How much does Codestral cost via Railwail?

Input: €3.00 per 1M tokens. Output: €9.00 per 1M tokens. No monthly minimum, no subscription. Start with €5 free credits.

What is the context window of Codestral?

Codestral supports a 256K tokens context window — enough for entire codebases or research papers in one prompt.

How fast is Codestral?

Average response latency: 1.5s (p50 across recent Railwail traffic). See live p50/p95 metrics on /rankings.

Is Codestral better than DeepSeek Coder V2?

It depends on your use case. Codestral (Mistral AI) and DeepSeek Coder V2 (DeepSeek) are both strong choices in code. Compare them side-by-side at /compare/codestral-vs-deepseek-coder-v2.

Codestral

Name: Codestral
Brand: Mistral AI
SKU: codestral
Price: 0.003 EUR
Availability: InStock

New

Popular

Mistral AI

Code

Mistral's code-specialized model. Optimized for code generation, completion, and understanding across 80+ languages.

Try Codestral now

Send a single prompt and stream a response inline. Hit Cmd+Enter to submit.

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TL;DR·Last updated March 4, 2026

Codestral is code AI model from Mistral AI, priced at €3.00 per 1M input tokens with a 256K tokens context window.

Try Codestral

Prompt

Language

Examples

See what Codestral can generate

TypeScript API Handler

Write a type-safe Express middleware for JWT authentication in TypeScript

import { Request, Response, NextFunction } from 'express';
import jwt from 'jsonwebtoken';

interface JwtPayload {
  userId: string;
  email: string;
  role: 'admin' | 'user';
}

declare global {
  namespace Express {
    interface Request {
      user?: JwtPayload;
    }
  }
}

export const authenticate = (
  req: Request,
  res: Response,
  next: NextFunction
): void => {
  const token = req.headers.authorization?.split(' ')[1];

  if (!token) {
    res.status(401).json({ error: 'No token provided' });
    return;
  }

  try {
    const decoded = jwt.verify(
      token,
      process.env.JWT_SECRET!
    ) as JwtPayload;
    req.user = decoded;
    next();
  } catch (err) {
    res.status(403).json({ error: 'Invalid or expired token' });
  }
};

Rust Error Handling

Write a Rust function that reads a JSON config file and returns a parsed struct with proper error handling

use serde::Deserialize;
use std::fs;
use std::path::Path;
use thiserror::Error;

#[derive(Debug, Deserialize)]
pub struct AppConfig {
    pub host: String,
    pub port: u16,
    pub database_url: String,
    pub max_connections: Option<u32>,
}

#[derive(Error, Debug)]
pub enum ConfigError {
    #[error("Failed to read config file: {0}")]
    IoError(#[from] std::io::Error),
    #[error("Failed to parse config JSON: {0}")]
    ParseError(#[from] serde_json::Error),
    #[error("Config file not found: {0}")]
    NotFound(String),
}

pub fn load_config(path: &str) -> Result<AppConfig, ConfigError> {
    if !Path::new(path).exists() {
        return Err(ConfigError::NotFound(path.to_string()));
    }
    let contents = fs::read_to_string(path)?;
    let config: AppConfig = serde_json::from_str(&contents)?;
    Ok(config)
}

// Usage:
// let config = load_config("config.json")?;

Pricing

Price per Generation

Per generationFree

API Integration

Use our OpenAI-compatible API to integrate Codestral into your application.

Install

npm install railwail

JavaScript / TypeScript

import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

// Simple — just pass a string
const reply = await rw.run("codestral", "Hello! What can you do?");
console.log(reply);

// With message history
const reply2 = await rw.run("codestral", [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);

// Full response with usage info
const res = await rw.chat("codestral", [
  { role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);

Specifications

Context window

256,000 tokens

Max output

8,192 tokens

Avg. latency

1.5s

Developer

Mistral AI

Deep dive — Mistral AI's Codestral

About Mistral AI

Founded 2023 · Paris, France

Mistral AI was founded in April 2023 in Paris by Arthur Mensch (CEO, former DeepMind), Guillaume Lample and Timothée Lacroix (both former Meta FAIR co-authors of the LLaMA papers). Mistral has built a frontier-scale European LLM lab with a portfolio mixing fully open-weight releases (Mistral 7B, Mixtral 8x7B, Mixtral 8x22B, Mistral Small) and commercial closed-API models (Mistral Large, Mistral Embed, Mistral Saba). The company has raised over €1B from investors including Andreessen Horowitz, General Catalyst, Lightspeed, Salesforce, Nvidia and Microsoft, with a 2024 valuation around €6B. Codestral was released May 2024 as Mistral's first dedicated code model — a 22B dense transformer trained on 80+ programming languages with native fill-in-the-middle support for IDE integrations. A successor, Codestral 25.01, followed in January 2025.

Visit Mistral AI →

Architecture

Decoder-only Transformer for code (Mistral architecture)

Codestral-22B is a 22B dense decoder-only transformer using Mistral's standard architecture: 56 layers, 6,144 hidden size, 48-head grouped-query attention with 8 KV heads, sliding-window attention (4,096-token window), RoPE positional embeddings with theta=1M (for long-context support), SwiGLU activations and RMSNorm. The tokeniser is the Mistral BPE with 32,768 entries plus added fill-in-the-middle special tokens (`[PREFIX]`, `[SUFFIX]`, `[MIDDLE]`). Training combined a standard left-to-right autoregressive objective with FIM training for IDE-grade code completion. The training corpus covers 80+ programming languages drawn from public code repositories, plus natural-language code documentation pairs and instruction data for the chat / explain capability. Released May 2024 under the Mistral AI Non-Production License (MNPL), which permits open-weights research and evaluation but not commercial production use without a separate license or hosted API access.

Parameters: 22B (dense)
Context: 32K tokens

What it can do

22B dense transformer purpose-built for code
Native fill-in-the-middle (FIM) for IDE autocomplete
Trained on 80+ programming languages
32K context window
Competitive with CodeLlama-34B and DeepSeek-Coder-33B at smaller size
Strong on mainstream: Python, JS/TS, Java, C/C++, Go, Rust, Bash, SQL, PHP, Swift
Open weights available under MNPL (research only)
Best for: IDE code completion, code generation, explanation and refactoring across mainstream languages.

Training & License

Trained on trillions of tokens (exact figure not disclosed) of public code repositories across 80+ programming languages, natural-language code-documentation pairs, and instruction-style code data for chat and explain capabilities. Knowledge cutoff approximately early 2024. Training combined autoregressive and fill-in-the-middle objectives.

License: Mistral AI Non-Production License (MNPL). Open weights for research, evaluation and personal use; commercial production requires Mistral commercial license or hosted API access (la Plateforme, AWS Bedrock, Azure AI Studio).

Known limitations

MNPL blocks commercial production use of open weights
Smaller than successor Codestral 25.01 and below DeepSeek-Coder V2 on top benchmarks
32K context shorter than newer code models (DeepSeek-Coder V2: 128K)
Weaker on rare languages and DSLs
Not as strong on agentic / repo-level reasoning as longer-context code models
No multimodal input (no image-of-code understanding)