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Large Language ModelText GenerationCodestral Family

Mistral Codestral

Codestral is Mistral AI’s open-source, 22B-parameter code-specialized language model optimized for software development workflows such as code generation, completion, and debugging across many programming languages. It is tuned for long-context coding scenarios and integrates well with IDEs and developer tools.

by Mistral AIReleased 2024-05-29Apache 2.0
Context Window
33K
Parameters
22B
API Access
Available

Key Capabilities

  • +High-quality code generation and completion across many popular languages
  • +Strong performance on repository-level and long-context coding tasks
  • +Support for fill-in-the-middle and structured code editing workflows
  • +Optimized tokenizer and training data for software engineering use cases
  • +Open-source weights suitable for on-premise and self-hosted deployment

Limitations

  • -Specialized for code; weaker than general-purpose LLMs on broad natural language tasks
  • -May emit insecure, non-idiomatic, or non-compiling code without human review
  • -Knowledge of libraries, frameworks, and language features is limited to its training cutoff
  • -No native access to private codebases, tools, or runtime environments without external integration
  • -Open benchmarks and evaluations may lag behind the latest proprietary code models

Benchmark Performance

coding

coding

HumanEval

81.1%
coding

Mostly Basic Python Problems

78.2%

conversation

conversation

Chatbot Arena Elo

1134.0Elo

Alternatives & Comparisons

GPT-4.1 (Code Interpreter)proprietary code LLM

Frontier-level proprietary model with strong coding, reasoning, and tool-use capabilities but closed weights and usage-based pricing.

Strengths
  • + Very strong coding and multi-step reasoning performance
  • + Deep ecosystem integrations (GitHub, VS Code, etc.)
Weaknesses
  • - Proprietary and closed weights
  • - Usage-based API pricing
Claude 3.5 Sonnet (Artifacts)proprietary code LLM

Strong coding and reasoning with interactive artifact-based workflows, but closed-source and API-gated.

Strengths
  • + High-quality code generation and refactoring
  • + Interactive UI for working with code artifacts
Weaknesses
  • - Closed weights and proprietary stack
  • - Dependent on Anthropic’s API availability and pricing
Llama 3.1 Code 70Bopen-source code LLM

Larger open-source code model with strong performance but higher resource requirements than Codestral.

Strengths
  • + High coding quality across many languages
  • + Open weights suitable for self-hosting
Weaknesses
  • - Higher inference cost and latency due to 70B parameters
  • - May require more aggressive optimization for real-time IDE use

Sources

mistral.aigithub.comhuggingface.co