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Codestral Embed 2505
Codestral Embed 2505 is an embedding model from Mistral AI designed for creating vector representations of text, with a focus on code-related content. It offers an 8K-token context window at a competitive input cost for large-scale retrieval and search applications.
About the model
What is Codestral Embed 2505?
Codestral Embed 2505 is a Mistral AI embedding model that converts text, especially source code, into dense vector representations for similarity search and retrieval. It is mainly used for semantic code search, powering code-focused RAG pipelines over large repositories, and building coding assistants that rely on high-quality retrieval. It is also suitable for other embedding-driven tasks like indexing technical documentation or integrating with vector databases where efficient storage and search over embeddings is required. The model is part of Mistral’s Codestral line of code-oriented models and represents their first specialized code embedding offering in the 25-05 (May 2025) release generation.
Model capabilities
5 Core Capabilities
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Code Embedding
Generates dense vector embeddings tailored for source code, capturing syntax and semantics for downstream machine learning and retrieval.
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Semantic Code Search
Enables semantic search over large codebases by embedding snippets, functions, and files for similarity-based retrieval and navigation.
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Repository Analytics
Supports clustering and organization of code repositories using embeddings to reveal functional groupings, patterns, and architectural structure.
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Duplicate Detection
Identifies near-duplicate or similar code blocks by comparing embedding vectors, assisting refactoring, deduplication, and code quality improvements.
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RAG for Code
Powers retrieval-augmented generation pipelines for coding assistants by providing high-quality embeddings as the retrieval backbone.
Use cases
6 Most Valuable Use Cases
- Semantic Code Search
- Codebase Retrieval Augmented
- Developer Helpdesk Indexing
- Repository Impact Analysis
- Technical Docs Embedding
- Code Snippet Deduplication
Transparent pricing
Cost Comparison
LLM API offers the lowest embedding costs and best performance for Codestral-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120K tps | 99.99% | $0.03 | $0.03 | ~1M tokens |
| Mistral | EU West | ~140ms | ~60K tps | 99.9% | ~$0.06 | ~$0.06 | ~1M tokens |
| OpenAI | Global | ~160ms | ~80K tps | 99.9% | ~$0.10 | ~$0.10 | ~1M tokens |
| Azure AI | US East | ~180ms | ~50K tps | 99.9% | ~$0.11 | ~$0.11 | ~1M tokens |
| Google Cloud | US Central | ~170ms | ~70K tps | 99.9% | ~$0.09 | ~$0.09 | ~1M tokens |
Performance benchmarks
Technical Specifications
| Metric | Codestral Embed 2505 | text-embedding-3-large (OpenAI) | nomic-embed-text (Nomic) |
|---|---|---|---|
| Dimensions | 1024~estimate | 3072 | 768 |
| Max Input Tokens | 8K~estimate | 8K~estimate | 8K~estimate |
| Price per 1M Tokens | $0.05~estimate | $0.13 | $0.10~estimate |
| Throughput | 2,000 tps~estimate | 1,500 tps~estimate | 1,200 tps~estimate |
| Avg Latency | ~120ms~estimate | ~150ms~estimate | ~180ms~estimate |
| Uptime | 99.9%~estimate | 99.9%~estimate | 99.5%~estimate |
30-day usage via LLM API
- 420M
- Prompt tokens processed (30 days)
- 3.1M
- API requests served (30 days)
- 310M
- Embedding vectors generated (30 days)
- 99.9%
- Average API uptime (30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality. One API, pluggable policies, zero vendor lock-in.
One endpoint, every model -
Cost-Aware Orchestration
Dynamically pick the cheapest viable model for each call, with guardrails on spend. Optimize token usage without rewriting app logic or juggling pricing tables.
Cut spend, keep quality -
Automatic Model Fallbacks
Configure fallback chains so failures, rate limits, or regional outages seamlessly fail over to alternatives. Keep production apps resilient without custom retry logic.
Stay online by default -
Full-Stack Observability
Get end-to-end traces, latency breakdowns, token usage, and errors across all models and providers. Debug faster and tune prompts with real production telemetry.
See every token flow -
Task-Level Abstractions
Define tasks like chat, generation, extraction, or tools once and map them to any model. Swap providers without touching your business logic or payload shapes.
Think tasks, not models -
High-Throughput Batch Jobs
Run massive batch inferences with smart chunking, concurrency control, and retries baked in. Ship evaluations, backfills, and data labeling pipelines with one call.
Batch at production scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need fast, domain-aware code search across large repositories using compact embedding vectors.
- You need semantic code clone detection to identify similar implementations across multiple languages.
- Your use case involves code recommendation systems powered by nearest-neighbor searches on embeddings.
- Your use case involves de-duplicating or clustering large codebases by functional similarity.
- You need language-specific embeddings optimized for understanding code structure, APIs, and identifiers.
- Your use case involves augmenting code review tools with semantic similarity and pattern detection.
- You need embeddings to power retrieval-augmented generation for a separate code LLM.
Avoid if...
- You need a general-purpose text embedding model tuned primarily for natural language tasks.
- Your workload requires generating or editing code directly rather than encoding it.
- You need multimodal embeddings that jointly represent code, images, and other non-textual modalities.
- Your workload requires instruction-following, chat interactions, or reasoning beyond similarity search.
- You need embeddings highly optimized for non-code tasks like recommendation, ads, or user profiles.
- Your workload requires ultra-long context embeddings beyond typical file or snippet sizes.
- You need an open-weight model that can be deployed completely offline without provider dependence.
FAQ
Frequently Asked Questions
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What is Codestral Embed 2505?
Codestral Embed 2505 is a Mistral embedding model optimized for generating vector representations of code and related textual content.
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What is Codestral Embed 2505 best suited for?
It is best suited for code search, semantic retrieval, similarity, and indexing large codebases via high-quality embeddings.
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What context window does Codestral Embed 2505 support?
Codestral Embed 2505 supports long input sequences suitable for embedding substantial code files or documents in a single request.
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What modalities does Codestral Embed 2505 support?
Codestral Embed 2505 is a text-only embedding model and does not support images, audio, or video.
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How is pricing for Codestral Embed 2505 handled on LLM.API?
On LLM.API, Codestral Embed 2505 is billed per input token, with exact rates shown in the project’s pricing and usage dashboard.
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How fast is Codestral Embed 2505 when called through LLM.API?
Latency is typically low and dominated by network and provider response time, making it suitable for real-time or interactive tools.
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How do I call Codestral Embed 2505 via LLM.API?
You select the Codestral Embed 2505 model in your LLM.API request and send text input to receive embedding vectors in the response payload.
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How does Codestral Embed 2505 compare to general-purpose text embedding models?
It is specialized for code understanding and may outperform general-purpose text embeddings on developer and repository search tasks.
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Does Codestral Embed 2505 support multilingual code comments and documentation?
It can embed code and associated natural-language text from multiple languages, but performance may vary across less-represented languages.
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What are the main limitations of Codestral Embed 2505?
It cannot generate natural-language outputs, execute code, or handle non-text modalities, and is limited to producing fixed-length numeric vectors.