{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemini-2.5-pro-preview","slug":"google-gemini-2.5-pro-preview","name":"Google: Gemini 2.5 Pro Preview 06-05","type":"model","url":"https://openrouter.ai/models/google~gemini-2.5-pro-preview","page_url":"https://unfragile.ai/google-gemini-2.5-pro-preview","categories":["model-training"],"tags":["google","api-access","text","image","audio"],"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__cap_0","uri":"capability://planning.reasoning.extended.thinking.reasoning.with.step.by.step.problem.decomposition","name":"extended thinking reasoning with step-by-step problem decomposition","description":"Gemini 2.5 Pro implements an internal 'thinking' mode that performs multi-step reasoning before generating responses, similar to OpenAI's o1 architecture. The model allocates computational budget to explore solution paths, verify intermediate steps, and self-correct before committing to output. This is achieved through a separate reasoning token stream that is not exposed to the user but influences final response quality.","intents":["I need the model to work through complex math proofs step-by-step and show me only the verified final answer","I want accurate reasoning for scientific problems where intermediate steps matter for correctness","I need the model to catch its own logical errors before responding to coding architecture questions"],"best_for":["researchers and engineers solving complex mathematical or scientific problems","teams building AI systems that require high-confidence reasoning over accuracy-critical domains","developers debugging intricate algorithmic problems where correctness is non-negotiable"],"limitations":["Thinking mode increases latency by 5-15 seconds per request due to internal reasoning computation","Thinking tokens are not directly inspectable or controllable by the user — reasoning process is opaque","Extended thinking may not activate for simple queries, making behavior non-deterministic","Thinking budget is finite per request; extremely complex problems may timeout or produce incomplete reasoning"],"requires":["API access to Google's Gemini 2.5 Pro endpoint via OpenRouter or Google AI Studio","Network connectivity with 30+ second timeout tolerance for reasoning-heavy requests","Understanding that thinking mode is enabled by default for Pro tier, no explicit flag needed"],"input_types":["text prompts","code snippets for analysis","mathematical problem statements","scientific research questions"],"output_types":["text responses with reasoning-informed accuracy","code solutions with verified logic","mathematical proofs or derivations","structured explanations of complex concepts"],"categories":["planning-reasoning","advanced-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_1","uri":"capability://image.visual.multimodal.input.processing.with.image.audio.and.text.fusion","name":"multimodal input processing with image, audio, and text fusion","description":"Gemini 2.5 Pro accepts simultaneous inputs across text, image, and audio modalities in a single request, using a unified embedding space to fuse information across modalities. The model processes images via vision transformer components, audio via spectrogram analysis, and text via standard tokenization, then combines representations before the reasoning/generation stage. This enables cross-modal understanding where image context informs text generation and vice versa.","intents":["I need to upload a screenshot and ask questions about what's shown in it while providing text context","I want to transcribe and analyze an audio file while referencing related documents or images","I need to generate code based on a diagram image plus written specifications"],"best_for":["product teams building AI features that consume user-generated content (screenshots, voice, documents)","researchers analyzing multimodal datasets (medical imaging + patient notes, scientific papers + figures)","developers building accessibility tools that convert audio/images to structured outputs"],"limitations":["Image resolution is limited to ~4096x4096 pixels; higher resolutions are downsampled, losing fine detail","Audio input must be under 10 minutes; longer files require chunking or external preprocessing","No video input support — only static images and audio files","Cross-modal fusion quality degrades when modalities are semantically misaligned (e.g., image of unrelated content paired with text query)"],"requires":["API key for Google Gemini or OpenRouter access","Image files in JPEG, PNG, WebP, or GIF format","Audio files in MP3, WAV, or OGG format","Maximum file sizes: images 20MB, audio 100MB"],"input_types":["text (UTF-8 strings)","images (JPEG, PNG, WebP, GIF)","audio (MP3, WAV, OGG, FLAC)"],"output_types":["text responses","structured data (JSON, CSV)","code generation","transcriptions and summaries"],"categories":["image-visual","data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_10","uri":"capability://planning.reasoning.instruction.following.and.task.decomposition.with.multi.step.execution.planning","name":"instruction following and task decomposition with multi-step execution planning","description":"Gemini 2.5 Pro can follow complex, multi-step