{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemini-2.5-pro-preview-05-06","slug":"google-gemini-2.5-pro-preview-05-06","name":"Google: Gemini 2.5 Pro Preview 05-06","type":"model","url":"https://openrouter.ai/models/google~gemini-2.5-pro-preview-05-06","page_url":"https://unfragile.ai/google-gemini-2.5-pro-preview-05-06","categories":["model-training"],"tags":["google","api-access","text","image","audio","video"],"pricing":{"model":"paid","free":false,"starting_price":"$1.25e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_0","uri":"capability://planning.reasoning.extended.reasoning.with.internal.thinking","name":"extended-reasoning-with-internal-thinking","description":"Implements an internal 'thinking' mechanism that allows the model to reason through complex problems before generating responses, similar to chain-of-thought but internalized within the model's inference process. The model allocates computational budget to explore multiple reasoning paths and verify logical consistency before committing to an output, improving accuracy on tasks requiring multi-step deduction, mathematical proof, or scientific analysis.","intents":["I need the model to work through a complex math problem step-by-step and show me its reasoning","I want more accurate answers on logic puzzles and constraint satisfaction problems","I need the model to catch its own errors before responding to scientific questions"],"best_for":["researchers and scientists requiring high-accuracy reasoning on novel problems","educators building tutoring systems that need to explain reasoning","developers building agents that must solve multi-constraint optimization problems"],"limitations":["Thinking process is not exposed to the user — only final response is returned, limiting transparency into reasoning paths","Increased latency due to extended inference time for reasoning computation","Thinking budget allocation is opaque — no control over how much computation is spent on reasoning vs. generation","May not improve performance on tasks that don't benefit from deep reasoning (e.g., simple factual retrieval)"],"requires":["API access to Gemini 2.5 Pro Preview via OpenRouter or Google AI Studio","Network connectivity for real-time inference","Sufficient API quota/credits for extended inference costs"],"input_types":["text prompts","natural language questions","problem statements with constraints"],"output_types":["text responses","structured explanations","mathematical proofs or derivations"],"categories":["planning-reasoning","advanced-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_1","uri":"capability://code.generation.editing.multimodal.code.generation.and.analysis","name":"multimodal-code-generation-and-analysis","description":"Generates, debugs, and analyzes code across 40+ programming languages with support for multimodal context including images, text, and code snippets. The model understands code structure through semantic analysis rather than pattern matching, enabling it to refactor across file boundaries, suggest architectural improvements, and generate code that integrates with existing codebases when provided as context.","intents":["I need to generate boilerplate code for a new microservice given a system architecture diagram","I want to refactor legacy code and understand the architectural implications of changes","I need to debug code by showing the model error logs, stack traces, and relevant code files together"],"best_for":["full-stack developers building complex applications with multiple languages","teams migrating or refactoring large codebases","developers working with unfamiliar frameworks or languages"],"limitations":["Context window limits the amount of code that can be analyzed in a single request (200K tokens for Gemini 2.5 Pro)","No persistent codebase indexing — each request requires re-providing relevant code context","Generated code may not account for subtle framework-specific patterns or performance optimizations","Image-based code analysis (e.g., from screenshots) may have lower accuracy than text-based code"],"requires":["API access to Gemini 2.5 Pro via OpenRouter or Google AI Studio","Code files or snippets as text input, or images of code/architecture diagrams","Knowledge of target programming language syntax for validation"],"input_types":["source code (text)","code snippets","architecture diagrams (images)","error messages and stack traces","natural language specifications"],"output_types":["generated source code","refactored code with explanations","bug fixes and patches","architectural recommendations"],"categories":["code-generation-editing","multimodal-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_10","uri":"capability://tool.use.integration.function.calling.with.structured.tool.integration","name":"function-calling-with-structured-tool-integration","description":"Supports function calling and tool use through a structured schema-based interface, allowing the model to invoke external APIs, functions, or tools as part of its reasoning process. The model can determine when to call tools, format requests according to tool schemas, and integrate tool responses back into its reasoning to generate final answers.","intents":["I need the model to call a weather API to answer questions about current