{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-mistralai-devstral-2512","slug":"mistralai-devstral-2512","name":"Mistral: Devstral 2 2512","type":"model","url":"https://openrouter.ai/models/mistralai~devstral-2512","page_url":"https://unfragile.ai/mistralai-devstral-2512","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-2512__cap_0","uri":"capability://code.generation.editing.agentic.code.generation.with.tool.planning","name":"agentic-code-generation-with-tool-planning","description":"Generates code by decomposing development tasks into sub-steps and planning tool use (function calls, API invocations, file operations) before execution. Uses a 123B dense transformer architecture trained on agentic coding patterns to reason about multi-step workflows, select appropriate tools, and generate executable code that orchestrates external systems. Supports iterative refinement through agent feedback loops.","intents":["I need to generate code that calls multiple APIs and orchestrates their results","I want an AI to plan out a multi-file refactoring before executing it","I need code generation that understands when to use external tools vs inline logic","I want to build autonomous agents that can reason about task decomposition"],"best_for":["teams building LLM-powered agents and autonomous coding systems","developers creating multi-step workflow automation","AI engineers prototyping agentic architectures"],"limitations":["256K context window limits multi-file codebase analysis to ~80K tokens of actual code before hitting practical limits","No built-in execution sandbox — generated code must be validated before running in production","Tool planning quality depends on clarity of tool schema definitions; ambiguous schemas degrade planning accuracy","Agentic reasoning adds latency (~2-5s per planning step) compared to direct code generation"],"requires":["API access via OpenRouter or direct Mistral API endpoint","Tool/function schema definitions in JSON Schema or OpenAI function-calling format","Structured prompt engineering for agent task definition","External execution environment for generated code"],"input_types":["text (natural language task descriptions)","code (existing codebase context, up to 256K tokens)","structured data (tool schemas, API specifications)"],"output_types":["code (Python, JavaScript, TypeScript, Go, Rust, etc.)","structured tool calls (function invocations with arguments)","task decomposition plans (step-by-step reasoning)"],"categories":["code-generation-editing","planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_1","uri":"capability://code.generation.editing.long.context.code.understanding.and.analysis","name":"long-context-code-understanding-and-analysis","description":"Analyzes and reasons about large codebases up to 256K tokens (~80K lines of code) in a single context window using a dense transformer architecture. Maintains coherent understanding of cross-file dependencies, architectural patterns, and semantic relationships without requiring chunking or retrieval augmentation. Enables full-codebase refactoring analysis, impact assessment, and architectural recommendations.","intents":["I need to understand how a change in one file affects the entire codebase","I want to analyze architectural patterns across a large monorepo","I need to assess the impact of a refactoring before executing it","I want to generate documentation that reflects the actual codebase structure"],"best_for":["teams maintaining large monorepos (50K-200K lines)","developers performing large-scale refactorings","architects analyzing codebase health and dependencies"],"limitations":["256K token limit still insufficient for very large monorepos (>500K lines); requires strategic file selection","Long-context processing increases latency (~5-10s for full 256K context) compared to short-context models","Attention mechanism may dilute focus on specific code sections when context is maximally filled","No incremental analysis — each query requires reprocessing the full context"],"requires":["API access to Mistral via OpenRouter or direct endpoint","Codebase formatted as text (concatenated files or structured code blocks)","Clear context boundaries (file separators, language markers)"],"input_types":["code (multiple files, up to 256K tokens total)","text (architectural documentation, design docs)"],"output_types":["text (analysis, recommendations, impact assessments)","code (refactored snippets, architectural changes)","structured data (dependency graphs, architectural patterns)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_10","uri":"capability://code.generation.editing.code.migration.and.language.translation","name":"code-migration-and-language-translation","description":"Translates code between programming languages while preserving intent and functionality. Understands language-specific idioms and generates idiomatic code in target language rather than literal translations. Handles library/framework mapping (e.g., Django to FastAPI, React to Vue) and maintains architectural patterns across language boundaries.","intents":["I want to migrate code from Python to Go while maintaining architecture","I need to translate a React component to Vue with the same functionality","I want to migrate from Django to FastAPI with minimal refactoring","I need to