{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemini-3.1-pro-preview","slug":"google-gemini-3.1-pro-preview","name":"Google: Gemini 3.1 Pro Preview","type":"model","url":"https://openrouter.ai/models/google~gemini-3.1-pro-preview","page_url":"https://unfragile.ai/google-gemini-3.1-pro-preview","categories":["model-training"],"tags":["google","api-access","text","image","audio","video"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemini-3.1-pro-preview__cap_0","uri":"capability://code.generation.editing.multimodal.reasoning.with.enhanced.software.engineering.performance","name":"multimodal reasoning with enhanced software engineering performance","description":"Processes and reasons across text, code, images, audio, and video inputs simultaneously using a unified transformer architecture optimized for complex software engineering tasks. The model applies chain-of-thought reasoning patterns internally to decompose multi-step coding problems, architectural decisions, and system design challenges, with architectural improvements that reduce hallucination in code generation and increase correctness on competitive programming and system design benchmarks.","intents":["I need to analyze a screenshot of a system architecture diagram and generate corresponding infrastructure-as-code","I want to debug a complex multi-file codebase issue by providing code snippets, error logs, and architectural context simultaneously","I need to understand a video tutorial on a new framework and generate boilerplate code based on what I learned","I want to review code quality across multiple languages and formats in a single request"],"best_for":["software engineers building complex systems requiring cross-modal understanding","teams migrating legacy systems who need to analyze documentation, diagrams, and code together","AI agents performing multi-step software engineering workflows"],"limitations":["Audio and video inputs require preprocessing into compatible formats; raw video files may need transcoding","Context window constraints limit the total amount of multimodal data processable in a single request","Image understanding quality varies by resolution and complexity; OCR-heavy tasks may require supplementary text input","No real-time streaming of video/audio — batch processing only"],"requires":["API key for Google Gemini or OpenRouter access","Supported input formats: JPEG, PNG, GIF, WebP for images; MP3, WAV, FLAC for audio; MP4, WebM for video","Network connectivity for API calls","Minimum context length of 32K tokens recommended for complex multi-modal tasks"],"input_types":["text (code, documentation, natural language queries)","image (screenshots, diagrams, design mockups, charts)","audio (voice instructions, meeting recordings)","video (tutorials, screen recordings, demos)"],"output_types":["text (explanations, code generation, analysis)","code (multiple languages)","structured data (JSON, YAML configurations)","reasoning traces (step-by-step problem decomposition)"],"categories":["code-generation-editing","image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_1","uri":"capability://planning.reasoning.agentic.task.execution.with.improved.reliability","name":"agentic task execution with improved reliability","description":"Implements enhanced agentic patterns through improved instruction following, better handling of tool-use sequences, and more robust error recovery in multi-step workflows. The model uses internal reasoning to plan action sequences, validate intermediate results, and adapt when encountering failures, with architectural improvements that reduce agent hallucination and improve task completion rates in autonomous workflows.","intents":["I want to deploy an AI agent that can autonomously debug production issues by gathering logs, analyzing them, and proposing fixes","I need an agent that can orchestrate multiple API calls to different services and handle partial failures gracefully","I want to build a code review agent that can examine pull requests, run tests, and provide structured feedback"],"best_for":["teams building autonomous AI agents for DevOps and infrastructure tasks","developers creating multi-step workflow orchestrators that need reliable error handling","organizations deploying agents in production environments where reliability is critical"],"limitations":["Agent reliability improves with clear tool definitions but still requires explicit error handling in the orchestration layer","No built-in persistence of agent state — requires external state management for long-running tasks","Tool hallucination can still occur; requires validation of generated tool calls before execution","Complex branching logic in agents may require explicit prompt engineering to guide decision-making"],"requires":["API key for Google Gemini or OpenRouter","Tool/function definitions in JSON schema format","External orchestration framework (LangChain, LlamaIndex, custom implementation)","State management system for multi-turn agent interactions"],"input_types":["text (task descriptions, tool definitions)","structured data (JSON schemas for tools, previous execution history)"],"output_types":["text (reasoning and explanations)","structured data (tool calls with parameters)","code (generated scripts for task execution)"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_10","uri":"capability://code.generation.editing.api.documentation.generation.and.openapi.specification.creation","name":"api documentation generation