weather conditions","I want the model to use a calculator tool for precise mathematical computations","I need to build an agent that can call multiple APIs to gather information and synthesize answers"],"best_for":["developers building AI agents that need to interact with external systems","teams building chatbots that need access to real-time data or business systems","builders creating autonomous workflows that combine reasoning with tool use"],"limitations":["Tool schemas must be provided by the developer — model cannot discover available tools automatically","No built-in error handling for tool failures — developer must handle and retry failed tool calls","Tool calling adds latency due to additional inference steps for tool selection and formatting","No persistent tool state — each request must re-specify available tools"],"requires":["API access to Gemini 2.5 Pro","Tool schemas defined in OpenAPI or JSON Schema format","Implementation of tool execution layer (developer must handle actual tool invocation)"],"input_types":["natural language requests","tool schema definitions","tool responses and results"],"output_types":["tool calls with formatted parameters","final answers synthesizing tool results","structured tool invocation sequences"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_11","uri":"capability://text.generation.language.context.aware.conversation.with.memory.management","name":"context-aware-conversation-with-memory-management","description":"Maintains conversation context across multiple turns, tracking user intent, previous statements, and evolving context to provide coherent and contextually appropriate responses. The model can reference earlier parts of conversations, understand pronouns and references, and adapt its responses based on conversation history without explicit memory management by the developer.","intents":["I need to build a chatbot that remembers previous questions and provides consistent answers","I want the model to understand follow-up questions that reference earlier parts of the conversation","I need to maintain context across a long conversation without manually managing conversation state"],"best_for":["developers building conversational AI and chatbots","teams creating customer support systems with multi-turn interactions","builders of interactive tutoring or coaching systems"],"limitations":["Context window limits conversation length — very long conversations may exceed token limits","No persistent memory between sessions — conversation history must be provided in each request","Model may lose track of context in very long conversations (100+ turns)","No explicit memory management — developer must handle conversation history storage and retrieval"],"requires":["API access to Gemini 2.5 Pro","Conversation history provided as input (previous messages and responses)","Message format following OpenAI-compatible chat format"],"input_types":["user messages","conversation history","system prompts and instructions"],"output_types":["contextually appropriate responses","follow-up questions and clarifications","conversation summaries"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_2","uri":"capability://planning.reasoning.mathematical.problem.solving.with.symbolic.reasoning","name":"mathematical-problem-solving-with-symbolic-reasoning","description":"Solves mathematical problems ranging from algebra to calculus and discrete mathematics by combining symbolic reasoning with numerical computation. The model can manipulate equations algebraically, verify solutions, and explain derivation steps, leveraging its extended reasoning capability to explore multiple solution approaches and validate correctness before responding.","intents":["I need to solve a system of differential equations and understand the solution method","I want to verify that my mathematical proof is correct and identify logical gaps","I need to generate practice problems for a calculus course with detailed solutions"],"best_for":["mathematics educators and tutoring platform builders","researchers and engineers solving applied mathematics problems","students learning advanced mathematics who need step-by-step explanations"],"limitations":["May struggle with extremely large symbolic expressions or high-dimensional optimization problems","Symbolic reasoning is limited to mathematical domains — cannot perform symbolic reasoning on non-mathematical domains","No integration with computer algebra systems (CAS) like Mathematica or SymPy — all reasoning is within the model","Numerical precision is limited by floating-point representation; not suitable for arbitrary-precision arithmetic"],"requires":["API access to Gemini 2.5 Pro","Mathematical notation in text form (LaTeX, plain text, or ASCII math)","Understanding of mathematical concepts to validate model outputs"],"input_types":["mathematical equations and expressions","problem statements with constraints","proof sketches for verification"],"output_types":["solved equations with steps","mathematical proofs","numerical answers with explanations","alternative solution methods"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_3","uri":"capability://data.processing.analysis.scientific.document.analysis.and.synthesis","name":"scientific-document-analysis-and-synthesis","description":"Analyzes