{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gemini-2-5-pro","slug":"gemini-2-5-pro","name":"Gemini 2.5 Pro","type":"model","url":"https://deepmind.google/technologies/gemini/pro/","page_url":"https://unfragile.ai/gemini-2-5-pro","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gemini-2-5-pro__cap_0","uri":"capability://memory.knowledge.extended.context.reasoning.with.1m.token.window","name":"extended context reasoning with 1m token window","description":"Processes up to 1 million tokens in a single request, enabling analysis of entire codebases, long-form documents, video transcripts, and multi-file projects without context truncation. Implements a transformer-based architecture optimized for long-sequence attention patterns, allowing developers to maintain full project context across complex reasoning tasks without splitting work into multiple API calls or managing manual context windows.","intents":["Analyze an entire codebase for refactoring opportunities without losing context","Process full research papers or documentation for comprehensive summarization","Maintain conversation history across 50+ turn interactions without information loss","Extract insights from hour-long video transcripts in a single request"],"best_for":["Enterprise teams analyzing large codebases (100k+ lines)","Researchers processing long-form academic content","Developers building multi-file code generation agents","Teams requiring conversation continuity across extended sessions"],"limitations":["1M token limit still finite — projects exceeding this require chunking strategies","Latency increases with context size; exact scaling characteristics not disclosed","Token counting for multimodal inputs (video, audio) not publicly specified","No local caching of context across separate API calls — each request re-processes full context"],"requires":["API key for Google AI Studio or Gemini API","Network connectivity for cloud API calls","Token budget sufficient for 1M-token requests (pricing model not disclosed in artifact)"],"input_types":["text","code","image","video","audio"],"output_types":["text","code","structured JSON"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_1","uri":"capability://planning.reasoning.native.chain.of.thought.reasoning.with.extended.thinking","name":"native chain-of-thought reasoning with extended thinking","description":"Implements built-in extended thinking capabilities that decompose complex problems into step-by-step reasoning chains before generating final answers. The model internally explores multiple solution paths, backtracks when needed, and validates reasoning before output, mimicking human problem-solving without requiring explicit prompt engineering for chain-of-thought patterns. This is a native architectural feature rather than a prompt-based technique.","intents":["Solve multi-step mathematical proofs with verified intermediate steps","Debug complex code by reasoning through execution flow before suggesting fixes","Evaluate competitive programming problems with exhaustive solution exploration","Generate scientifically rigorous explanations for GPQA-level questions"],"best_for":["Competitive programmers solving algorithmic challenges","Researchers requiring rigorous scientific reasoning","Teams building AI agents for complex problem-solving","Educators creating detailed explanations for technical content"],"limitations":["Extended thinking increases latency — exact overhead not disclosed","Reasoning process not exposed to user; only final answer returned","Cannot selectively disable thinking for simple queries to optimize cost/speed","Benchmark improvements (e.g., GPQA 94.3%) suggest thinking is always active, adding latency to all requests"],"requires":["API access to Gemini 2.5/3.1 Pro (thinking feature may not be available on all model variants)","Tolerance for increased response latency vs. standard models","Understanding that thinking is opaque — cannot inspect reasoning steps"],"input_types":["text","code","mathematical expressions","scientific questions"],"output_types":["text","code","structured explanations"],"categories":["planning-reasoning","chain-of-thought"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_10","uri":"capability://code.generation.editing.interactive.application.development.with.visualization","name":"interactive application development with visualization","description":"Generates code for interactive applications including data visualizations, 3D simulations, and terrain generation. The model understands visualization libraries (matplotlib, plotly, Three.js, etc.) and can generate complete, runnable applications that produce visual output. Combined with code execution capability, enables rapid prototyping of interactive tools.","intents":["Generate interactive dashboards and data visualizations","Create 3D simulations or terrain generators","Build interactive educational tools or demos","Prototype data exploration interfaces"],"best_for":["Data scientists building interactive dashboards","Educators creating interactive learning tools","Game developers prototyping 3D visualizations","Teams rapidly prototyping data exploration