{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-devstral-medium","slug":"mistralai-devstral-medium","name":"Mistral: Devstral Medium","type":"model","url":"https://openrouter.ai/models/mistralai~devstral-medium","page_url":"https://unfragile.ai/mistralai-devstral-medium","categories":["model-training"],"tags":["mistralai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$4.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-mistralai-devstral-medium__cap_0","uri":"capability://code.generation.editing.multi.language.code.generation.with.context.aware.completion","name":"multi-language code generation with context-aware completion","description":"Generates syntactically correct, semantically meaningful code across 40+ programming languages by leveraging transformer-based token prediction trained on high-quality code corpora. The model uses attention mechanisms to understand surrounding code context, function signatures, and import statements to produce contextually appropriate completions that respect language-specific idioms and patterns.","intents":["I need to auto-complete a function body given its signature and docstring","Generate boilerplate code for common patterns in my language","Fill in missing implementations across multiple files with consistent style"],"best_for":["solo developers building production code in polyglot environments","teams migrating from Copilot seeking better code quality for specialized domains","developers working with less-common languages where general-purpose models underperform"],"limitations":["Context window limited to ~4K tokens, constraining multi-file reasoning for large codebases","No real-time linting or syntax validation — generated code may contain subtle type errors in statically-typed languages","Training data cutoff means unfamiliarity with very recent language features or framework APIs released after training"],"requires":["API key for Mistral AI or OpenRouter access","HTTP client capable of streaming responses","Code editor or IDE with API integration (VS Code extension, Vim plugin, or custom wrapper)"],"input_types":["code snippet with cursor position","function signature with docstring","partial code with comments indicating intent"],"output_types":["code completion (single line to full function)","code snippet (multiple lines)","structured code with type annotations"],"categories":["code-generation-editing","language-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_1","uri":"capability://planning.reasoning.agentic.reasoning.with.tool.use.planning","name":"agentic reasoning with tool-use planning","description":"Executes multi-step reasoning chains where the model decides when to call external tools, APIs, or functions based on task decomposition. Uses chain-of-thought patterns to break down complex problems into subtasks, generate tool invocation schemas, and reason about tool outputs before proceeding to the next step. Integrates with function-calling APIs (OpenAI-compatible, Anthropic-compatible) to bind external capabilities.","intents":["I need an AI agent that can decide whether to call a database query, API, or perform local computation","Build a multi-step workflow where the model reasons about intermediate results and adjusts strategy","Create a code-writing agent that can validate generated code against a test suite and iterate"],"best_for":["teams building autonomous agents for software engineering tasks (code review, refactoring, testing)","developers creating task-specific agents that combine LLM reasoning with deterministic tool execution","builders prototyping agentic workflows before scaling to production orchestration systems"],"limitations":["No built-in state persistence — requires external database or message queue for multi-turn agent memory","Tool-use planning adds 200-500ms latency per reasoning step due to token generation overhead","Limited to synchronous tool execution — no native support for parallel tool invocations or async workflows","Reasoning quality degrades with task complexity beyond 5-7 sequential steps without explicit prompt engineering"],"requires":["API key for Mistral AI or OpenRouter","Function schema definitions in JSON Schema format","External tool execution environment (HTTP endpoints, local functions, or database connections)","Prompt engineering for task decomposition and tool selection guidance"],"input_types":["natural language task description","structured tool definitions (JSON Schema)","code snippets or test cases for validation"],"output_types":["tool invocation sequence (function name + arguments)","reasoning trace (chain-of-thought steps)","final result after tool execution and aggregation"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_10","uri":"capability://text.generation.language.streaming.response.generation.for.real.time.agent.feedback","name":"streaming response generation for real-time agent feedback","description":"Streams token-by-token responses enabling real-time display of reasoning traces, code generation, and tool-use planning as it happens. Supports streaming of intermediate reasoning steps, allowing agents to display chain-of-thought reasoning to users or downstream systems in real-time. Integrates with streaming APIs (Server-Sent Events, WebSockets) for low-latency feedback.","intents":["Display code generation in real-time as the model writes it","Show reasoning traces as an agent thinks through a problem","Stream tool-use planning decisions for transparency and debugging"],"best_for":["developers building interactive coding assistants with real-time feedback","teams building transparent agents where users see reasoning in real-time","builders creating streaming chat interfaces with code generation"],"limitations":["Streaming adds complexity to error handling — partial responses may be incomplete if connection drops","Token-by-token streaming prevents batching optimizations, potentially increasing latency for non-streaming use cases","Streaming responses cannot be easily cached or deduplicated","Real-time display of incomplete reasoning may confuse users if intermediate steps are incorrect"],"requires":["API key for Mistral AI or OpenRouter","HTTP client with streaming