scientific papers, research documents, and technical literature by extracting key findings, methodology, and implications, then synthesizes information across multiple documents to identify patterns, contradictions, and research gaps. The model processes both text and images (figures, tables, diagrams) from scientific documents and can reason about experimental design and statistical validity.","intents":["I need to summarize the key findings from 10 research papers on a specific topic and identify consensus","I want to understand the methodology of a paper and evaluate whether the conclusions are justified","I need to extract data from tables and figures in scientific papers and convert them to structured formats"],"best_for":["researchers conducting literature reviews and meta-analyses","scientists evaluating experimental design and statistical rigor","knowledge workers building research databases or knowledge graphs from scientific literature"],"limitations":["Context window limits analysis to ~50-100 pages of scientific text per request","Cannot access paywalled or proprietary scientific databases — requires documents be provided as input","May misinterpret domain-specific terminology or novel methodologies not well-represented in training data","Figure and table extraction from images may have lower accuracy for complex multi-panel figures or small text"],"requires":["API access to Gemini 2.5 Pro","Scientific documents in text or image format (PDFs must be converted to images or text)","Domain knowledge to validate extracted information and evaluate model reasoning"],"input_types":["scientific papers (text or images)","research abstracts","figures and tables from papers","experimental data and results"],"output_types":["structured summaries with key findings","extracted data from tables and figures","methodology evaluations","synthesis across multiple papers","identified research gaps and contradictions"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_4","uri":"capability://image.visual.image.understanding.and.visual.reasoning","name":"image-understanding-and-visual-reasoning","description":"Analyzes images including photographs, diagrams, charts, screenshots, and visual documents to extract information, answer questions about visual content, and reason about spatial relationships and visual patterns. The model can read text from images (OCR), interpret charts and graphs, understand architectural and technical diagrams, and reason about visual composition and design.","intents":["I need to extract text and data from a screenshot of a spreadsheet or table","I want to understand what's happening in a photograph and answer specific questions about it","I need to analyze an architecture diagram and explain the system design"],"best_for":["developers building document processing or data extraction pipelines","teams analyzing visual content at scale (screenshots, diagrams, charts)","educators and content creators working with visual materials"],"limitations":["Image resolution and quality affect accuracy — low-resolution or heavily compressed images may produce poor results","Cannot process video directly — only static images (though can analyze individual frames)","OCR accuracy varies by font, language, and text size — not suitable for critical document processing without human review","Spatial reasoning is limited to 2D images — cannot reason about 3D structures from single images"],"requires":["API access to Gemini 2.5 Pro","Images in common formats (JPEG, PNG, WebP, GIF)","Images must be provided as base64-encoded data or URLs"],"input_types":["photographs and natural images","screenshots and UI captures","diagrams and technical drawings","charts and graphs","documents and scanned pages","artwork and design mockups"],"output_types":["text extracted from images (OCR)","descriptions and captions","answers to visual questions","structured data extracted from charts/tables","design and composition analysis"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_5","uri":"capability://data.processing.analysis.audio.transcription.and.understanding","name":"audio-transcription-and-understanding","description":"Transcribes audio content to text and extracts meaning from spoken language, including support for multiple languages, accents, and audio quality conditions. The model can identify speakers, extract key points from conversations, and understand context-dependent speech patterns, though the actual audio processing may be handled by a separate audio encoder component.","intents":["I need to transcribe a recorded meeting or interview and extract action items","I want to analyze a podcast episode and summarize the main topics discussed","I need to understand spoken instructions in multiple languages"],"best_for":["teams processing meeting recordings and generating summaries","content creators transcribing audio for accessibility or documentation","researchers analyzing spoken language data"],"limitations":["Audio must be provided as input — no real-time streaming transcription capability documented","Accuracy varies significantly with audio quality, background noise, and speaker clarity","No speaker diarization (identifying which speaker said what) — treats all speech as continuous","Language