instructions and decompose tasks into subtasks with explicit planning. The model understands conditional logic, dependencies between steps, and can adapt execution based on intermediate results. Extended thinking enables explicit task decomposition and verification that all steps are completed correctly. This capability supports both simple sequential tasks and complex workflows with branching logic.","intents":["I need the model to follow a detailed workflow with multiple conditional branches and report completion status","I want to give the model a complex task and have it break it down into steps, execute them, and verify results","I need the model to handle error cases and adapt its approach if a step fails"],"best_for":["teams building AI agents for complex workflows","developers creating task automation systems","researchers studying task decomposition and planning in LLMs","users managing multi-step processes that require reasoning and adaptation"],"limitations":["Task decomposition is heuristic-based; complex tasks may be decomposed suboptimally","No built-in error recovery; requires explicit instructions for handling failures","Cannot execute external actions without integration (no native function calling)","Verification of task completion is best-effort; may miss subtle failures or side effects","Very complex workflows (>20 steps) may exceed reasoning budget or produce incomplete plans"],"requires":["API access to Gemini 2.5 Pro","Clear task description with explicit or implicit step requirements","Optional: examples of expected task decomposition or execution flow"],"input_types":["task descriptions in natural language","structured task specifications","workflow diagrams or pseudocode","examples of successful task execution"],"output_types":["task decomposition and execution plan","step-by-step execution with intermediate results","completion status and verification","error reports and recovery suggestions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_11","uri":"capability://text.generation.language.knowledge.synthesis.and.explanation.generation.with.pedagogical.adaptation","name":"knowledge synthesis and explanation generation with pedagogical adaptation","description":"Gemini 2.5 Pro generates explanations tailored to audience expertise level, using analogies, examples, and progressive complexity. The model can explain complex concepts in simple terms, provide deep technical details for experts, and adapt explanations based on feedback. Extended thinking enables the model to reason about what prior knowledge is needed and structure explanations for maximum clarity.","intents":["I need to explain a complex technical concept to a non-technical audience","I want a deep technical explanation of a concept for an expert audience","I need to generate educational content that builds understanding progressively"],"best_for":["educators and instructional designers creating learning materials","technical writers documenting complex systems","teams building educational AI tutors","content creators explaining concepts to diverse audiences"],"limitations":["Pedagogical adaptation quality depends on how well audience expertise is described","May oversimplify or over-complicate explanations if audience level is unclear","Cannot assess actual learning or comprehension; requires external evaluation","Analogies may be culturally specific or misleading if not carefully chosen","Very specialized domains may lack good analogies or examples in training data"],"requires":["API access to Gemini 2.5 Pro","Clear description of target audience and expertise level","Concept or topic to be explained"],"input_types":["concept or topic descriptions","audience expertise level descriptions","optional: examples of desired explanation style"],"output_types":["explanations at specified expertise level","analogies and examples","progressive learning sequences","visual descriptions (for diagrams or illustrations)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_12","uri":"capability://planning.reasoning.comparative.analysis.and.decision.support.with.structured.reasoning","name":"comparative analysis and decision support with structured reasoning","description":"Gemini 2.5 Pro can compare multiple options (products, approaches, strategies) across specified criteria, weigh trade-offs, and provide structured decision support. The model uses extended thinking to reason through pros/cons, identify hidden assumptions, and verify logical consistency of arguments. It can generate comparison matrices, identify decision criteria, and explain reasoning transparently.","intents":["I need to compare three cloud providers across cost, performance, and compliance criteria","I want to evaluate different architectural approaches for a system and understand trade-offs","I need to analyze competing research methodologies and identify their strengths/weaknesses"],"best_for":["teams making high-stakes technical or business decisions","researchers comparing methodologies or approaches","product managers evaluating feature options","engineers choosing between architectural patterns"],"limitations":["Comparison quality depends on how well criteria are specified; vague criteria produce subjective