Governed Profile

Mistral: Codestral Embed 2505

Mistral: Codestral Embed 2505 is a cost-efficient model with standard context support, suited to code retrieval and repository search for enterprise teams.

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Last reviewed: 2026-06-09

Mistral AI

Stable

Context Window

8,192

Input / 1M

$0.23

Output / 1M

$0.00

What can you do with Mistral: Codestral Embed 2505?

Practical ways teams can use Mistral: Codestral Embed 2505 inside governed AI workflows.

Search codebases with Mistral: Codestral Embed 2505

Embed repositories, snippets, and technical docs so developers can find relevant implementation context with Mistral: Codestral Embed 2505.

Power coding assistants with Mistral: Codestral Embed 2505

Retrieve related files, APIs, examples, and dependency context for governed developer workflows with Mistral: Codestral Embed 2505.

Index repositories with Mistral: Codestral Embed 2505

Create searchable vectors for source files, documentation, issues, and engineering knowledge bases with Mistral: Codestral Embed 2505.

Deduplicate code knowledge with Mistral: Codestral Embed 2505

Cluster similar snippets, docs, tickets, and examples for cleaner engineering support systems with Mistral: Codestral Embed 2505.

Rank technical evidence with Mistral: Codestral Embed 2505

Surface relevant code, logs, docs, and tickets during incident and compliance reviews with Mistral: Codestral Embed 2505.

Measure code similarity with Mistral: Codestral Embed 2505

Compare snippets, repositories, and technical records for recommendations or migration planning with Mistral: Codestral Embed 2505.

Why this model

Mistral: Codestral Embed 2505 is available in Remova as a standard context option with $0.23 per 1M tokens input pricing, $0.00 per 1M tokens output pricing, and text->embeddings modality support for enterprise AI operations.

Mistral: Codestral Embed 2505 offers standard context capacity for enterprise prompts and documents.
Current Remova pricing band is cost-efficient: $0.23 per 1M tokens input and $0.00 per 1M tokens output.
Best-fit workloads include: Code retrieval, Repository search, Coding assistant retrieval.
Keep audit logs enabled for high-impact use cases.

At a glance

Model ID: mistralai/codestral-embed-2505
Context Window: 8,192 tokens
Modality: text->embeddings
Input Modalities: text
Output Modalities: embeddings
Input Price: $0.23 per 1M tokens
Output Price: $0.00 per 1M tokens
Provider: Mistral AI
Listing Date: 2025-10-30

Strengths

Mistral: Codestral Embed 2505 is suited for code retrieval.
Supports standard context for multi-step prompts and larger working sets.
Pricing profile is cost-efficient, enabling predictable workload routing decisions.
Can be paired with policy guardrails for safer deployment at scale.

Tradeoffs

Without workload routing, teams may overuse this model for requests that fit lower-cost tiers.
Standard context limits may require chunking or retrieval strategies for large documents.
Low-cost tiers can still underperform on high-consequence decisions without escalation paths.
Code retrieval systems need repository access controls, freshness checks, and relevance benchmarks.

Best for

Mistral: Codestral Embed 2505 for codebase retrieval across repositories, docs, issues, and technical records.
Mistral: Codestral Embed 2505 for repository search with access controls and relevance benchmarks.
Mistral: Codestral Embed 2505 for grounding coding assistants in approved repository context.
Mistral: Codestral Embed 2505 for surfacing relevant code, logs, and tickets during engineering reviews.

Rollout checklist

Define where Mistral: Codestral Embed 2505 is default vs. fallback in your routing policy.
Enable role-based access and policy checks before opening access broadly.
Set spend guardrails by team and monitor weekly token consumption.
Start with approved teams, then expand in controlled waves.
Re-run quality and cost benchmarks monthly as newer releases appear.

Related models

Explore adjacent model profiles for routing and benchmarking decisions.

Free Resource

Where Should Your Team Start with AI?

Tell us your industry and team size. We'll tell you which AI use cases will save the most time with the least setup.

You get

A shortlist of AI use cases ranked by impact and effort for your situation.

Tuning notes

frequency_penalty

Tune repetition control for long responses in multi-step workflows.

max_tokens

Set completion limits to avoid unpredictable long-output spend.

presence_penalty

Use carefully when expanding idea diversity in exploration-heavy prompts.

response_format

Prefer structured output where responses feed internal systems.

Free Assessment

What Could Go Wrong?

5 questions about how your company uses AI today. We'll show you the risks most companies miss until it's too late.

You get

A risk breakdown with the 3 things you should fix first.

Knowledge Hub

Mistral: Codestral Embed 2505 FAQs

Choose Mistral: Codestral Embed 2505 when the workload aligns with code retrieval, repository search, coding assistant retrieval and quality targets justify its pricing profile.

It depends on workload mix. Most organizations use routing policies so routine traffic stays on lower-cost tiers.

Validate quality on real internal prompts, token efficiency, latency, and policy compliance behavior.

Deploy This Model With Governance

Use policy controls, role-based access, and budget guardrails before enabling advanced model tiers at scale.

Try Mistral: Codestral Embed 2505 with your team