support depends on training data — may have lower accuracy for low-resource languages"],"requires":["API access to Gemini 2.5 Pro","Audio files in supported formats (exact formats not specified in artifact)","Audio must be provided as base64-encoded data or URLs"],"input_types":["audio files (MP3, WAV, OGG, FLAC, etc.)","recorded conversations and meetings","podcasts and spoken content"],"output_types":["transcribed text","extracted key points and summaries","identified topics and themes","structured meeting notes"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_6","uri":"capability://image.visual.video.frame.analysis.and.temporal.reasoning","name":"video-frame-analysis-and-temporal-reasoning","description":"Analyzes video content by processing individual frames and reasoning about temporal sequences, motion, and changes across frames. The model can understand what's happening in a video, identify key moments, track objects or people across frames, and reason about cause-and-effect relationships in video sequences, though frame extraction and preprocessing may be handled by external components.","intents":["I need to analyze a video and extract key moments or scenes","I want to understand what's happening in a video and answer specific questions about it","I need to track an object or person across a video and describe their actions"],"best_for":["video content creators and editors analyzing footage","security and surveillance teams reviewing video evidence","researchers analyzing behavioral or motion data from video"],"limitations":["Video must be provided as individual frames or short clips — no real-time video streaming","Temporal reasoning is limited to the frames provided — cannot reason about events outside the provided frames","Object tracking across frames requires sufficient visual consistency — may fail with occlusions or rapid motion","No audio-visual synchronization — audio and video must be analyzed separately"],"requires":["API access to Gemini 2.5 Pro","Video frames extracted as images or short video clips","Frames must be provided in sequence for temporal reasoning"],"input_types":["video frames (as images)","short video clips","surveillance footage","screen recordings"],"output_types":["frame-by-frame descriptions","identified key moments and scenes","object tracking and motion analysis","temporal event sequences","answers to video-specific questions"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.content","name":"structured-data-extraction-from-unstructured-content","description":"Extracts structured data (JSON, tables, key-value pairs) from unstructured text, images, and documents using semantic understanding of content. The model can identify entities, relationships, and attributes from natural language or visual content and format them according to specified schemas, handling variations in formatting and terminology.","intents":["I need to extract contact information from business cards or documents and convert to structured format","I want to parse natural language requirements and convert them to a structured specification","I need to extract product information from e-commerce listings and normalize it to a standard schema"],"best_for":["data engineering teams building ETL pipelines","teams automating document processing workflows","developers building knowledge extraction systems"],"limitations":["Extraction accuracy depends on content clarity and schema complexity — ambiguous content may produce inconsistent results","No validation against external data sources — cannot verify extracted data against databases or APIs","Schema must be provided by the user — model cannot infer optimal schemas automatically","Large-scale extraction may require batching due to context window limits"],"requires":["API access to Gemini 2.5 Pro","Unstructured content (text, images, or documents) as input","Target schema or format specification (JSON schema, table structure, etc.)"],"input_types":["unstructured text","documents and PDFs (as images or text)","web pages and HTML content","natural language descriptions"],"output_types":["JSON objects matching specified schema","CSV or table format","key-value pairs","structured entity lists"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_8","uri":"capability://memory.knowledge.long.context.reasoning.with.200k.token.window","name":"long-context-reasoning-with-200k-token-window","description":"Maintains and reasons over extended context windows of up to 200,000 tokens, enabling analysis of entire books, codebases, or document collections in a single request. The model can track information across long documents, identify patterns and relationships across distant parts of the context, and maintain coherent reasoning over extended sequences without losing track of earlier information.","intents":["I need to analyze an entire codebase (thousands of lines) and understand the architecture","I want to read a full research paper or book and answer detailed questions about it","I need to find inconsistencies or patterns across a large collection of documents"],"best_for":["developers analyzing large codebases without splitting into chunks","researchers and analysts working with long documents or document collections","teams building RAG systems that want to avoid