and openapi specification creation","description":"Generates comprehensive API documentation and OpenAPI/Swagger specifications from code, comments, and requirements. The model extracts endpoint definitions, parameter types, response schemas, and error handling patterns to create machine-readable specifications that can be used for code generation, testing, and client library creation.","intents":["I need to generate OpenAPI specs from my existing REST API code","I want to create comprehensive API documentation from code comments and examples","I need to generate client libraries for multiple languages from API specifications"],"best_for":["API developers documenting REST and GraphQL APIs","teams automating client library generation","organizations standardizing API documentation across services"],"limitations":["Generated documentation requires review for accuracy and completeness","Complex API behaviors may not be fully captured in specifications","Authentication and authorization patterns may require manual specification","Generated specs may need refinement to match organizational standards"],"requires":["API key for Google Gemini or OpenRouter","Source code or API specification","Clear endpoint definitions and parameter documentation"],"input_types":["code (API implementation)","text (API documentation, requirements)"],"output_types":["structured data (OpenAPI/Swagger JSON or YAML)","text (markdown documentation)","code (client library stubs)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_11","uri":"capability://code.generation.editing.test.case.generation.and.test.coverage.analysis","name":"test case generation and test coverage analysis","description":"Generates comprehensive test cases covering normal cases, edge cases, and error conditions based on code analysis and requirements. The model understands control flow, data dependencies, and error handling patterns to create tests that maximize coverage and catch potential bugs, generating tests in multiple frameworks and languages.","intents":["I need to generate unit tests for a complex function with multiple branches and edge cases","I want to create integration tests for an API endpoint with various input scenarios","I need to analyze test coverage and generate tests for uncovered code paths"],"best_for":["teams improving test coverage and code quality","developers writing tests for legacy code without existing tests","organizations automating test generation for faster development"],"limitations":["Generated tests may not cover all business logic requirements; requires review","Test quality depends on code clarity and documentation","Complex integration scenarios may require manual test design","Generated tests may have false positives or negatives requiring refinement"],"requires":["API key for Google Gemini or OpenRouter","Source code to analyze","Test framework specification (Jest, pytest, JUnit, etc.)"],"input_types":["code (functions, classes, APIs to test)","text (requirements, test scenarios)"],"output_types":["code (test implementations)","text (test coverage analysis)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_12","uri":"capability://text.generation.language.technical.documentation.and.architecture.diagram.generation","name":"technical documentation and architecture diagram generation","description":"Generates technical documentation, architecture diagrams, and system design explanations from code, requirements, and architectural context. The model creates visual representations (as ASCII art or Mermaid diagrams), detailed explanations of system components, and documentation that helps teams understand complex systems.","intents":["I need to create architecture diagrams for a microservices system from code and requirements","I want to generate comprehensive technical documentation for a complex system","I need to explain system design decisions to new team members"],"best_for":["teams documenting complex systems and architectures","organizations onboarding new engineers","teams creating system design documentation for compliance or knowledge management"],"limitations":["Generated diagrams may require manual refinement for clarity and accuracy","Complex systems may be difficult to represent in simple diagrams","Documentation quality depends on code clarity and architectural decisions","Generated documentation may not capture all important design decisions"],"requires":["API key for Google Gemini or OpenRouter","Code or architectural specifications","Diagram format preference (ASCII, Mermaid, PlantUML)"],"input_types":["code (system implementation)","text (architectural requirements, design decisions)"],"output_types":["text (markdown documentation)","structured data (Mermaid or PlantUML diagram definitions)","code (diagram rendering code)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_2","uri":"capability://data.processing.analysis.efficient.token.usage.optimization.for.long.context.workflows","name":"efficient token usage optimization for long-context workflows","description":"Implements token-efficient processing through architectural improvements that reduce redundant computation and optimize attention patterns for long-context scenarios. The model uses techniques like token pruning, efficient caching of repeated patterns, and optimized positional embeddings to maintain performance while reducing token consumption across complex multi-turn conversations and large document processing tasks.","intents":["I need to process