Mistral Releases Codestral: A Game-Changer in Generative AI for Code

Mistral Codestral. Generative AI, AI for coding, code generation, AI development tools, software development, coding assistance, AI model, programming languages, Microsoft-backed startup

Mistral, the French AI startup renowned for its innovative solutions and backed by tech giant Microsoft, has unveiled its first generative AI model for coding, named Codestral. Valued at a staggering $6 billion, Mistral aims to revolutionize the way developers write and interact with code. This article delves into the features, potential, and implications of Codestral in the realm of software development.

Discover Codestral, Mistral’s groundbreaking generative AI model for coding. Trained on over 80 programming languages, Codestral offers code completion, test writing, and codebase querying in English. Learn about its features, performance, and the debate surrounding its licensing and practical use in the software development industry.

Mistral Releases Codestral
Mistral Releases Codestral

What is Codestral?

Codestral is a generative AI model specifically designed to assist developers in writing and managing code. Trained on an extensive dataset encompassing over 80 programming languages, including popular ones like Python, Java, C++, and JavaScript, Codestral promises to enhance coding efficiency and accuracy. According to Mistral’s blog post, the model can complete coding functions, write tests, fill in partial code snippets, and even answer questions about a codebase in English.

Features of Codestral

  1. Multi-Language Support: Codestral supports over 80 programming languages, making it a versatile tool for developers working across different coding environments.
  2. Code Completion and Assistance: The AI can auto-complete code, provide suggestions, and fill in missing parts, significantly speeding up the coding process.
  3. Test Writing: It can automatically generate tests for the written code, ensuring higher reliability and reducing the manual effort required in test creation.
  4. Codebase Querying: Developers can query Codestral in natural language to get insights and explanations about their codebase, facilitating better understanding and maintenance.

The Licensing Controversy

While Mistral describes Codestral as “open,” its licensing terms have sparked debate. The model’s license prohibits the use of Codestral and its outputs for commercial activities. There’s an allowance for “development,” but even this comes with strict caveats, explicitly banning any internal usage by employees within a business context. The underlying reason might be related to the training data, which reportedly includes copyrighted content. Although Mistral hasn’t confirmed this, it aligns with practices seen in their previous datasets.

Performance and Practicality

Codestral boasts 22 billion parameters, a metric indicating the complexity and capability of the AI model. This large parameter count suggests high performance but also means that it requires significant computational resources to run effectively. While Codestral surpasses some competitors in benchmarks, these benchmarks can be unreliable, and the performance improvement might be incremental rather than groundbreaking.

The Debate on Generative AI in Coding

Generative AI models like Codestral have been a topic of intense discussion in the developer community. A Stack Overflow poll from June 2023 revealed that 44% of developers currently use AI tools in their development process, with another 26% planning to adopt them soon. Despite their growing popularity, these tools are not without flaws. An analysis of over 150 million lines of code by GitClear highlighted that AI-generated code often contains more mistakes compared to human-written code. Moreover, security researchers have raised concerns that AI tools can exacerbate existing bugs and security vulnerabilities in software projects. A study from Purdue University found that more than half of the answers provided by OpenAI’s ChatGPT to programming questions were incorrect.

Mistral’s Strategic Moves

Despite these challenges, Mistral continues to push forward. They have launched a hosted version of Codestral on their Le Chat conversational AI platform and made it available through a paid API. Additionally, Mistral has integrated Codestral into various app frameworks and development environments, including LlamaIndex, LangChain, Continue.dev, and Tabnine. These integrations aim to make Codestral more accessible and useful to developers, embedding it into the tools they already use.

The Future of Codestral and Generative AI in Coding

Codestral represents a significant step in the evolution of generative AI for coding. Its ability to support multiple languages, assist in code completion, write tests, and answer codebase queries makes it a valuable tool for developers. However, its high resource requirements and licensing restrictions might limit its widespread adoption.

The controversy over the use of copyrighted content in training AI models like Codestral underscores the need for clearer guidelines and ethical considerations in AI development. As generative AI tools become more integrated into the software development lifecycle, addressing these issues will be crucial to ensure they are used responsibly and effectively.


Mistral’s release of Codestral marks a milestone in the application of generative AI to coding. While it offers powerful features that can streamline development tasks, it also raises important questions about licensing, data usage, and the reliability of AI-generated code. As the debate over the role of AI in software development continues, tools like Codestral will likely play a central role in shaping the future of coding. Whether it becomes a staple in the developer’s toolkit or a topic of contention will depend on how well these challenges are addressed and how developers adapt to this new technology.

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