understand how to express a pattern in a different language"],"best_for":["teams migrating between languages or frameworks","polyglot organizations standardizing on new tech stacks","developers learning new languages by translating familiar code"],"limitations":["Translation quality depends on language similarity; Python-to-Go is easier than Python-to-Haskell","Framework mappings are heuristic-based; some features may not have direct equivalents","Idiomatic translation requires deep knowledge of both languages; edge cases may be missed","Performance characteristics may differ significantly between languages; optimization may be needed"],"requires":["Source code in source language","Target language specification","Optional: framework/library mappings"],"input_types":["code (source code to translate)","text (migration requirements, constraints)"],"output_types":["code (translated code in target language)","text (migration notes, framework mapping explanations)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_11","uri":"capability://code.generation.editing.debugging.and.error.analysis","name":"debugging-and-error-analysis","description":"Analyzes error messages, stack traces, and failing code to identify root causes and generate fixes. Understands common error patterns and debugging techniques. Provides step-by-step debugging guidance and generates code that addresses identified issues. Supports multi-turn debugging conversations where each iteration narrows down the problem.","intents":["I want the model to explain what's causing this error","I need help debugging a complex issue with a stack trace","I want the model to generate a fix for this failing code","I need step-by-step debugging guidance to understand the issue"],"best_for":["developers debugging complex issues","teams with CI/CD pipelines that feed test failures to code generation","developers learning debugging techniques"],"limitations":["Root cause analysis is heuristic-based; may identify symptoms rather than root causes","Requires sufficient context (error messages, code, stack traces) for accurate diagnosis","Some issues require runtime inspection; static analysis may miss them","Multi-turn debugging can fill context window quickly"],"requires":["Error message or stack trace","Code context (failing function, surrounding code)","Optional: reproduction steps or test case"],"input_types":["text (error messages, stack traces)","code (failing code, surrounding context)"],"output_types":["text (root cause analysis, debugging guidance)","code (fixes, debugging code)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_12","uri":"capability://code.generation.editing.code.review.and.quality.assessment","name":"code-review-and-quality-assessment","description":"Reviews code for quality issues (style violations, potential bugs, performance problems, maintainability concerns) and provides actionable feedback. Understands code quality metrics and best practices for specific languages and frameworks. Generates detailed review comments with explanations and suggested improvements.","intents":["I want automated code review that catches issues before human review","I need feedback on code quality and maintainability","I want to enforce team coding standards automatically","I need detailed explanations of why code should be changed"],"best_for":["teams automating code review in CI/CD pipelines","teams enforcing coding standards","developers learning best practices through AI feedback"],"limitations":["Review quality depends on code clarity and context","No built-in enforcement — requires integration with CI/CD to block merges","May generate false positives (flagging valid patterns as issues)","Subjective quality metrics (readability, maintainability) are heuristic-based"],"requires":["Code to review (functions, files, pull requests)","Optional: coding standards or style guide","Optional: team conventions or best practices"],"input_types":["code (code to review)","text (coding standards, team conventions)"],"output_types":["text (review comments, quality assessment)","structured data (quality metrics, issue severity)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_2","uri":"capability://code.generation.editing.multi.language.code.generation.with.syntax.preservation","name":"multi-language-code-generation-with-syntax-preservation","description":"Generates syntactically correct code across 40+ programming languages (Python, JavaScript, TypeScript, Go, Rust, Java, C++, C#, etc.) while preserving language-specific idioms, conventions, and best practices. Uses language-aware tokenization and training data balanced across multiple language ecosystems to avoid bias toward Python/JavaScript. Maintains consistency with existing codebase style when provided as context.","intents":["I need to generate Go code that follows Go idioms, not Python patterns translated to Go","I want code generation that respects my team's language-specific conventions","I need to generate code in a language I'm less familiar with and trust it's idiomatic","I want to migrate code from one language to another while preserving intent"],"best_for":["polyglot teams using multiple languages across services","developers working in less common languages (Go, Rust, Kotlin)","teams enforcing language-specific style guides"],"limitations":["Quality varies