interfaces"],"limitations":["Visualization libraries supported not fully specified","Output capture for binary formats (images, 3D models) may require workarounds","No support for interactive UI frameworks (React, Vue) — limited to data visualization libraries","Real-time interactivity limited by API request/response cycle","Large visualizations may exceed token limits or execution timeouts"],"requires":["API key for Gemini 2.5/3.1 Pro with code execution","Understanding of visualization libraries (matplotlib, plotly, etc.)","Tolerance for generated code quality (may require manual refinement)"],"input_types":["data descriptions","visualization requirements","code snippets"],"output_types":["visualization code","rendered visualizations","interactive applications"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_11","uri":"capability://text.generation.language.cross.lingual.understanding.and.translation","name":"cross-lingual understanding and translation","description":"Understands and processes text in multiple languages with deep semantic understanding, not just surface-level translation. The model can reason about content in non-English languages, translate while preserving nuance and context, and handle code-switching (mixing languages). Supports both explicit translation requests and implicit multilingual reasoning.","intents":["Translate technical documentation while preserving accuracy","Analyze content in non-English languages with full semantic understanding","Build multilingual AI applications without language-specific models","Handle code-switching in multilingual conversations"],"best_for":["Global teams working with multilingual content","Developers building applications for non-English markets","Researchers analyzing content in multiple languages","Content creators translating technical material"],"limitations":["Language support not fully specified — some languages may have lower quality","Translation quality varies by language pair and domain","No explicit control over translation style (formal vs. casual, technical vs. general)","Idioms and cultural references may not translate accurately","No specialized terminology dictionaries — technical translations may require manual review"],"requires":["API key for Gemini 2.5/3.1 Pro","Content in supported language","Verification of translations against native speakers (especially for critical content)"],"input_types":["text in any language","code with comments in any language","multilingual content"],"output_types":["translated text","multilingual analysis","code with translated comments"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_12","uri":"capability://tool.use.integration.enterprise.grade.api.with.production.deployment","name":"enterprise-grade api with production deployment","description":"Provides production-ready API infrastructure through Google AI Studio and Gemini API with enterprise features including rate limiting, authentication, monitoring, and SLA support. Designed for integration into production applications with reliability guarantees and support for high-volume usage. Includes deployment guidance and integration patterns for enterprise environments.","intents":["Deploy AI capabilities into production applications with SLA guarantees","Build enterprise AI applications with authentication and access control","Monitor and debug AI model behavior in production","Scale AI features to handle high-volume user traffic"],"best_for":["Enterprise teams deploying AI to production","SaaS companies embedding AI into products","Teams requiring audit logs and compliance features","Organizations needing dedicated support"],"limitations":["Pricing model not disclosed in artifact — cost structure unknown","Rate limits and quota management not specified","SLA terms not detailed in provided documentation","No local deployment option — cloud-only architecture","Vendor lock-in risk — migrating to alternative models requires code changes","Data privacy terms not disclosed — unclear how user data is handled"],"requires":["Google Cloud account or Google AI Studio account","API key management and secure storage","Network connectivity for API calls","Understanding of API rate limits and quota management"],"input_types":["API requests","authentication credentials"],"output_types":["API responses","monitoring data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_13","uri":"capability://tool.use.integration.enterprise.api.access.with.rate.limiting.and.quota.management","name":"enterprise-api-access-with-rate-limiting-and-quota-management","description":"Gemini 2.5 Pro is available through the Gemini API with enterprise-grade access controls, rate limiting, quota management, and billing integration. Developers can manage API keys, set usage limits, monitor consumption, and integrate the model into production systems with reliability guarantees and support.","intents":["I need to integrate Gemini into a production application with proper access controls","I want to monitor API usage and control costs through quota management","I need reliable API access with SLA guarantees for my enterprise application","I'm building a multi-tenant system and need per-user quota