, real-time mode | $3.36 |
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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
We’re thrilled to announce the release of Parser, the most recent entrant into the Embed family of enterprise-focused large language models (LLMs).
Parser is Cohere’s most performant search model to date - the model supports the following new features:
- Matryoshka Embeddings in the following dimensions: [256, 512, 1024, 1536]
- Unified Embeddings from mixed modality input (i.e. a single payload of image(s) and text(s))
- Context length of 128k
Parser is State of the Art in the following areas:
- Text to Text Retrieval
- Text to Image Retrieval
- Text to Mixed Modality Retrieval (e.g. PDFs)
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_url"", ""image_url"": 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
}"
N/a batch job unavailable
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)" | - | 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. | - | 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, | - | No |
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