results","May exhibit bias toward options that are more represented in training data","Cannot access real-time pricing, performance metrics, or current information","Weighting of criteria is subjective; different stakeholders may disagree on importance","Cannot guarantee that all relevant criteria are considered or that hidden factors are identified"],"requires":["API access to Gemini 2.5 Pro","Clear description of options to compare","Explicit or implicit criteria for comparison"],"input_types":["descriptions of options to compare","comparison criteria","supporting data or documentation","images or diagrams of options"],"output_types":["structured comparison matrices","pros/cons analysis","trade-off explanations","decision recommendations with reasoning","identification of hidden assumptions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_2","uri":"capability://code.generation.editing.code.generation.and.analysis.with.multi.language.support.and.execution.context.awareness","name":"code generation and analysis with multi-language support and execution context awareness","description":"Gemini 2.5 Pro generates code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) with awareness of framework-specific patterns, library APIs, and execution environments. The model is trained on vast code repositories and can generate idiomatic solutions, suggest optimizations, and identify bugs. It understands context like project structure, dependencies, and runtime constraints to produce code that integrates with existing systems rather than isolated snippets.","intents":["I need to generate a REST API endpoint in Python that integrates with my existing FastAPI codebase","I want to refactor legacy JavaScript code to modern ES6+ patterns while maintaining backward compatibility","I need to debug a complex SQL query that's timing out on large datasets"],"best_for":["full-stack developers accelerating feature implementation across multiple languages","teams migrating codebases between frameworks or language versions","junior developers learning idiomatic patterns and best practices in unfamiliar languages"],"limitations":["Generated code may contain subtle bugs in complex logic; always requires human review before production use","Context window limits prevent analyzing entire large codebases (>100k lines); requires selective file submission","No real-time execution or testing — generated code must be tested in actual environment","Framework-specific knowledge degrades for niche or recently-released libraries not well-represented in training data","Does not have access to private/proprietary APIs or internal documentation unless explicitly provided in prompt"],"requires":["API access to Gemini 2.5 Pro","Code context provided as text (copy-paste or file upload)","Understanding of target language and framework to validate generated code"],"input_types":["text prompts describing requirements","code snippets or full files","error messages and stack traces","architecture diagrams or specifications"],"output_types":["code in specified language","refactored code with explanations","bug fixes with root cause analysis","optimization suggestions with performance metrics"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_3","uri":"capability://planning.reasoning.mathematical.problem.solving.with.symbolic.reasoning.and.proof.verification","name":"mathematical problem solving with symbolic reasoning and proof verification","description":"Gemini 2.5 Pro applies extended thinking to mathematical problems, performing symbolic manipulation, algebraic simplification, and logical proof construction. The model can solve equations, verify mathematical identities, work with abstract algebra concepts, and explain derivations step-by-step. It leverages training on mathematical texts and formal logic to produce rigorous solutions rather than numerical approximations.","intents":["I need to solve a system of differential equations and verify the solution is correct","I want to understand the proof of a complex theorem and have it explained in simpler terms","I need to check if my mathematical derivation is correct before submitting it for publication"],"best_for":["mathematics students and educators verifying solutions and understanding proofs","researchers in STEM fields needing symbolic computation and verification","engineers solving physics or optimization problems with mathematical rigor"],"limitations":["Very large symbolic expressions (>1000 terms) may exceed reasoning budget or produce incomplete simplifications","Numerical precision is limited to floating-point accuracy; not suitable for arbitrary-precision arithmetic","Cannot perform symbolic computation on proprietary or domain-specific mathematical notations without explanation","Proof verification is heuristic-based, not formally certified — complex proofs may contain logical gaps"],"requires":["API access to Gemini 2.5 Pro","Mathematical problems expressed in text or LaTeX notation","Understanding of mathematical notation to interpret responses"],"input_types":["mathematical equations in text or LaTeX","problem statements in natural language","proofs or derivations for verification","images of handwritten math (via vision