by language — Python and JavaScript are highest quality due to training data prevalence","Niche languages (Elixir, Clojure, Haskell) may generate syntactically correct but non-idiomatic code","No built-in linting — generated code should be validated with language-specific tools","Type system understanding is uneven; complex generic types in TypeScript/Go may be less accurate"],"requires":["API access to Mistral","Language specification in prompt (explicit language name or file extension context)","Optional: existing codebase context to infer style"],"input_types":["text (natural language requirements)","code (existing code in target language for style inference)"],"output_types":["code (syntactically valid in target language)","text (explanations of language-specific choices)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_3","uri":"capability://tool.use.integration.function.calling.with.structured.tool.schemas","name":"function-calling-with-structured-tool-schemas","description":"Executes function calls and tool invocations using structured JSON schemas (OpenAI function-calling format, JSON Schema) to define tool interfaces. Model reasons about which tools to invoke, generates properly-typed arguments, and handles tool response integration. Supports parallel tool execution, error handling, and multi-turn tool use within a single conversation context.","intents":["I need the model to decide when and how to call my APIs based on user requests","I want structured function calling that validates argument types before execution","I need to chain multiple tool calls together (e.g., fetch data, transform it, store it)","I want the model to handle tool errors and retry with different arguments"],"best_for":["developers building LLM agents with external tool integration","teams implementing ReAct or similar agentic patterns","applications requiring deterministic function calling with schema validation"],"limitations":["Tool selection quality depends on schema clarity — ambiguous tool descriptions degrade accuracy","No built-in tool execution — requires external orchestration layer to actually invoke tools","Parallel tool calling support is model-dependent; sequential execution is more reliable","Tool response integration requires explicit prompt engineering; model doesn't automatically learn from tool outputs"],"requires":["Tool schemas in JSON Schema or OpenAI function-calling format","API endpoint or function registry for actual tool execution","Structured prompt that defines tool availability and usage patterns"],"input_types":["text (user requests, tool descriptions)","structured data (JSON Schema tool definitions)"],"output_types":["structured data (function calls with typed arguments)","text (reasoning about tool selection)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_4","uri":"capability://code.generation.editing.iterative.code.refinement.with.feedback.loops","name":"iterative-code-refinement-with-feedback-loops","description":"Accepts code feedback (test failures, linting errors, performance issues, architectural concerns) and iteratively refines generated code based on explicit constraints. Maintains context of previous iterations and reasons about trade-offs between competing requirements (performance vs readability, type safety vs flexibility). Supports multi-turn conversations where each turn builds on previous code generation decisions.","intents":["I want to generate code, run tests, and have the model fix failures automatically","I need the model to optimize code based on performance profiling results","I want to enforce architectural constraints and have the model respect them in refinements","I need to iterate on code quality (linting, type safety) without starting from scratch"],"best_for":["developers using AI in tight feedback loops (TDD, iterative development)","teams with automated testing pipelines that feed results back to code generation","AI-assisted development workflows where human feedback drives refinement"],"limitations":["Context window fills quickly with multi-turn iterations; 256K limit supports ~10-20 refinement cycles before truncation","Model may over-fit to specific feedback and lose generality; requires careful prompt engineering","No memory across sessions — each new conversation loses previous refinement history","Conflicting feedback (e.g., 'make it faster' vs 'make it more readable') requires explicit prioritization"],"requires":["API access to Mistral with conversation/multi-turn support","Structured feedback format (test output, linting errors, performance metrics)","Clear prioritization of competing requirements"],"input_types":["code (previously generated code)","text (feedback, error messages, requirements)","structured data (test results, performance metrics)"],"output_types":["code (refined version addressing feedback)","text (explanation of changes and trade-offs)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_5","uri":"capability://code.generation.editing.architectural.pattern.recognition.and.generation","name":"architectural-pattern-recognition-and-generation","description":"Identifies architectural patterns in existing code (MVC, CQRS, event-driven, microservices, etc.) and generates new code that follows recognized patterns. Uses semantic understanding of code structure to infer architectural intent