management"],"best_for":["enterprise applications requiring production-grade API access","teams building multi-tenant systems","organizations with strict cost control requirements","applications requiring high reliability and support"],"limitations":["Pricing structure is not documented in provided materials","Rate limits and quota management details are not publicly specified","SLA guarantees and support tiers are not documented","No information about enterprise contracts or volume discounts"],"requires":["Google Cloud account or Google AI Studio account","API key for authentication","Client SDK in supported language (Python, JavaScript, etc.)","Network access to Gemini API endpoints"],"input_types":["API requests with authentication","quota and rate limit configurations"],"output_types":["API responses","usage metrics and billing data","quota status and limits"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_14","uri":"capability://text.generation.language.google.ai.studio.web.interface.for.rapid.experimentation","name":"google-ai-studio-web-interface-for-rapid-experimentation","description":"Gemini 2.5 Pro is accessible through Google AI Studio, a web-based development environment where users can experiment with the model, test prompts, adjust parameters, and prototype applications without writing code. The interface provides prompt templates, example management, and direct API integration for quick iteration.","intents":["I want to experiment with Gemini without setting up a development environment","I need to quickly test different prompts and see results in real-time","I want to prototype an application idea before building it programmatically","I need to share prompts and results with team members for feedback"],"best_for":["non-technical users experimenting with AI","teams prototyping ideas quickly","educators demonstrating model capabilities","developers iterating on prompts before implementation"],"limitations":["Limited to web browser interface — no offline access","No persistent project management or version control","Limited customization compared to programmatic API access","Sharing and collaboration features are not documented"],"requires":["Web browser with internet access","Google account","No coding or technical setup required"],"input_types":["text prompts","images (for multimodal experiments)","parameter adjustments"],"output_types":["model responses","generated code snippets","API integration examples"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_2","uri":"capability://image.visual.multimodal.understanding.across.text.image.video.and.audio","name":"multimodal understanding across text, image, video, and audio","description":"Processes and reasons over mixed-media inputs including text, images, video frames, and audio transcripts in a single request. The model uses a unified embedding space that allows cross-modal reasoning — for example, analyzing code alongside screenshots, or correlating audio narration with video content. Supports direct video/audio upload without requiring pre-transcription or frame extraction.","intents":["Analyze UI screenshots alongside code to suggest accessibility improvements","Extract insights from hour-long video tutorials by processing video + audio together","Debug visual bugs by examining error screenshots and corresponding log files","Generate descriptions of complex diagrams or technical illustrations"],"best_for":["Teams building AI-powered code review tools with visual context","Content creators automating video analysis and summarization","QA engineers automating visual regression testing","Accessibility teams auditing UI/code combinations"],"limitations":["Video/audio processing latency not disclosed; likely higher than text-only requests","Maximum video length, resolution, and audio duration not specified","No explicit support for streaming video — requires complete upload","Token counting for multimodal inputs unclear — video/audio may consume disproportionate tokens relative to text","No fine-grained control over which modalities are processed (e.g., cannot disable audio analysis for video-only tasks)"],"requires":["API key for Gemini API or Google AI Studio","Video/audio files in supported formats (specific formats not disclosed)","Sufficient API quota for multimodal requests (pricing likely higher than text-only)"],"input_types":["text","image","video","audio"],"output_types":["text","code","structured analysis"],"categories":["image-visual","multimodal-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_3","uri":"capability://code.generation.editing.code.generation.and.execution.with.real.time.feedback","name":"code generation and execution with real-time feedback","description":"Generates executable code across multiple languages and can execute generated code in a sandboxed environment, returning results directly in the conversation. The model understands code semantics deeply enough to generate syntactically correct, runnable code on first attempt for most tasks. Execution feedback loops enable iterative refinement — the model can see execution errors and self-correct without user intervention.","intents":["Generate and run data