Codestral Embed

We are excited to release Codestral Embed, our first embedding model specialized for code. It performs especially well for retrieval use cases on real-world code data. 

Codestral Embed significantly outperforms leading code embedders in the market today: Voyage Code 3, Cohere Embed v4.0 and OpenAI’s large embedding model.

Codestral Embed can output embeddings with different dimensions and precisions, and the figure below illustrates the trade-offs between retrieval quality and storage costs. Codestral Embed with dimension 256 and int8 precision still performs better than any model from our competitors. The dimensions of our embeddings are ordered by relevance. For any integer target dimension n, you can choose to keep the first n dimensions for a smooth trade-off between quality and cost.

Results

Below  we show the performance of Codestral Embed for several categories. The details of the benchmarks corresponding to each category can be found in the table in the “Benchmarks details” section.

SWE-Bench is based on a dataset of real-world GitHub issues and corresponding fixes, and is especially relevant for retrieval-augmented generation for coding agents. Text2Code (GitHub) contains benchmarks relevant for giving context for code completion or edition. We believe that these two categories are especially relevant to code assistants. 

Use cases

Codestral Embed is optimized for high-performance code retrieval and semantic understanding. It enables a range of practical applications across development workflows, especially when working with large-scale code corpora. 

1. Retrieval-augmented generation 

Codestral Embed facilitates rapid and efficient context retrieval for code completion, editing, or explanation tasks. It is ideal for AI-powered software engineering in copilots or coding agent frameworks. 

2. Semantic code search

Embed enables accurate search of relevant code snippets from natural language or code queries. It is suitable for use within developer tools, documentation systems, and copilots. 

3. Similarity search and duplicate detection

The model’s embeddings can be used to identify near-duplicate or functionally similar code segments, even with significant lexical variation. This supports use cases such as identifying reusable code to avoid duplicates, or detecting copy-paste reuse to enforce licensing policies.

4. Semantic clustering and code analytics

Codestral Embed supports unsupervised grouping of code based on functionality or structure. This is useful for analyzing repository composition, identifying emergent architecture patterns, or feeding into automated documentation and categorization systems.

Availability 

Codestral Embed is available on our API under the name `codestral-embed-2505` at a price of $0.15 per million tokens. It is also available on our batch API at a 50% discount. For on-prem deployments, please contact us to connect with our applied AI team. 

Please check our docs to get started and our cookbook for examples of how to use Codestral Embed for code agent retrieval. 

Chunking parameters

For retrieval use cases, while you can use the full context size of 8192 tokens, it is often more efficient to chunk your dataset. We recommend using chunks of 3000 characters with 1000 characters overlap. Larger chunks tend to adversely affect the performance of the retrieval system. Refer to our cookbook for more information about chunking. 

Benchmark details

You can find the details of the benchmarks that we used to evaluate our model in the table below. We report the average score per category, and the macro average (average of the scores of each category).