support (fetch with ReadableStream, axios with responseType: 'stream', etc.)","Frontend or display system capable of handling streaming responses"],"input_types":["prompt or code completion request","agent task with tool definitions"],"output_types":["streamed tokens (text, code, or reasoning)","structured streaming events (tool calls, reasoning steps)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_2","uri":"capability://code.generation.editing.code.refactoring.and.transformation.with.structural.awareness","name":"code refactoring and transformation with structural awareness","description":"Analyzes existing code and applies transformations (renaming, extracting functions, converting patterns, modernizing syntax) while preserving semantics and maintaining code structure. Uses AST-aware reasoning to understand code dependencies, scope, and control flow, enabling safe refactoring that respects language-specific constraints and avoids breaking changes.","intents":["Refactor legacy code to modern patterns while maintaining backward compatibility","Extract common logic into reusable functions across a codebase","Convert code between language versions or frameworks (e.g., Python 2 to 3, React class to hooks)"],"best_for":["teams maintaining large codebases with technical debt","developers automating repetitive refactoring tasks across multiple files","engineering teams preparing code for migration to new frameworks or language versions"],"limitations":["Refactoring suggestions may not account for runtime behavior in dynamic languages (Python, JavaScript) without explicit type hints","No built-in test execution — refactored code requires manual validation or integration with CI/CD for automated testing","Context window limits prevent refactoring very large files (>2K lines) in a single pass","May miss implicit dependencies or side effects in loosely-coupled architectures"],"requires":["API key for Mistral AI or OpenRouter","Source code in supported language (Python, JavaScript, Java, C++, Go, Rust, etc.)","Optional: test suite or type definitions for validation","Optional: AST parser or linter output for structural guidance"],"input_types":["source code file or snippet","refactoring intent (natural language or structured directive)","test cases or type definitions for validation"],"output_types":["refactored code with inline comments explaining changes","diff-format output showing before/after","structured refactoring plan with risk assessment"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_3","uri":"capability://code.generation.editing.code.review.and.quality.analysis.with.architectural.reasoning","name":"code review and quality analysis with architectural reasoning","description":"Analyzes code for bugs, style violations, performance issues, and architectural concerns by reasoning about code patterns, dependencies, and best practices. Generates detailed review comments with specific line references, severity levels, and actionable remediation steps. Uses knowledge of common vulnerability patterns, performance anti-patterns, and language-specific idioms to provide context-aware feedback.","intents":["Automate code review for pull requests to catch common bugs and style issues before human review","Identify performance bottlenecks and suggest optimizations with complexity analysis","Detect security vulnerabilities and suggest hardening strategies"],"best_for":["teams implementing automated code review gates in CI/CD pipelines","open-source projects with limited maintainer bandwidth for code review","developers seeking architectural feedback on code organization and design patterns"],"limitations":["Security analysis limited to pattern-matching known vulnerabilities — cannot detect novel zero-days or context-specific exploits","Performance analysis lacks runtime profiling data — suggestions are heuristic-based and may not reflect actual bottlenecks","Architectural feedback depends on explicit documentation or naming conventions; implicit architectural intent may be missed","False positives on style violations in codebases with non-standard conventions"],"requires":["API key for Mistral AI or OpenRouter","Source code in supported language","Optional: linter output or AST for structural analysis","Optional: test coverage metrics or performance profiles for context"],"input_types":["source code file or diff","pull request description or commit message","test results or coverage reports"],"output_types":["structured review comments with line numbers and severity","list of detected issues categorized by type (bug, style, performance, security)","remediation suggestions with code examples"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_4","uri":"capability://code.generation.editing.test.case.generation.and.validation","name":"test case generation and validation","description":"Generates unit tests, integration tests, and edge-case test scenarios based on code analysis and specification. Understands function signatures, docstrings, and type hints to infer expected behavior and generate comprehensive test coverage. Validates generated tests against the code to ensure they pass and provide meaningful coverage, with support for multiple testing frameworks (pytest, Jest, JUnit, etc.).","intents":["Generate unit tests for existing functions to improve code coverage","Create edge-case and boundary-condition tests for critical functions","Generate integration tests that validate interactions between multiple components"],"best_for":["teams improving test coverage on legacy codebases without existing tests","developers writing test-driven development code and needing test scaffolding","QA teams automating test case generation for regression testing"],"limitations":["Generated tests may not cover all real-world edge cases without explicit specification of business logic","Test generation assumes deterministic behavior — non-deterministic or time-dependent code requires manual test adjustments","No built-in test execution environment — requires integration with actual test runner for validation","Mock generation is heuristic-based and may not accurately simulate complex dependencies"],"requires":["API key for Mistral