analysis scripts without leaving the chat interface","Create and test visualization code (matplotlib, plotly) with immediate visual output","Debug code by generating test cases and executing them to validate fixes","Prototype algorithms with immediate execution feedback"],"best_for":["Data scientists prototyping analysis workflows interactively","Educators teaching programming with immediate execution feedback","Teams building AI-assisted development tools","Developers rapidly iterating on algorithms"],"limitations":["Sandboxed execution environment — cannot access external APIs, databases, or file systems without explicit integration","Execution timeout and resource limits not disclosed","No persistent state between code blocks — each execution is isolated","Language support not fully specified (Python likely supported; others unknown)","Output capture limited to stdout/stderr; binary outputs (images, files) may require workarounds"],"requires":["API access to Gemini 2.5/3.1 Pro with code execution enabled","Understanding that execution is sandboxed and isolated from user's local environment","Code must be self-contained or use only built-in libraries (external dependencies unclear)"],"input_types":["text prompts","code snippets","data descriptions"],"output_types":["executable code","execution results","error messages with suggestions"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_4","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"Generates outputs conforming to user-specified JSON schemas or structured formats, with built-in validation ensuring outputs match the schema before returning. The model understands schema constraints and generates valid structured data on first attempt for most cases. Supports complex nested schemas, enums, and type constraints without requiring post-processing or validation logic.","intents":["Extract structured data from unstructured text (e.g., parse customer feedback into predefined fields)","Generate API responses conforming to OpenAPI schemas","Create configuration files in JSON/YAML with guaranteed valid structure","Build data pipelines that require strict output formatting"],"best_for":["Teams building data extraction pipelines with strict format requirements","API developers generating responses that must conform to OpenAPI specs","Data engineers automating ETL with schema validation","LLM application builders requiring deterministic output formats"],"limitations":["Schema complexity limits not disclosed — very large schemas may fail","No streaming support for structured outputs (full response must be generated before validation)","Validation errors not exposed to user — invalid outputs are retried internally with unknown retry limits","Schema must be provided by user; no automatic schema inference from examples","Performance impact of schema validation not disclosed"],"requires":["API key for Gemini API","JSON schema definition for desired output format","Understanding of JSON Schema specification"],"input_types":["text","unstructured data","schema definitions"],"output_types":["JSON","structured data conforming to schema"],"categories":["data-processing-analysis","structured-output"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_5","uri":"capability://search.retrieval.google.search.grounding.with.real.time.information","name":"google search grounding with real-time information","description":"Integrates live Google Search results into model reasoning, allowing the model to ground responses in current information rather than relying solely on training data. When enabled, the model queries Google Search for relevant information, incorporates results into context, and cites sources. This enables accurate responses to time-sensitive queries (current events, recent research, live data) without requiring manual search integration.","intents":["Answer questions about current events or recent news without hallucinating","Provide up-to-date pricing, availability, or product information","Cite recent research papers or academic findings with proper attribution","Build AI agents that need access to real-time information"],"best_for":["Teams building customer-facing AI assistants requiring current information","Researchers needing access to recent publications","News/media applications requiring factual accuracy","Enterprise agents that must ground decisions in live data"],"limitations":["Search grounding adds latency — exact overhead not disclosed","Search results quality depends on Google Search quality; no control over result ranking or filtering","No explicit control over search scope (e.g., cannot limit to academic sources or specific domains)","Citation format and accuracy not guaranteed — model may misattribute sources","Search grounding may increase token consumption (search results added to context)","No offline mode — requires internet connectivity and Google Search API availability"],"requires":["API key for Gemini API with Search grounding enabled","Network connectivity for Google Search queries","Acceptance that responses may include external sources with varying reliability"],"input_types":["text queries","questions requiring current