Sold by: MongoDB, Inc
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Code embedding model purpose-built for coding agents and code search, with state-of-the-art agentic code retrieval quality. 32K context length.
Overview
Text embedding models are neural networks that transform texts into numerical vectors. They are a foundational building block for semantic search/retrieval systems and retrieval-augmented generation (RAG) and directly determine retrieval quality.
voyage-code-4 is the next generation of Voyage AI's code embedding model, purpose-built to improve performance and reduce cost for coding agents. Coding agents now issue many of the code retrieval queries served in production: they explore, backtrack, and re-query across multiple steps, often starting from a goal as vague as a bug report rather than a known identifier. Most agents rely entirely on full-text search (grep), which works well when the agent already knows the symbol it is looking for, but returns no useful hits when the query describes a symptom rather than syntax. Semantic retrieval with voyage-code-4 complements full-text search and significantly reduces wasted token usage.
voyage-code-4 is trained on an entirely new corpus mined from completed, issue-fixing pull requests spanning tens of thousands of repositories and hundreds of programming languages - substantially larger than the corpus used for voyage-code-3. Conventional code embedding corpora pair source files with docstrings, comments, or synthetic questions, which teach a model what code says. They do not teach a model what code does wrong, which is the context an agent needs when it starts from a bug report.
voyage-code-4 outperforms Cohere Embed v4 and Gemini Embedding 2 by an average of 28.25% and 31.03% on agentic code retrieval, a new benchmark suite of 19 datasets built from issue-fixing pull requests, and by 19.21% and 16.01% across the 28 code retrieval datasets used to evaluate voyage-code-3. It surpasses voyage-code-3 itself by 27.54% and 13.98% on those two suites respectively, and OpenAI v3 large by 48.58% and 40.06%. Enabled by Matryoshka representation learning and quantization-aware training, voyage-code-4 supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple quantization options.
Learn more about voyage-code-4 here: https://blog.voyageai.com/2026/07/30/voyage-code-4/
Highlights
- Code embedding model purpose-built for coding agents, trained on a new corpus mined from issue-fixing pull requests across tens of thousands of repositories and hundreds of programming languages - so it can locate the code that needs to change given only a symptom.
- Outperforms Cohere Embed v4 and Gemini Embedding 2 by an average of 28.25% and 31.03% on agentic code retrieval, and by 19.21% and 16.01% across the 28 code retrieval datasets used to evaluate voyage-code-3.
- 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).
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g6.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.xlarge instance type, real-time mode | $2.25 |
ml.g6.2xlarge Inference (Batch) Recommended | Model inference on the ml.g6.2xlarge instance type, batch 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.g6.2xlarge Inference (Real-Time) | Model inference on the ml.g6.2xlarge instance type, real-time mode | $2.44 |
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 |
ml.g7e.2xlarge Inference (Real-Time) | Model inference on the ml.g7e.2xlarge instance type, real-time mode | $4.49 |
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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.
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Version release notes
MongoDB is excited to announce the initial release of voyage-code-4, a code embedding model purpose-built for coding agents, delivering state-of-the-art agentic code retrieval quality at a third below the price of voyage-code-3.
Additional details
Inputs
- Summary
Supply one or more texts to vectorise, with optional input type, truncation, dimension, and dtype controls.
- Limitations for input type
- Max 1,000 strings per request; max 32,000 total tokens.
- Input MIME type
- application/json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_embedding_input.json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_batch_input_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 |
|---|---|---|---|
input | A single string or a list of strings to embed. | Maximum 1,000 items. | Yes |
input_type | The role of the input: query, document, or null. | Default: null | No |
truncation | Whether to truncate inputs to fit context limits. | Default: true | No |
output_dimension | Number of dimensions for the output embeddings. Supported values: 2048, 1024, 512, 256. | Default: null | No |
output_dtype | Data type for the output embeddings. | Default: float. Options: float, int8, uint8, binary, ubinary | No |
encoding_format | Format in which the embeddings are encoded. | Default: null. Options: base64 | No |
id | Batch transform request ID; returned in the output if specified. | Default: null | No |
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