a 100K+ token codebase for analysis without exceeding my API budget","I want to maintain long-running conversations with context without exponential token growth","I need to analyze multiple large documents in a single batch operation efficiently"],"best_for":["cost-conscious teams processing large codebases or document collections","applications requiring long-context understanding with budget constraints","enterprises running high-volume inference workloads where token efficiency directly impacts costs"],"limitations":["Token efficiency gains are relative; absolute token consumption still scales with input size","Aggressive token optimization may slightly reduce output quality in edge cases","Efficiency improvements are most pronounced for repetitive or structured content; unstructured text sees smaller gains","No explicit control over token optimization level — applied automatically"],"requires":["API key for Google Gemini or OpenRouter","Understanding of token counting for cost estimation","Minimum context length of 32K tokens to see efficiency benefits"],"input_types":["text (code, documentation, conversations)","structured data (JSON, CSV, logs)"],"output_types":["text (analysis, summaries)","code","structured data"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_3","uri":"capability://code.generation.editing.code.generation.and.completion.across.40.programming.languages","name":"code generation and completion across 40+ programming languages","description":"Generates syntactically correct and semantically sound code across a wide range of programming languages using language-specific patterns learned during training. The model understands language idioms, standard libraries, and framework conventions for each language, enabling it to generate production-ready code snippets, complete partial implementations, and suggest refactorings with language-appropriate patterns.","intents":["I need to generate boilerplate code for a new microservice in Go, Python, and TypeScript simultaneously","I want to convert a Python function to Rust while maintaining the same logic and error handling","I need to complete a partially written function with proper error handling and type annotations"],"best_for":["polyglot development teams working across multiple languages","developers learning new languages who need idiomatic code examples","teams automating code generation for infrastructure and configuration"],"limitations":["Code generation quality varies by language popularity; less common languages may have lower accuracy","Generated code requires review and testing; no guarantee of correctness or security","Complex domain-specific languages or proprietary frameworks may not be well-represented in training data","No built-in linting or style enforcement — generated code may not match project conventions"],"requires":["API key for Google Gemini or OpenRouter","Clear code context or requirements specification","Testing infrastructure to validate generated code"],"input_types":["text (code snippets, requirements, comments)","code (partial implementations, function signatures)"],"output_types":["code (complete implementations, refactored code)","text (explanations of generated code)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_4","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.output.generation","name":"structured data extraction and schema-based output generation","description":"Extracts structured information from unstructured text, images, and documents by mapping content to predefined JSON schemas or custom output formats. The model uses semantic understanding to identify relevant information and format it according to specified schemas, enabling reliable extraction of entities, relationships, and attributes from complex documents without requiring regex or rule-based parsing.","intents":["I need to extract invoice details (vendor, amount, date, line items) from PDF documents and output as JSON","I want to parse API documentation and generate structured OpenAPI specifications","I need to extract structured data from unstructured logs and convert to CSV format"],"best_for":["teams automating data extraction from documents and logs","organizations migrating from rule-based extraction to semantic understanding","developers building data pipelines that require structured output from unstructured sources"],"limitations":["Extraction accuracy depends on schema clarity and document quality; ambiguous schemas may produce inconsistent results","Complex nested structures may require iterative refinement of schema definitions","No built-in validation of extracted data against schema constraints — requires post-processing validation","Performance degrades with very large documents or complex schemas"],"requires":["API key for Google Gemini or OpenRouter","Well-defined JSON schema or output format specification","Input documents in supported formats (text, images, PDFs)"],"input_types":["text (unstructured documents, logs)","image (scanned documents, screenshots)","structured data (partial data to be enriched)"],"output_types":["structured data (JSON, YAML, CSV)","code (generated parsers or validators)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_5","uri":"capability://planning.reasoning.reasoning.trace.generation.for.explainable.ai.outputs","name":"reasoning trace generation for explainable ai outputs","description":"Generates detailed step-by-step reasoning traces that explain how the model arrived at its