AI or OpenRouter","Source code with function signatures and docstrings","Test framework installed (pytest, Jest, JUnit, etc.)","Optional: existing test examples for style matching"],"input_types":["function signature with docstring","source code file or snippet","specification or requirements document"],"output_types":["test code in framework-specific syntax","test execution results with pass/fail status","coverage report showing lines/branches covered"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_5","uri":"capability://code.generation.editing.api.documentation.generation.and.schema.inference","name":"api documentation generation and schema inference","description":"Analyzes code and generates comprehensive API documentation including endpoint descriptions, parameter specifications, return types, and usage examples. Infers OpenAPI/Swagger schemas from code structure, type hints, and docstrings. Generates human-readable documentation in Markdown, HTML, or interactive formats with examples and error handling documentation.","intents":["Generate OpenAPI schemas from existing REST API code","Create comprehensive API documentation with examples for external developers","Infer parameter types and constraints from code to auto-generate schema validation"],"best_for":["teams maintaining APIs without formal documentation","developers exposing internal APIs and needing quick documentation generation","teams standardizing on OpenAPI/Swagger for API contracts"],"limitations":["Schema inference depends on explicit type hints — dynamically-typed code requires manual annotation","Generated examples may not reflect all valid input combinations or error scenarios","Documentation accuracy depends on docstring quality and completeness","No automatic validation that generated schemas match actual runtime behavior"],"requires":["API key for Mistral AI or OpenRouter","Source code with type hints or docstrings","Optional: existing OpenAPI schema for reference"],"input_types":["source code (REST API, GraphQL, gRPC definitions)","type definitions or interface specifications","docstrings or comments describing endpoints"],"output_types":["OpenAPI/Swagger JSON or YAML","Markdown documentation","HTML documentation site","code examples in multiple languages"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_6","uri":"capability://code.generation.editing.debugging.assistance.with.root.cause.analysis","name":"debugging assistance with root-cause analysis","description":"Analyzes error messages, stack traces, and code context to identify root causes and suggest fixes. Uses reasoning about control flow, variable state, and common bug patterns to pinpoint the source of issues. Generates debugging strategies (breakpoint placement, logging statements, test cases) and provides step-by-step remediation guidance with code examples.","intents":["I have a stack trace and need to understand what went wrong in my code","Debug intermittent failures by analyzing logs and suggesting root causes","Generate debugging code (logging, assertions) to isolate issues"],"best_for":["developers debugging complex issues in unfamiliar codebases","teams analyzing production errors and post-mortems","developers learning debugging techniques and best practices"],"limitations":["Root-cause analysis depends on complete stack traces and relevant logs — incomplete information leads to speculative suggestions","Cannot debug issues requiring runtime state inspection without explicit memory dumps or profiler output","Suggestions may not account for race conditions or timing-dependent bugs without explicit concurrency context","Performance debugging requires profiler output — heuristic suggestions may not identify actual bottlenecks"],"requires":["API key for Mistral AI or OpenRouter","Error message or stack trace","Source code context around error location","Optional: logs, profiler output, or memory dumps"],"input_types":["error message or exception","stack trace","source code snippet","logs or debug output"],"output_types":["root-cause analysis with explanation","suggested fixes with code examples","debugging strategy (breakpoints, logging, tests)","prevention recommendations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_7","uri":"capability://code.generation.editing.natural.language.to.code.translation.with.intent.preservation","name":"natural language to code translation with intent preservation","description":"Converts natural language specifications, requirements, or pseudocode into executable code while preserving intent and handling ambiguity through clarifying questions or reasonable assumptions. Uses semantic understanding of programming concepts to map natural language descriptions to idiomatic code patterns in the target language. Supports incremental refinement through iterative feedback.","intents":["Convert a written algorithm description or pseudocode into working code","Translate requirements or user stories into code scaffolding","Generate code from natural language descriptions of business logic"],"best_for":["non-technical stakeholders or domain experts describing features to developers","developers rapidly prototyping from specifications","teams translating pseudocode or algorithm descriptions into production code"],"limitations":["Ambiguous specifications may result in code that doesn't match actual intent — requires validation and iteration","Complex business logic with implicit constraints may be misinterpreted without explicit specification","Generated code may not follow team conventions or architectural patterns without explicit guidance","Performance characteristics of generated code may not match requirements without explicit optimization directives"],"requires":["API key for Mistral AI or OpenRouter","Natural language specification or pseudocode","Target programming language specification","Optional: existing code examples for style matching"],"input_types":["natural language description","pseudocode or algorithm description","requirements or user story","specification document"],"output_types":["executable code in target language","code with explanatory comments","multiple implementation options for