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Codestral

Up to 30%

Codestral brings Mistral conversational intelligence to LLM.API so teams can ship assistants and agents without juggling multiple vendor SDKs.

Start Using API

What is Codestral?

Codestral is a Mistral chat model exposed on LLM.API under id `codestral-2508`. codestral-2508 provided by mistral. It is wired for API access through LLM.API with OpenAI-compatible patterns. Teams use it when they need reliable text generation with text inputs and text outputs over an OpenAI-compatible API.

Providers

LLM.API routes Codestral to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput
mistral$0.3 in
$0.9 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
mistral30% offin $300; out $900 per 1M tokens256K tokenstools, streaming, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test Codestral right here — free to start.

Codestral
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Codestral through the OpenAI-compatible API — POST /v1/chat/completions.

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="codestral-2508",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)
{
  "model": "codestral-2508",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Conversational UX

    Maintains coherent multi-turn assistant behavior for product chat surfaces. Tuned to how teams typically call Codestral.

  • Code assistance

    Helps write, explain, refactor, and debug application code across common languages. Tuned to how teams typically call Codestral.

  • Long-context synthesis

    Summarizes and cross-references information across large prompts when context allows. Relevant for `codestral-2508` workloads on LLM.API.

  • Analytical writing

    Produces clear analyses, comparisons, and decision memos from messy source material.

  • Instruction following

    Follows detailed system and user instructions with strong adherence to format and tone.

6 Most Valuable Use Cases

  • Meeting-note cleanup and action-item generation with Codestral
  • Product analytics narration and anomaly explanations
  • Customer support copilots that draft accurate, on-brand replies with Codestral
  • Research synthesis across long documents and tickets
  • Internal knowledge assistants grounded with your retrieval layer with Codestral
  • Sales and success email drafting with CRM context

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Codestral on LLM.API?

  • Unified AI Routing

    Reach Codestral and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Codestral.

  • Reliability Layer

    Practical: Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for Codestral alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap Codestral for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You need provider failover options exposed for this model id (Codestral)
  • Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Codestral)
  • You need a general-purpose text model for assistants, agents, or content workflows (Codestral)
  • You want OpenAI-compatible chat completions through a single LLM.API key (Codestral)

Avoid if...

  • Your use case depends on unpublished proprietary benchmarks not listed here
  • You require on-prem only deployment with no cloud inference
  • You need guaranteed real-time hard latency SLAs without benchmarking the provider
  • You need pure embedding, OCR, or media generation instead of chat

COMMUNITY

What developers say about Mistral models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Codestral.

  • Long-form paid reviews rate Mistral Large around 4/5 and call it the best European-hosted option when data residency matters.
  • For pure coding, community consensus is that it trails the top US and Chinese models, though it is considered fully usable as an assistant.
  • Developers pick it mainly for EU hosting, predictable pricing, and open-weight siblings rather than leaderboard position.

SOURCES

Frequently Asked Questions

  • What modalities does Codestral support?

    Codestral accepts text and produces text according to its architecture metadata on LLM.API.

  • Which providers serve Codestral?

    LLM.API currently lists: mistral. Availability can vary by region and account.

  • What is the context length for Codestral?

    Reported context for Codestral is 256K tokens. Always verify the active provider row if multiple providers are listed.

  • How is Codestral priced on LLM.API?

    Listed pricing metadata shows: In $0.3 / 1M tokens · Out $0.9 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • Where is the canonical page for Codestral?

    https://llmapi.ai/models/mistralai-codestral-2508/

  • Is Codestral a chat model?

    Yes—Codestral is exposed as a chat/completions-style endpoint on LLM.API.

  • What is Codestral?

    codestral-2508 provided by mistral. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `codestral-2508`.

  • How do I call Codestral via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "codestral-2508" and your LLM.API key. See the code snippet on this page.

  • What are limitations of Codestral?

    Like other API models, Codestral can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • Can I use tools or structured outputs with Codestral?

    Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

  • When should I choose Codestral?

    You need provider failover options exposed for this model id — especially when you specifically need Codestral.

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