and maintain consistency when extending or refactoring. Supports pattern-aware code generation that respects existing architectural decisions.","intents":["I want the model to recognize my codebase uses CQRS and generate new code following that pattern","I need to refactor code while maintaining the existing architectural pattern","I want to generate boilerplate that follows my team's standard architectural patterns","I need to assess whether new code violates existing architectural constraints"],"best_for":["teams with established architectural patterns and style guides","architects ensuring consistency across large codebases","teams migrating between architectural patterns"],"limitations":["Pattern recognition is heuristic-based; complex or hybrid patterns may be misidentified","No formal architecture validation — generated code may technically follow patterns but violate domain-specific constraints","Requires sufficient codebase context to infer patterns; small codebases may not provide enough signal","Pattern-aware generation adds latency (~1-2s) compared to pattern-agnostic generation"],"requires":["Sufficient codebase context (10K+ tokens) to infer architectural patterns","Optional: explicit architectural documentation or pattern definitions"],"input_types":["code (existing codebase for pattern inference)","text (architectural requirements, pattern descriptions)"],"output_types":["code (pattern-consistent implementation)","text (architectural analysis, pattern identification)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_6","uri":"capability://code.generation.editing.test.generation.and.validation","name":"test-generation-and-validation","description":"Generates unit tests, integration tests, and end-to-end tests from code specifications and existing implementations. Understands testing frameworks (pytest, Jest, JUnit, etc.) and generates tests that cover edge cases, error conditions, and happy paths. Validates generated code against test suites and suggests fixes when tests fail.","intents":["I want to generate comprehensive test suites for existing code","I need tests for edge cases and error conditions that I might miss","I want the model to validate generated code by running tests","I need to generate tests in a specific framework (pytest, Jest, etc.)"],"best_for":["teams practicing test-driven development","developers generating code that must pass test suites","teams with high test coverage requirements"],"limitations":["Test quality depends on code clarity; poorly documented code generates weak tests","No built-in test execution — requires external test runner integration","Framework-specific idioms vary; generated tests may not follow team conventions","Edge case coverage is heuristic-based; critical edge cases may be missed"],"requires":["Code to test (function signatures, implementations)","Test framework specification (pytest, Jest, JUnit, etc.)","Optional: existing test examples for style inference"],"input_types":["code (functions, classes, modules to test)","text (test requirements, edge cases)"],"output_types":["code (test files in specified framework)","text (test coverage analysis)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_7","uri":"capability://text.generation.language.documentation.generation.from.code","name":"documentation-generation-from-code","description":"Generates API documentation, README files, and inline code comments from source code and architectural context. Understands code intent from implementation details and generates documentation that accurately reflects behavior. Supports multiple documentation formats (Markdown, Sphinx, JSDoc, etc.) and can infer documentation structure from codebase organization.","intents":["I want to generate API documentation that stays in sync with code changes","I need to generate README files that explain my codebase architecture","I want to add inline comments that explain non-obvious code decisions","I need documentation in a specific format (Markdown, Sphinx, etc.)"],"best_for":["teams maintaining large codebases with documentation debt","open-source projects needing comprehensive documentation","teams automating documentation generation in CI/CD"],"limitations":["Generated documentation may be verbose or miss domain-specific context","No automatic synchronization — documentation must be regenerated when code changes","Format-specific idioms vary; generated documentation may not match team style","Complex architectural decisions may be oversimplified in generated docs"],"requires":["Source code with clear structure and naming","Optional: existing documentation examples for style inference","Documentation format specification (Markdown, Sphinx, etc.)"],"input_types":["code (source files to document)","text (architectural overview, design decisions)"],"output_types":["text (documentation in specified format)","structured data (documentation metadata)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_8","uri":"capability://code.generation.editing.performance.optimization.and.profiling.guidance","name":"performance-optimization-and-profiling-guidance","description":"Analyzes code for performance bottlenecks and generates optimized implementations. Understands algorithmic complexity, memory usage patterns, and language-specific performance characteristics. Provides optimization