{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-codestral-2508","slug":"mistralai-codestral-2508","name":"Mistral: Codestral 2508","type":"model","url":"https://openrouter.ai/models/mistralai~codestral-2508","page_url":"https://unfragile.ai/mistralai-codestral-2508","categories":["code-review-security","testing-quality"],"tags":["mistralai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-codestral-2508__cap_0","uri":"capability://code.generation.editing.fill.in.the.middle.fim.code.completion","name":"fill-in-the-middle (fim) code completion","description":"Generates code to fill gaps between existing code context using bidirectional attention patterns optimized for low-latency inference. The model processes prefix and suffix tokens simultaneously to predict the most contextually appropriate code segment, enabling inline code completion without full-file regeneration. Specialized training on code infilling tasks reduces latency compared to standard left-to-right generation approaches.","intents":["Complete partial code snippets in the middle of a function or class definition","Generate missing method bodies when prefix and suffix context are available","Implement IDE-integrated inline suggestions without waiting for full-file analysis"],"best_for":["IDE plugin developers building real-time code completion features","Teams using Codestral via OpenRouter API for low-latency editor integrations","Developers prioritizing sub-100ms completion latency for interactive workflows"],"limitations":["FIM performance degrades with very long context windows (>8K tokens) due to attention complexity","Bidirectional attention requires both prefix and suffix context; performs poorly with only prefix or suffix alone","No built-in caching of completion results across similar code patterns"],"requires":["OpenRouter API key or direct Mistral API access","Support for FIM-specific prompt formatting (special tokens for prefix/suffix boundaries)","HTTP client capable of streaming token responses for real-time display"],"input_types":["code (prefix context)","code (suffix context)","language identifier (Python, JavaScript, Java, etc.)"],"output_types":["code (generated middle segment)","token probabilities (optional)"],"categories":["code-generation-editing","real-time-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-codestral-2508__cap_1","uri":"capability://code.generation.editing.code.correction.and.bug.fixing","name":"code correction and bug fixing","description":"Analyzes code with syntax errors, logic bugs, or style issues and generates corrected versions with explanations of the problems identified. The model uses error detection patterns learned from large-scale code repair datasets to identify common bug categories (null pointer dereferences, off-by-one errors, type mismatches) and apply targeted fixes. Operates on full code blocks or individual functions with optional context about error messages or test failures.","intents":["Automatically fix compilation errors and syntax issues in code snippets","Identify and correct logic bugs based on error messages or failing test output","Refactor code to follow language-specific best practices and style guidelines"],"best_for":["Developers using IDE extensions for real-time error correction suggestions","CI/CD pipeline integrations that automatically suggest fixes for failing builds","Teams building code review automation tools that flag and fix common issues"],"limitations":["Correction accuracy depends on clarity of error context; vague or missing error messages reduce fix quality","Cannot fix bugs requiring domain-specific knowledge or business logic understanding beyond code patterns","May introduce new bugs when fixing complex interdependencies across multiple functions"],"requires":["OpenRouter API key or Mistral API credentials","Code snippet or file content (up to context window limit)","Optional: error message, stack trace, or test failure output for better context"],"input_types":["code (buggy or malformed)","error message (optional)","test failure output (optional)","language identifier"],"output_types":["code (corrected)","explanation (bug description and fix rationale)","diff (optional)"],"categories":["code-generation-editing","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-codestral-2508__cap_2","uri":"capability://code.generation.editing.automated.test.generation","name":"automated test generation","description":"Generates unit tests, integration tests, and edge-case test suites from source code by analyzing function signatures, docstrings, and implementation logic. The model infers expected behavior from code structure and generates test cases covering normal paths, boundary conditions, and error scenarios. Supports multiple testing frameworks (pytest, Jest, JUnit, etc.) and produces tests with assertions, mocks, and fixtures appropriate to the language and framework.","intents":["Generate comprehensive unit test suites for existing functions without manual test writing","Create edge-case and boundary-condition tests to improve code coverage","Produce integration tests that verify interactions between multiple code modules"],"best_for":["Teams with low test coverage seeking to improve quality without manual test authoring","Developers building test-generation plugins for IDEs or CI/CD systems","Projects migrating legacy code to modern testing frameworks"],"limitations":["Generated tests may miss domain-specific edge cases or business logic constraints not evident from code alone","Test quality depends on code clarity; poorly documented or complex functions produce weaker test suites","Cannot generate tests for external API calls or system-level behavior without explicit mocking instructions","May produce redundant or overlapping test cases without deduplication"],"requires":["OpenRouter API key or Mistral API access","Source code file or function definition","Testing framework identifier (pytest, Jest, JUnit, etc.)","Optional: existing test examples for style/pattern matching"],"input_types":["code (function or class definition)","docstring or comments (optional)","testing framework name","language identifier"],"output_types":["test code (unit tests with assertions)","test fixtures (setup/teardown code)","mock definitions (optional)"],"categories":["code-generation-editing","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-codestral-2508__cap_3","uri":"capability://code.generation.editing.multi.language.code.generation.with.syntax.awareness","name":"multi-language