chunking and retrieval complexity"],"limitations":["Latency increases significantly with context size — 200K token requests may take 30+ seconds","Cost scales linearly with context size — processing large contexts is expensive","Model may have reduced reasoning quality on very long contexts due to attention limitations","No persistent memory between requests — context must be re-provided for follow-up questions"],"requires":["API access to Gemini 2.5 Pro","Sufficient API quota and credits for extended inference costs","Content that can be represented as text (code, documents, transcripts, etc.)"],"input_types":["long text documents (books, papers, specifications)","source code files and entire codebases","meeting transcripts and conversation logs","document collections and corpora"],"output_types":["analysis and summaries of long content","answers to questions about specific parts of long documents","identified patterns and relationships across long contexts","structured extraction from long documents"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview-05-06__cap_9","uri":"capability://text.generation.language.multilingual.understanding.and.generation","name":"multilingual-understanding-and-generation","description":"Understands and generates text in 100+ languages with support for code-switching (mixing languages in a single response), translating between languages while preserving meaning and tone, and handling language-specific nuances like grammar, idioms, and cultural context. The model can reason about language-specific concepts and generate culturally appropriate responses.","intents":["I need to translate technical documentation from English to 10 different languages","I want to understand a customer support ticket in Spanish and generate a response in the same language","I need to analyze sentiment in social media posts across multiple languages"],"best_for":["global teams building multilingual applications and services","companies providing customer support in multiple languages","researchers and analysts working with multilingual data"],"limitations":["Translation quality varies by language pair — low-resource languages may have lower accuracy","Idioms and cultural context may not translate perfectly — human review recommended for critical content","Language detection may fail for code-switched text or rare languages","No support for constructed or fictional languages"],"requires":["API access to Gemini 2.5 Pro","Text in supported languages (100+ languages supported)","Language specification for generation tasks (optional — model can auto-detect)"],"input_types":["text in any supported language","code-switched text (mixing multiple languages)","documents and content in multiple languages"],"output_types":["translated text in target language","generated text in specified language","language-specific analysis and insights","multilingual summaries"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"low","permissions":["API access to Gemini 2.5 Pro Preview via OpenRouter or Google AI Studio","Network connectivity for real-time inference","Sufficient API quota/credits for extended inference costs","API access to Gemini 2.5 Pro via OpenRouter or Google AI Studio","Code files or snippets as text input, or images of code/architecture diagrams","Knowledge of target programming language syntax for validation","API access to Gemini 2.5 Pro","Tool schemas defined in OpenAPI or JSON Schema format","Implementation of tool execution layer (developer must handle actual tool invocation)","Conversation history provided as input (previous messages and responses)"],"failure_modes":["Thinking process is not exposed to the user — only final response is returned, limiting transparency into reasoning paths","Increased latency due to extended inference time for reasoning computation","Thinking budget allocation is opaque — no control over how much computation is spent on reasoning vs. generation","May not improve performance on tasks that don't benefit from deep reasoning (e.g., simple factual retrieval)","Context window limits the amount of code that can be analyzed in a single request (200K tokens for Gemini 2.5 Pro)","No persistent codebase indexing — each request requires re-providing relevant code context","Generated code may not account for subtle framework-specific patterns or performance optimizations","Image-based code analysis (e.g., from screenshots) may have lower accuracy than text-based code","Tool schemas must be provided by the developer — model cannot discover available tools automatically","No built-in error handling for tool failures — developer must handle and retry failed tool calls","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"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-2.5-pro-preview-05-06","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-2.5-pro-preview-05-06"}},"signature":"pRpxLIS3Slv05/ralwLSJVIh8V4CUp7FsaxqpSPj/vHoKS+TwHP0OXtqQj8rnNIQAnklcgvUYxEn0Xu6EF93CA==","signedAt":"2026-06-20T02:30:34.854Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-2.5-pro-preview-05-06","artifact":"https://unfragile.ai/google-gemini-2.5-pro-preview-05-06","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-2.5-pro-preview-05-06","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"}}