selection"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_8","uri":"capability://code.generation.editing.code.explanation.and.documentation.with.architectural.context","name":"code explanation and documentation with architectural context","description":"Analyzes code and generates human-readable explanations at multiple levels of detail (line-by-line, function-level, module-level, architectural). Explains intent, design decisions, and how components interact. Generates documentation in multiple formats (docstrings, comments, markdown guides) with examples and architectural diagrams in text form.","intents":["Understand what a complex function or module does without reading all the code","Generate docstrings and comments for undocumented code","Create architectural documentation explaining how components interact"],"best_for":["developers onboarding to unfamiliar codebases","teams documenting legacy code without existing documentation","technical writers creating developer guides from source code"],"limitations":["Explanations depend on code clarity — obfuscated or poorly-named code results in inaccurate explanations","Architectural context requires understanding of module dependencies — may miss implicit architectural patterns","Generated documentation may not reflect actual business intent if code diverges from requirements","Explanations are heuristic-based and may miss subtle behaviors or edge cases"],"requires":["API key for Mistral AI or OpenRouter","Source code in supported language","Optional: existing documentation or comments for context"],"input_types":["source code file or snippet","function or class definition","module or package structure"],"output_types":["natural language explanation","docstring in standard format (JSDoc, Sphinx, etc.)","inline comments","architectural documentation"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-medium__cap_9","uri":"capability://code.generation.editing.multi.file.codebase.reasoning.and.cross.file.refactoring","name":"multi-file codebase reasoning and cross-file refactoring","description":"Understands relationships between multiple files in a codebase and performs refactorings that span file boundaries while maintaining consistency. Tracks imports, dependencies, and type definitions across files to enable safe cross-file transformations. Uses codebase-wide context to suggest refactorings that improve modularity and reduce coupling.","intents":["Refactor a function that's used across multiple files while updating all call sites","Extract common logic into a shared module and update all imports","Rename a class or function across an entire codebase with type-safe updates"],"best_for":["teams performing large-scale refactorings across multiple files","developers improving code modularity and reducing coupling","teams migrating code between different module structures or architectures"],"limitations":["Context window limits prevent analyzing very large codebases (>100K lines) in a single pass","Cross-file reasoning requires understanding of import systems and module resolution — may miss dynamic imports or circular dependencies","Refactoring suggestions may not account for runtime behavior in dynamic languages without explicit type hints","No built-in test execution — cross-file refactorings require validation through CI/CD"],"requires":["API key for Mistral AI or OpenRouter","Source code for multiple related files","Optional: dependency graph or import analysis","Optional: type definitions or linter output for structural guidance"],"input_types":["multiple source files","refactoring intent (natural language or structured directive)","dependency graph or import analysis"],"output_types":["refactored code across multiple files","import updates and dependency changes","migration guide for cross-file changes"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API key for Mistral AI or OpenRouter access","HTTP client capable of streaming responses","Code editor or IDE with API integration (VS Code extension, Vim plugin, or custom wrapper)","API key for Mistral AI or OpenRouter","Function schema definitions in JSON Schema format","External tool execution environment (HTTP endpoints, local functions, or database connections)","Prompt engineering for task decomposition and tool selection guidance","HTTP client with streaming support (fetch with ReadableStream, axios with responseType: 'stream', etc.)","Frontend or display system capable of handling streaming responses","Source code in supported language (Python, JavaScript, Java, C++, Go, Rust, etc.)"],"failure_modes":["Context window limited to ~4K tokens, constraining multi-file reasoning for large codebases","No real-time linting or syntax validation — generated code may contain subtle type errors in statically-typed languages","Training data cutoff means unfamiliarity with very recent language features or framework APIs released after training","No built-in state persistence — requires external database or message queue for multi-turn agent memory","Tool-use planning adds 200-500ms latency per reasoning step due to token generation overhead","Limited to synchronous tool execution — no native support for parallel tool invocations or async workflows","Reasoning quality degrades with task complexity beyond 5-7 sequential steps without explicit prompt engineering","Streaming adds complexity to error handling — partial responses may be incomplete if connection drops","Token-by-token streaming prevents batching optimizations, potentially increasing latency for non-streaming use cases","Streaming responses cannot be easily cached or deduplicated","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.24,"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-devstral-medium","compare_url":"https://unfragile.ai/compare?artifact=mistralai-devstral-medium"}},"signature":"CZ+2vHOvZXlZEQp1dznrPyABRjTS20TcOUCtQ9Ip9GXG4v5tfCDJFGs7eOym5YrFJdA8YTj30gkKGEWe4Ae3DA==","signedAt":"2026-06-19T22:23:27.786Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-devstral-medium","artifact":"https://unfragile.ai/mistralai-devstral-medium","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-devstral-medium","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"}}