capability)"],"output_types":["step-by-step solutions","verified proofs or counterexamples","simplified symbolic expressions","numerical answers with derivations"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_4","uri":"capability://text.generation.language.scientific.research.synthesis.and.literature.analysis.with.cross.reference.understanding","name":"scientific research synthesis and literature analysis with cross-reference understanding","description":"Gemini 2.5 Pro can analyze scientific papers, synthesize findings across multiple sources, identify research gaps, and explain complex scientific concepts. It understands domain-specific terminology, experimental methodologies, and statistical reasoning. The model can extract key findings, compare methodologies across papers, and contextualize results within broader scientific frameworks. Extended thinking enables verification of scientific claims and identification of logical inconsistencies in arguments.","intents":["I need to understand the current state of research on a specific topic by synthesizing 10 papers I've uploaded","I want to identify methodological differences between competing studies and understand their implications","I need to explain a complex scientific finding to a non-specialist audience while maintaining accuracy"],"best_for":["researchers conducting literature reviews and meta-analyses","graduate students learning to synthesize scientific knowledge","science communicators translating research for public audiences","teams evaluating scientific claims for evidence-based decision making"],"limitations":["Cannot access paywalled journals or proprietary databases; requires text/PDF uploads of papers","Domain knowledge is limited to fields well-represented in training data; cutting-edge niche research may be misunderstood","Statistical analysis is qualitative; cannot perform quantitative meta-analysis or complex statistical tests","May conflate correlation with causation or miss subtle methodological flaws in study design","Cannot verify claims against real-time databases or access post-publication corrections/retractions"],"requires":["API access to Gemini 2.5 Pro","Scientific papers provided as text or PDF uploads","Domain knowledge to validate interpretations and catch errors"],"input_types":["scientific paper text or PDFs","research abstracts and summaries","experimental data descriptions","images of figures, graphs, or tables from papers"],"output_types":["literature review summaries","comparative analysis of methodologies","synthesis of findings across papers","explanations of scientific concepts","identification of research gaps"],"categories":["text-generation-language","data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_5","uri":"capability://image.visual.image.understanding.and.visual.question.answering.with.spatial.reasoning","name":"image understanding and visual question answering with spatial reasoning","description":"Gemini 2.5 Pro processes images using vision transformer architecture to extract visual features, understand spatial relationships, recognize objects/text, and answer questions about image content. The model can read text in images (OCR), identify objects and their relationships, understand diagrams and charts, and reason about visual composition. It integrates visual understanding with text generation to produce detailed descriptions, answer specific questions, or extract structured data from images.","intents":["I need to extract all text from a screenshot and convert it to structured data","I want to understand what's happening in a complex diagram and have it explained in plain language","I need to identify objects in an image and their spatial relationships for a computer vision application"],"best_for":["developers building document processing or OCR applications","teams analyzing visual content at scale (screenshots, diagrams, charts)","accessibility teams converting visual content to text descriptions","researchers analyzing images in scientific or medical contexts"],"limitations":["OCR accuracy degrades on low-resolution, rotated, or heavily stylized text","Cannot identify individuals by face (privacy-preserving design) — only detects presence of faces","Spatial reasoning is approximate; precise measurements or geometric calculations require explicit coordinate data","Performance on abstract or artistic images is weaker than on photographs or diagrams","Cannot process video — only static images; requires frame extraction for video analysis"],"requires":["API access to Gemini 2.5 Pro","Images in JPEG, PNG, WebP, or GIF format","Maximum image size 20MB; resolution up to 4096x4096"],"input_types":["images (JPEG, PNG, WebP, GIF)","screenshots","diagrams and charts","photographs","scanned documents"],"output_types":["text descriptions","extracted text (OCR)","structured data (JSON, CSV)","answers to visual questions","object detection results"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_6","uri":"capability://text.generation.language.audio.transcription.and.analysis.with.speaker.diarization.and.context.understanding","name":"audio transcription and analysis with speaker diarization and context understanding","description":"Gemini 2.5 Pro transcribes