conclusions, using chain-of-thought patterns to decompose complex problems into intermediate steps. The model can expose its internal reasoning process, making decisions transparent and enabling developers to understand failure modes and validate correctness of complex analyses.","intents":["I need to understand why the model rejected a code change and what alternatives it suggests","I want to audit the reasoning behind a security vulnerability assessment","I need to explain to stakeholders how the model arrived at a specific architectural recommendation"],"best_for":["teams building AI systems that require explainability for compliance or trust","developers debugging model behavior and understanding failure modes","organizations using AI for high-stakes decisions that require audit trails"],"limitations":["Reasoning traces add latency and token consumption; not suitable for real-time applications","Traces reflect the model's reasoning but may not capture all factors influencing the decision","Verbosity of traces can make them difficult to parse; requires post-processing for readability","No guarantee that reasoning traces are complete or fully accurate representations of internal computation"],"requires":["API key for Google Gemini or OpenRouter","Explicit request for reasoning traces in prompts","Post-processing logic to parse and format traces"],"input_types":["text (questions, problems, code)","structured data (context, constraints)"],"output_types":["text (reasoning traces, step-by-step explanations)","structured data (reasoning steps as JSON)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_6","uri":"capability://code.generation.editing.context.aware.code.refactoring.and.optimization.suggestions","name":"context-aware code refactoring and optimization suggestions","description":"Analyzes code within its full architectural context to suggest refactorings, optimizations, and improvements that maintain semantic correctness while improving performance, maintainability, or security. The model understands design patterns, architectural principles, and language-specific best practices to provide suggestions that align with project conventions and goals.","intents":["I want to refactor a legacy monolith into microservices with the model suggesting decomposition strategies","I need performance optimization suggestions for a bottleneck in my data processing pipeline","I want to modernize Python 2 code to Python 3 with proper type hints and async patterns"],"best_for":["teams modernizing legacy codebases","developers optimizing performance-critical code","organizations improving code quality and maintainability"],"limitations":["Refactoring suggestions require manual validation and testing; automated application may introduce bugs","Suggestions may not account for business constraints or technical debt trade-offs","Complex architectural refactorings require human judgment and may not be fully automated","Performance optimization suggestions are heuristic-based and may not be optimal for specific hardware"],"requires":["API key for Google Gemini or OpenRouter","Full codebase context or representative code samples","Testing infrastructure to validate refactored code"],"input_types":["code (full files or snippets)","text (requirements, constraints, performance goals)"],"output_types":["code (refactored implementations)","text (explanations of changes and rationale)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_7","uri":"capability://code.generation.editing.natural.language.to.code.translation.with.semantic.preservation","name":"natural language to code translation with semantic preservation","description":"Converts natural language descriptions, specifications, and requirements into executable code while preserving semantic intent and handling ambiguities through clarifying questions or reasonable assumptions. The model maps natural language concepts to programming constructs, handles implicit requirements, and generates code that matches the described behavior.","intents":["I have a detailed specification document and need to generate the corresponding API implementation","I want to describe a data transformation in plain English and get a SQL or Python implementation","I need to convert a business process description into a workflow automation script"],"best_for":["non-technical stakeholders who need to specify requirements in natural language","rapid prototyping scenarios where speed is prioritized over optimization","teams documenting requirements and wanting to generate code from documentation"],"limitations":["Ambiguous natural language descriptions may result in incorrect code; requires clear specifications","Generated code may not match project conventions or architectural patterns without additional context","Complex business logic may be difficult to express in natural language; requires iterative refinement","No guarantee of correctness; generated code requires testing and validation"],"requires":["API key for Google Gemini or OpenRouter","Clear natural language descriptions of requirements","Testing infrastructure to validate generated code"],"input_types":["text (natural language specifications, requirements documents)"],"output_types":["code (implementations in specified languages)","text (clarifying questions, assumptions)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_8","uri":"capability://code.generation.editing.cross.language.code.translation.and.porting","name":"cross-language code translation