information"],"output_types":["text with citations","grounded responses with source attribution"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_6","uri":"capability://code.generation.editing.competitive.programming.and.algorithmic.problem.solving","name":"competitive programming and algorithmic problem-solving","description":"Specialized reasoning capability for solving competitive programming problems, including algorithm design, complexity analysis, and code generation for problems from platforms like LeetCode, Codeforces, and ICPC. The model understands algorithmic patterns, can identify optimal approaches, and generates correct solutions with proper time/space complexity. Achieves top benchmark scores on competitive programming tasks through combination of extended thinking and deep algorithmic knowledge.","intents":["Solve LeetCode/Codeforces problems with optimal algorithms","Explain algorithmic approaches and complexity tradeoffs","Generate correct code for competitive programming contests","Debug algorithmic solutions and suggest optimizations"],"best_for":["Competitive programmers preparing for contests","Coding interview candidates studying algorithms","Computer science educators teaching algorithm design","Teams building AI-powered coding interview platforms"],"limitations":["Benchmark scores not disclosed for competitive programming specifically (artifact claims 'top scores' but provides no data)","Performance on novel/unseen problem types unknown","Cannot access live contest platforms or problem databases","No real-time feedback on solution correctness against test cases (requires manual testing)","Extended thinking overhead may make real-time contest use impractical"],"requires":["API key for Gemini 2.5/3.1 Pro","Problem statement in text form","Understanding that model may not solve all problems correctly on first attempt"],"input_types":["problem statements","code snippets","algorithm descriptions"],"output_types":["code solutions","algorithm explanations","complexity analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_7","uri":"capability://text.generation.language.scientific.knowledge.and.reasoning.gpqa.level","name":"scientific knowledge and reasoning (gpqa-level)","description":"Demonstrates expert-level scientific knowledge and reasoning across physics, chemistry, biology, and other domains, achieving 94.3% accuracy on GPQA Diamond (a benchmark of graduate-level science questions). The model combines deep factual knowledge with rigorous reasoning to answer questions that require understanding of complex scientific concepts, experimental design, and domain-specific terminology.","intents":["Answer graduate-level science questions with detailed explanations","Explain complex scientific concepts to non-experts","Validate scientific reasoning in research proposals or papers","Generate scientifically accurate educational content"],"best_for":["Researchers and scientists seeking AI-assisted literature review","Educators creating science curriculum and explanations","Science communicators translating complex concepts for general audiences","Teams building scientific knowledge bases or tutoring systems"],"limitations":["Knowledge cutoff date not disclosed — may lack very recent research","GPQA Diamond benchmark (94.3%) is multiple-choice; open-ended scientific questions may have lower accuracy","No access to scientific databases, journals, or papers beyond training data","Cannot perform actual experiments or access real-time scientific data","Reasoning is opaque — cannot inspect how model arrived at scientific conclusions"],"requires":["API key for Gemini 2.5/3.1 Pro","Questions phrased clearly with necessary context","Verification of answers against authoritative sources (model is not infallible)"],"input_types":["text questions","scientific concepts","research descriptions"],"output_types":["text explanations","scientific reasoning","educational content"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_8","uri":"capability://planning.reasoning.abstract.reasoning.and.pattern.recognition.arc.agi","name":"abstract reasoning and pattern recognition (arc-agi)","description":"Solves abstract reasoning puzzles that require identifying patterns, generalizing rules, and applying them to novel situations without explicit instructions. Achieves 77.1% on ARC-AGI-2 benchmark, demonstrating ability to reason about visual patterns, logical sequences, and abstract concepts. This capability goes beyond pattern matching to genuine reasoning about underlying rules.","intents":["Solve visual pattern recognition puzzles","Identify hidden rules in sequences or datasets","Generalize from examples to novel situations","Reason about abstract concepts without explicit instruction"],"best_for":["AI researchers studying reasoning and generalization","Teams building AI systems for puzzle-solving or game AI","Educators assessing reasoning capabilities","Cognitive science researchers studying machine reasoning"],"limitations":["ARC-AGI-2 benchmark (77.1%) is still below human performance (estimated 85%+)","Performance on novel puzzle types not tested","Reasoning process is opaque — cannot inspect how patterns are