audio files to text, identifies speaker changes (diarization), and analyzes audio content for sentiment, intent, and key topics. The model processes spectrograms and audio embeddings to understand speech patterns, accents, and emotional tone. It can summarize conversations, extract action items, and answer questions about audio content. Integration with text/image context enables cross-modal understanding (e.g., transcribe audio while referencing related documents).","intents":["I need to transcribe a meeting recording and extract action items and decisions","I want to analyze a customer support call to identify sentiment and common issues","I need to transcribe an interview and have it summarized with key quotes highlighted"],"best_for":["teams processing meeting recordings and generating summaries","customer success teams analyzing support interactions","researchers transcribing interviews or focus groups","content creators converting audio to text for accessibility"],"limitations":["Audio must be under 10 minutes; longer files require chunking or external preprocessing","Speaker diarization works best with 2-3 speakers; accuracy degrades with >5 speakers or heavy background noise","Transcription accuracy varies by audio quality, accent, and domain-specific terminology","Cannot identify specific individuals by voice (privacy-preserving design)","Supported formats limited to MP3, WAV, OGG, FLAC; no M4A or proprietary formats"],"requires":["API access to Gemini 2.5 Pro","Audio files in MP3, WAV, OGG, or FLAC format","Maximum file size 100MB","Audio duration under 10 minutes"],"input_types":["audio files (MP3, WAV, OGG, FLAC)","meeting recordings","interviews and conversations","podcasts and lectures"],"output_types":["transcriptions with timestamps","speaker-labeled transcripts","summaries and key points","sentiment and intent analysis","structured data (action items, decisions)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.output.generation","name":"structured data extraction and schema-based output generation","description":"Gemini 2.5 Pro can extract structured data from unstructured text, images, or audio and output it in specified formats (JSON, CSV, XML, etc.). The model understands schema definitions and ensures output conforms to provided structures. It can parse documents, extract entities, relationships, and metadata, then format results according to user-defined schemas. This enables integration with downstream systems that require structured inputs.","intents":["I need to extract customer information from unstructured support tickets and output as JSON matching my database schema","I want to parse a PDF invoice and extract line items, amounts, and dates into a CSV for accounting software","I need to extract entities (people, organizations, locations) from a research paper and output as structured RDF"],"best_for":["data engineering teams building ETL pipelines","teams automating document processing workflows","developers integrating AI extraction into structured data systems","researchers extracting knowledge graphs from unstructured text"],"limitations":["Extraction accuracy depends on source document clarity; handwritten or low-quality scans produce errors","Schema validation is best-effort; complex nested schemas may produce incomplete or malformed output","No transactional guarantees — partial extraction on timeout or error","Cannot enforce referential integrity across multiple extracted records","Performance degrades with very large documents (>100k tokens); requires chunking"],"requires":["API access to Gemini 2.5 Pro","Clear schema definition (JSON Schema, XML DTD, or natural language description)","Source documents in text, image, or audio format"],"input_types":["unstructured text","documents (PDFs, images of documents)","audio transcripts","web content"],"output_types":["JSON","CSV","XML","structured text formats","knowledge graphs"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_8","uri":"capability://text.generation.language.creative.content.generation.with.style.transfer.and.tone.adaptation","name":"creative content generation with style transfer and tone adaptation","description":"Gemini 2.5 Pro generates creative content (stories, marketing copy, poetry, dialogue) with control over tone, style, and voice. The model can adapt content to specific audiences, match existing writing styles, and maintain consistency across long-form outputs. It understands narrative structure, character development, and rhetorical techniques. Extended thinking enables the model to plan content structure before generation, ensuring coherence and impact.","intents":["I need to write marketing copy for a product that matches my brand voice and appeals to a specific audience","I want to generate a short story in the style of a specific author or genre","I need to create dialogue for characters that sounds natural and advances a plot"],"best_for":["content creators and copywriters accelerating production","marketing teams generating variations of messaging","writers exploring creative ideas and overcoming writer's block","game developers generating dialogue and narrative content"],"limitations":["Generated content may lack originality or contain