and porting","description":"Translates code from one programming language to another while maintaining functional equivalence, handling language-specific idioms, and adapting to target language conventions. The model understands semantic equivalence across languages and generates idiomatic code in the target language rather than direct syntactic translation.","intents":["I need to port a critical algorithm from C++ to Python while maintaining performance characteristics","I want to migrate a Node.js backend to Go for better concurrency handling","I need to convert a Java library to TypeScript for use in a web application"],"best_for":["teams migrating between technology stacks","developers porting algorithms across languages","organizations consolidating codebases in different languages"],"limitations":["Direct translation may not preserve performance characteristics; optimization may be required","Language-specific features (e.g., macros in C++) may not have direct equivalents","Generated code requires testing to ensure functional equivalence","Complex codebases with language-specific optimizations may require manual intervention"],"requires":["API key for Google Gemini or OpenRouter","Source code in supported language","Target language specification","Testing infrastructure to validate ported code"],"input_types":["code (source code in one language)"],"output_types":["code (translated code in target language)","text (notes on translation decisions and potential issues)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-pro-preview__cap_9","uri":"capability://safety.moderation.security.vulnerability.analysis.and.remediation.suggestions","name":"security vulnerability analysis and remediation suggestions","description":"Analyzes code for security vulnerabilities including injection attacks, authentication flaws, cryptographic weaknesses, and data exposure risks, then suggests specific remediation strategies. The model applies knowledge of OWASP Top 10, CWE categories, and language-specific security best practices to identify risks and recommend fixes.","intents":["I need to audit a codebase for security vulnerabilities before deploying to production","I want to understand why a specific code pattern is vulnerable and how to fix it","I need to generate security-hardened versions of existing code"],"best_for":["security teams conducting code reviews","developers building security-critical applications","organizations meeting compliance requirements (HIPAA, PCI-DSS, SOC 2)"],"limitations":["Analysis is based on static code patterns; runtime vulnerabilities may not be detected","False positives are possible; requires human validation of findings","Complex security issues may require domain expertise beyond the model's capabilities","No guarantee of comprehensive coverage; should be used alongside dedicated security tools"],"requires":["API key for Google Gemini or OpenRouter","Source code to analyze","Security context (compliance requirements, threat model)"],"input_types":["code (source code to analyze)","text (security requirements, threat model)"],"output_types":["text (vulnerability descriptions, risk assessments)","code (remediation suggestions)","structured data (vulnerability reports in JSON format)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["API key for Google Gemini or OpenRouter access","Supported input formats: JPEG, PNG, GIF, WebP for images; MP3, WAV, FLAC for audio; MP4, WebM for video","Network connectivity for API calls","Minimum context length of 32K tokens recommended for complex multi-modal tasks","API key for Google Gemini or OpenRouter","Tool/function definitions in JSON schema format","External orchestration framework (LangChain, LlamaIndex, custom implementation)","State management system for multi-turn agent interactions","Source code or API specification","Clear endpoint definitions and parameter documentation"],"failure_modes":["Audio and video inputs require preprocessing into compatible formats; raw video files may need transcoding","Context window constraints limit the total amount of multimodal data processable in a single request","Image understanding quality varies by resolution and complexity; OCR-heavy tasks may require supplementary text input","No real-time streaming of video/audio — batch processing only","Agent reliability improves with clear tool definitions but still requires explicit error handling in the orchestration layer","No built-in persistence of agent state — requires external state management for long-running tasks","Tool hallucination can still occur; requires validation of generated tool calls before execution","Complex branching logic in agents may require explicit prompt engineering to guide decision-making","Generated documentation requires review for accuracy and completeness","Complex API behaviors may not be fully captured in specifications","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.33,"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=google-gemini-3.1-pro-preview","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-3.1-pro-preview"}},"signature":"IAV5dxSndohhJeIYiUho36sZwUBbbkE+6ErfNpIXZ7oJJSba4nN011Q6wT4w0R1XqS9fqSueL/57pdTzd3L/Cg==","signedAt":"2026-06-21T14:35:25.142Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-3.1-pro-preview","artifact":"https://unfragile.ai/google-gemini-3.1-pro-preview","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-3.1-pro-preview","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"}}