identified","No interactive feedback loop for iterative puzzle-solving","Benchmark is synthetic; real-world abstract reasoning tasks may differ significantly"],"requires":["API key for Gemini 2.5/3.1 Pro","Puzzle or pattern description in text or image form","Understanding that model may fail on novel or ambiguous patterns"],"input_types":["text descriptions","images","pattern sequences"],"output_types":["text reasoning","pattern explanations","predictions"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__cap_9","uri":"capability://planning.reasoning.agentic.task.decomposition.and.multi.step.execution","name":"agentic task decomposition and multi-step execution","description":"Breaks down complex, multi-step tasks into executable subtasks and orchestrates their execution. The model can plan task sequences, identify dependencies, handle failures, and adapt plans based on intermediate results. This enables building autonomous agents that can accomplish goals requiring multiple reasoning steps, tool calls, and decision points without human intervention between steps.","intents":["Build AI agents that autonomously complete multi-step workflows","Decompose complex projects into executable tasks with dependencies","Create agents that adapt plans based on intermediate results","Orchestrate tool use across multiple steps toward a goal"],"best_for":["Teams building autonomous AI agents for business processes","Developers creating multi-step automation workflows","Enterprises automating complex decision-making processes","Researchers studying agent architectures and planning"],"limitations":["No built-in persistence — agent state must be managed externally","Task decomposition quality depends on prompt clarity; ambiguous goals may lead to poor plans","No explicit rollback or transaction support for failed subtasks","Latency compounds with each step — multi-step tasks may be slow","No built-in monitoring or observability for agent execution","Tool integration requires manual implementation (no built-in tool registry)"],"requires":["API key for Gemini 2.5/3.1 Pro","External state management for agent persistence","Tool/function definitions for agent to call","Clear goal specification and success criteria"],"input_types":["text goals","task descriptions","tool definitions"],"output_types":["task plans","execution results","intermediate decisions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemini-2-5-pro__headline","uri":"capability://memory.knowledge.multimodal.ai.model.for.complex.reasoning.and.coding.tasks","name":"multimodal ai model for complex reasoning and coding tasks","description":"Gemini 2.5 Pro is a state-of-the-art multimodal AI model designed for complex reasoning, coding, and mathematics, with a 1M token context window and native thinking capabilities, making it ideal for enterprise applications.","intents":["best multimodal AI model","multimodal AI for coding tasks","AI model for complex reasoning","top AI model for enterprise applications","best AI model for mathematics"],"best_for":["enterprise applications","coding tasks","complex reasoning"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["API key for Google AI Studio or Gemini API","Network connectivity for cloud API calls","Token budget sufficient for 1M-token requests (pricing model not disclosed in artifact)","API access to Gemini 2.5/3.1 Pro (thinking feature may not be available on all model variants)","Tolerance for increased response latency vs. standard models","Understanding that thinking is opaque — cannot inspect reasoning steps","API key for Gemini 2.5/3.1 Pro with code execution","Understanding of visualization libraries (matplotlib, plotly, etc.)","Tolerance for generated code quality (may require manual refinement)","API key for Gemini 2.5/3.1 Pro"],"failure_modes":["1M token limit still finite — projects exceeding this require chunking strategies","Latency increases with context size; exact scaling characteristics not disclosed","Token counting for multimodal inputs (video, audio) not publicly specified","No local caching of context across separate API calls — each request re-processes full context","Extended thinking increases latency — exact overhead not disclosed","Reasoning process not exposed to user; only final answer returned","Cannot selectively disable thinking for simple queries to optimize cost/speed","Benchmark improvements (e.g., GPQA 94.3%) suggest thinking is always active, adding latency to all requests","Visualization libraries supported not fully specified","Output capture for binary formats (images, 3D models) may require workarounds","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":null,"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=gemini-2-5-pro","compare_url":"https://unfragile.ai/compare?artifact=gemini-2-5-pro"}},"signature":"0d1qNjFX3lygjozhFf83ZKQHSRTHrY+qeKGhDTyKefl3cxqgBv1IBwWPW3DJBm+vzRbW/b+gXUC74VYlhz3wDg==","signedAt":"2026-06-21T00:04:20.370Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gemini-2-5-pro","artifact":"https://unfragile.ai/gemini-2-5-pro","verify":"https://unfragile.ai/api/v1/verify?slug=gemini-2-5-pro","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"}}