code generation with syntax awareness","description":"Generates syntactically correct code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) using language-specific token patterns and grammar constraints learned during training. The model maintains language-specific idioms, naming conventions, and structural patterns rather than producing generic pseudocode. Supports both standalone code snippets and context-aware generation that respects existing codebase style and architecture.","intents":["Generate code in a specific language based on natural language requirements or pseudocode","Translate code logic between different programming languages while preserving intent","Complete code scaffolding (class definitions, function signatures, imports) in language-native style"],"best_for":["Polyglot development teams building services across multiple languages","Developers learning new languages who need syntax-correct examples","Code generation tools that must support diverse language ecosystems"],"limitations":["Code generation quality varies by language; well-represented languages (Python, JavaScript) produce better results than niche languages","Cannot guarantee type safety or compile-time correctness; generated code may have runtime errors","Language-specific idioms and best practices may not match team conventions without explicit style examples","No built-in dependency resolution; generated code may reference non-existent or incompatible libraries"],"requires":["OpenRouter API key or Mistral API credentials","Language identifier (Python, JavaScript, Java, etc.)","Optional: existing codebase context or style guide for consistency"],"input_types":["natural language description","pseudocode or partial code","language identifier","existing code context (optional)"],"output_types":["code (syntactically valid in target language)","imports/dependencies (optional)"],"categories":["code-generation-editing","polyglot-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-codestral-2508__cap_4","uri":"capability://code.generation.editing.low.latency.api.inference.with.streaming.responses","name":"low-latency api inference with streaming responses","description":"Delivers code generation results through OpenRouter's optimized inference pipeline with sub-100ms time-to-first-token and streaming token output for real-time display. Uses batched request processing, KV-cache optimization, and hardware acceleration (GPUs/TPUs) to minimize latency for high-frequency code completion and correction tasks. Supports both synchronous and asynchronous API calls with configurable timeout and retry logic.","intents":["Integrate Codestral into IDE plugins that require <200ms response time for interactive feel","Build real-time code suggestion features that stream results as they're generated","Handle high-volume code completion requests in production without queueing delays"],"best_for":["IDE and editor plugin developers requiring sub-200ms latency for user experience","Teams building production code generation services with SLA requirements","Developers prioritizing real-time streaming over batch processing"],"limitations":["Latency increases with context window size; very large files (>8K tokens) may exceed 200ms","Streaming responses require client-side buffering and token assembly; not suitable for simple request-response patterns","Rate limiting applies per API key; high-frequency requests may be throttled during peak usage","No local caching of model weights; all inference requires API calls (no offline mode)"],"requires":["OpenRouter API key with active billing","HTTP client supporting streaming responses (Server-Sent Events or chunked transfer encoding)","Network connectivity to OpenRouter infrastructure","Optional: retry logic and exponential backoff for reliability"],"input_types":["code (text)","natural language prompt","model parameters (temperature, max_tokens, etc.)"],"output_types":["token stream (real-time)","complete response (after streaming completes)","usage metadata (tokens consumed, latency)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-codestral-2508__cap_5","uri":"capability://code.generation.editing.context.aware.code.completion.with.codebase.understanding","name":"context-aware code completion with codebase understanding","description":"Generates code completions that respect existing codebase patterns, naming conventions, and architectural styles by incorporating file context and optional repository-level semantic information. The model analyzes surrounding code to infer project conventions (naming style, indentation, import patterns) and generates completions that blend seamlessly with existing code. Can optionally accept repository metadata or file structure hints to improve contextual relevance.","intents":["Complete code in a way that matches the existing codebase style and conventions","Generate function implementations that follow project-specific patterns and abstractions","Suggest imports and dependencies consistent with the project's architecture"],"best_for":["Teams with established code style guides who want AI completions to match conventions","Large codebases where consistency across files is critical","IDE integrations that can provide surrounding file context to the model"],"limitations":["Context window limits restrict how much codebase context can be provided; very large files may require truncation","Cannot access external repositories or package registries; suggestions may reference unavailable dependencies","Style inference from context is probabilistic; may occasionally generate code that violates subtle project conventions","No persistent memory of codebase patterns across separate API calls; each request requires fresh context"],"requires":["OpenRouter API key or Mistral API credentials","Surrounding code context (prefix and suffix from current file)","Optional: repository structure hints or style guide examples"],"input_types":["code (prefix context from current file)","code (suffix context from current file)","code (optional: examples from other files in project)","language identifier"],"output_types":["code (completion matching codebase style)","confidence score (optional)"],"categories":["code-generation-editing","context-awareness"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-codestral-2508__cap_6","uri":"capability://code.generation.editing.code.review.and.quality.analysis","name":"code review and quality analysis","description":"Analyzes code for potential issues including style violations, performance problems, security vulnerabilities, and maintainability concerns. The model applies learned patterns from code review datasets to identify anti-patterns, suggest improvements, and flag high-risk code sections. Provides actionable feedback with explanations of why changes are recommended and how to implement them, supporting both automated review workflows and interactive developer feedback.","intents":["Automatically review pull requests for common issues before human review","Identify security vulnerabilities and suggest hardening measures","Flag performance bottlenecks and suggest optimization strategies"],"best_for":["Teams implementing automated code review gates in CI/CD pipelines","Open-source projects seeking to maintain code quality with limited reviewer bandwidth","Organizations building internal code quality standards and enforcement tools"],"limitations":["Review quality depends on code clarity; complex or poorly documented code produces less useful feedback","Cannot identify issues requiring domain knowledge or business logic understanding","May flag false positives for legitimate patterns or intentional design choices","Security analysis is pattern-based; may miss novel or sophisticated vulnerabilities","No integration with external security scanners or SAST tools"],"requires":["OpenRouter API key or Mistral API credentials","Code file or snippet to review","Optional: project context, coding standards, or security policies"],"input_types":["code (full file or snippet)","language identifier","optional: coding standards or guidelines","optional: security policy or compliance requirements"],"output_types":["review feedback (structured list of issues)","severity levels (critical, high, medium, low)","suggested fixes (optional)","explanations (rationale for each issue)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-codestral-2508__cap_7","uri":"capability://code.generation.editing.documentation.generation.from.code","name":"documentation generation from code","description":"Generates comprehensive documentation including docstrings, README sections, API documentation, and code comments from source code analysis. The model infers function purpose, parameters, return values, and usage examples from code structure and context, producing documentation in multiple formats (Markdown, reStructuredText, Javadoc, etc.). Supports both inline documentation (docstrings) and standalone documentation files with cross-references and examples.","intents":["Auto-generate docstrings for functions and classes that lack documentation","Create API documentation from code signatures and implementations","Produce README sections describing module functionality and usage"],"best_for":["Teams with underdocumented codebases seeking to improve documentation coverage","Open-source projects generating API docs from source code","Developers building documentation generation tools for IDEs or build systems"],"limitations":["Generated documentation quality depends on code clarity; complex logic may produce inaccurate descriptions","Cannot infer business logic or domain-specific context from code alone","May miss edge cases or non-obvious behavior not evident from code structure","Documentation format must be specified; no automatic format detection"],"requires":["OpenRouter API key or Mistral API credentials","Source code file or function definition","Documentation format identifier (Markdown, reStructuredText, Javadoc, etc.)","Optional: existing documentation examples for style matching"],"input_types":["code (function, class, or module)","language identifier","documentation format (Markdown, reStructuredText, etc.)","optional: existing documentation examples"],"output_types":["docstring (inline documentation)","markdown (standalone documentation)","api documentation (structured format)"],"categories":["code-generation-editing","documentation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key or direct Mistral API access","Support for FIM-specific prompt formatting (special tokens for prefix/suffix boundaries)","HTTP client capable of streaming token responses for real-time display","OpenRouter API key or Mistral API credentials","Code snippet or file content (up to context window limit)","Optional: error message, stack trace, or test failure output for better context","OpenRouter API key or Mistral API access","Source code file or function definition","Testing framework identifier (pytest, Jest, JUnit, etc.)","Optional: existing test examples for style/pattern matching"],"failure_modes":["FIM performance degrades with very long context windows (>8K tokens) due to attention complexity","Bidirectional attention requires both prefix and suffix context; performs poorly with only prefix or suffix alone","No built-in caching of completion results across similar code patterns","Correction accuracy depends on clarity of error context; vague or missing error messages reduce fix quality","Cannot fix bugs requiring domain-specific knowledge or business logic understanding beyond code patterns","May introduce new bugs when fixing complex interdependencies across multiple functions","Generated tests may miss domain-specific edge cases or business logic constraints not evident from code alone","Test quality depends on code clarity; poorly documented or complex functions produce weaker test suites","Cannot generate tests for external API calls or system-level behavior without explicit mocking instructions","May produce redundant or overlapping test cases without deduplication","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.34,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mistralai-codestral-2508","compare_url":"https://unfragile.ai/compare?artifact=mistralai-codestral-2508"}},"signature":"set6wnHarehZe+YSHxAlH5KW1CmVX8h9DNQefjz7aplStBuAbc4sz0u1JQbMRsksHPlRaJwP4hYM4HlvOYLqDw==","signedAt":"2026-06-20T12:14:43.652Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-codestral-2508","artifact":"https://unfragile.ai/mistralai-codestral-2508","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-codestral-2508","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}