suggestions with trade-off analysis (latency vs memory, throughput vs latency) and generates optimized code variants.","intents":["I want the model to identify performance bottlenecks in my code","I need optimized implementations with explanations of trade-offs","I want to understand why certain patterns are faster in my language","I need to generate code that meets specific performance targets"],"best_for":["performance-critical applications (real-time systems, high-throughput services)","developers optimizing existing code","teams with strict latency or throughput requirements"],"limitations":["Optimization suggestions are heuristic-based; actual performance depends on runtime environment","No built-in profiling — requires external tools to validate optimization claims","Language-specific optimizations vary; suggestions may not apply to all implementations","Trade-off analysis is qualitative; quantitative performance impact requires benchmarking"],"requires":["Code to optimize (functions, algorithms, hot paths)","Optional: performance requirements (latency targets, throughput targets)","Optional: profiling data (flame graphs, timing results)"],"input_types":["code (functions to optimize)","text (performance requirements, constraints)","structured data (profiling results)"],"output_types":["code (optimized implementations)","text (optimization explanations, trade-off analysis)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-mistralai-devstral-2512__cap_9","uri":"capability://code.generation.editing.security.vulnerability.detection.and.remediation","name":"security-vulnerability-detection-and-remediation","description":"Identifies common security vulnerabilities in code (SQL injection, XSS, insecure deserialization, weak cryptography, etc.) and generates secure implementations. Understands security best practices for specific frameworks and languages. Provides vulnerability explanations and remediation guidance with secure code examples.","intents":["I want the model to identify security vulnerabilities in my code","I need secure implementations that follow OWASP guidelines","I want to understand why certain patterns are insecure","I need to remediate vulnerabilities while maintaining functionality"],"best_for":["security-conscious teams and applications","developers building authentication/authorization systems","teams with security compliance requirements (HIPAA, PCI-DSS, etc.)"],"limitations":["Vulnerability detection is pattern-based; complex or novel vulnerabilities may be missed","No built-in security scanning — requires external tools for comprehensive assessment","Remediation suggestions may introduce performance overhead or API changes","Security best practices evolve; generated code may become outdated"],"requires":["Code to analyze (functions, modules with security implications)","Optional: security requirements or compliance standards","Optional: threat model or attack surface definition"],"input_types":["code (security-sensitive code)","text (security requirements, threat model)"],"output_types":["code (secure implementations)","text (vulnerability explanations, remediation guidance)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Mistral API endpoint","Tool/function schema definitions in JSON Schema or OpenAI function-calling format","Structured prompt engineering for agent task definition","External execution environment for generated code","API access to Mistral via OpenRouter or direct endpoint","Codebase formatted as text (concatenated files or structured code blocks)","Clear context boundaries (file separators, language markers)","Source code in source language","Target language specification","Optional: framework/library mappings"],"failure_modes":["256K context window limits multi-file codebase analysis to ~80K tokens of actual code before hitting practical limits","No built-in execution sandbox — generated code must be validated before running in production","Tool planning quality depends on clarity of tool schema definitions; ambiguous schemas degrade planning accuracy","Agentic reasoning adds latency (~2-5s per planning step) compared to direct code generation","256K token limit still insufficient for very large monorepos (>500K lines); requires strategic file selection","Long-context processing increases latency (~5-10s for full 256K context) compared to short-context models","Attention mechanism may dilute focus on specific code sections when context is maximally filled","No incremental analysis — each query requires reprocessing the full context","Translation quality depends on language similarity; Python-to-Go is easier than Python-to-Haskell","Framework mappings are heuristic-based; some features may not have direct equivalents","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"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-2512","compare_url":"https://unfragile.ai/compare?artifact=mistralai-devstral-2512"}},"signature":"5YynjUu41fwaOy1yCMG5lgZCQNmIGlbCnIetU2g8gqey/xftPe9EZKSFPFutahTC1+ojRks/lri5QE8h03/+DQ==","signedAt":"2026-06-21T14:28:11.015Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mistralai-devstral-2512","artifact":"https://unfragile.ai/mistralai-devstral-2512","verify":"https://unfragile.ai/api/v1/verify?slug=mistralai-devstral-2512","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"}}