Sold by: MongoDB, Inc
Deployed on AWS
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Rich multimodal embedding model that can vectorize interleaved text, content-rich images, and video. 32K context length.
Overview
voyage-multimodal-3.5 is a state-of-the-art multimodal embedding model capable of vectorizing not only text, images, and video individually, but also content that interleaves all three modalities. It delivers excellent performance for mixed-modality searches involving text and visual content such as PDF screenshots, figures, tables, videos, and more. Enabled by Matryoshka learning and quantization-aware training, voyage-multimodal-3.5 supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options.
Learn more about voyage-multimodal-3.5 here: https://blog.voyageai.com/2026/01/15/voyage-multimodal-3-5
Highlights
- State-of-the-art multimodal embedding model capable of vectorizing not only text, images, and video individually, but also content that interleaves all three modalities. It delivers excellent performance for mixed-modality searches involving text and visual content such as PDF screenshots, figures, tables, videos, and more.
- Supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization, including float (32-bit floating point), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8).
- 32K token context length.
Details
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $3.03 |
ml.g6.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.2xlarge instance type, real-time mode | $2.44 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.82 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $3.03 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $4.06 |
ml.g5.8xlarge Inference (Real-Time) | Model inference on the ml.g5.8xlarge instance type, real-time mode | $6.12 |
ml.g6.xlarge Inference (Real-Time) | Model inference on the ml.g6.xlarge instance type, real-time mode | $2.25 |
ml.g6.4xlarge Inference (Real-Time) | Model inference on the ml.g6.4xlarge instance type, real-time mode | $3.31 |
ml.g6.8xlarge Inference (Real-Time) | Model inference on the ml.g6.8xlarge instance type, real-time mode | $5.04 |
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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
MongoDB is excited to announce the initial release of voyage-multimodal-3.5
Additional details
Inputs
- Summary
- inputs (List[dict]) – A list of multimodal inputs. Each input contains a content list of dictionaries with the following keys:
- type (string): text, image_base64, or video_base64.
- text (string): Text string (required if type is text).
- image_base64 / video_base64 (string): Data URL format (e.g., data:image/jpeg;base64,...).
- input_type (string, optional, default = null) – The role of the input: query, document, or null.
- truncation (bool, optional, default = true) – Whether to truncate inputs to fit context limits.
- output_encoding (string, optional, default = null) – Format of the embeddings: null (list of floats) or base64.
- output_dimension (int, optional, default = 1024) – Supported dimensions: 2048, 1024, 512, 256.
- output_dtype (string, optional, default = "float") – Data type for embeddings: float, int8, uint8, binary, or ubinary.
- id (string, optional, default=null) - Batch request ID.
- inputs (List[dict]) – A list of multimodal inputs. Each input contains a content list of dictionaries with the following keys:
- Limitations for input type
- Maximum Inputs: 1,000 per request. Per-Input Limit: 32,000 tokens. Total Request Limit: 320,000 tokens across all inputs. Image/Video Constraints: - Size: Max 20 MB per file. - Image Resolution: Max 16 million pixels. - Token Conversion: 560 pixels = 1 token (images); 1,120 pixels = 1 token (video).
- Input MIME type
- application/json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_multimodal_embedding_input.json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_batch_input_multimodal_embedding.jsonl
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 multimodal inputs. Each input contains a content list of dictionaries with the following keys:
- type (string): text, image_base64, or video_base64.
- text (string): Text string (required if type is text).
- image_base64 / video_base64 (string): Data URL format (e.g., data:image/jpeg;base64,...).
| Maximum Inputs: 1,000 per request.
Per-Input Limit: 32,000 tokens.
Total Request Limit: 320,000 tokens across all inputs.
Image/Video Constraints:
- Size: Max 20 MB per file.
- Image Resolution: Max 16 million pixels.
- Token Conversion: 560 pixels = 1 token (images); 1,120 pixels = 1 token (video). | Yes |
input_type | The role of the input: query, document, or null.
| Default value: null
Type: string | No |
truncation | Whether to truncate inputs to fit context limits. | Default value: true
Type: boolean | No |
output_encoding | Format of the embeddings: null (list of floats) or base64.
| Default value: null
Type: string | No |
output_dimension | Supported dimensions: 2048, 1024, 512, 256.
| Default value: 1024
Type: int | No |
output_dtype | Data type for embeddings: float, int8, uint8, binary, or ubinary. | Default value: "float"
Type: string | No |
id | Batch request ID. | Default value: null
Type: string | No |
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