clichés, especially for common genres","Tone consistency degrades in very long outputs (>5000 words); requires manual review and editing","Cannot guarantee factual accuracy in creative content; may invent plausible-sounding but false details","Style transfer quality depends on how well the target style is represented in training data","May inadvertently reproduce copyrighted material if trained on similar works"],"requires":["API access to Gemini 2.5 Pro","Clear description of desired tone, style, and audience","Optional: examples of target style or voice"],"input_types":["text prompts describing content requirements","style examples or reference materials","audience descriptions","plot outlines or content briefs"],"output_types":["marketing copy and ad text","creative stories and narratives","poetry and verse","dialogue and character interactions","social media content"],"categories":["text-generation-language","creative-writing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-pro-preview__cap_9","uri":"capability://text.generation.language.conversational.dialogue.with.multi.turn.context.retention.and.topic.tracking","name":"conversational dialogue with multi-turn context retention and topic tracking","description":"Gemini 2.5 Pro maintains conversation state across multiple turns, tracking topics, entities, and context to provide coherent responses. The model understands implicit references (pronouns, ellipsis), detects topic shifts, and can return to previous discussion threads. It supports follow-up questions, clarifications, and context refinement. Extended thinking enables the model to reason about conversation flow and identify when clarification is needed.","intents":["I need to have a multi-turn conversation where the model understands references to earlier points","I want to ask follow-up questions and have the model maintain context across turns","I need to switch topics mid-conversation and have the model track both threads"],"best_for":["developers building chatbot or conversational AI applications","teams creating customer support or help desk systems","researchers studying dialogue systems and conversational AI","users seeking interactive problem-solving or brainstorming"],"limitations":["Context window is finite (~100k tokens); very long conversations require summarization or context pruning","Context retention is per-session only; no persistent memory across separate conversations","May lose track of context in conversations with >50 turns or rapid topic switching","Cannot access external data or tools without explicit integration (no built-in web search or function calling)","Conversation state is not encrypted or persisted; requires application-level storage for durability"],"requires":["API access to Gemini 2.5 Pro","Conversation history provided as message array with roles (user/assistant)","Session management to track conversation state across API calls"],"input_types":["text messages","images (in multi-turn context)","audio (in multi-turn context)"],"output_types":["text responses","follow-up questions for clarification","structured summaries of conversation"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["API access to Google's Gemini 2.5 Pro endpoint via OpenRouter or Google AI Studio","Network connectivity with 30+ second timeout tolerance for reasoning-heavy requests","Understanding that thinking mode is enabled by default for Pro tier, no explicit flag needed","API key for Google Gemini or OpenRouter access","Image files in JPEG, PNG, WebP, or GIF format","Audio files in MP3, WAV, or OGG format","Maximum file sizes: images 20MB, audio 100MB","API access to Gemini 2.5 Pro","Clear task description with explicit or implicit step requirements","Optional: examples of expected task decomposition or execution flow"],"failure_modes":["Thinking mode increases latency by 5-15 seconds per request due to internal reasoning computation","Thinking tokens are not directly inspectable or controllable by the user — reasoning process is opaque","Extended thinking may not activate for simple queries, making behavior non-deterministic","Thinking budget is finite per request; extremely complex problems may timeout or produce incomplete reasoning","Image resolution is limited to ~4096x4096 pixels; higher resolutions are downsampled, losing fine detail","Audio input must be under 10 minutes; longer files require chunking or external preprocessing","No video input support — only static images and audio files","Cross-modal fusion quality degrades when modalities are semantically misaligned (e.g., image of unrelated content paired with text query)","Task decomposition is heuristic-based; complex tasks may be decomposed suboptimally","No built-in error recovery; requires explicit instructions for handling failures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.3,"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","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-2.5-pro-preview"}},"signature":"vl4iArFUY5DzB2gU/XZopBKuQef8WfbwKt5VyaqkJ1MYeb0EAvGnW6qhJkPwoVHORWMm+73KiVl4Mn9bUmPQCA==","signedAt":"2026-06-20T02:30:12.696Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-2.5-pro-preview","artifact":"https://unfragile.ai/google-gemini-2.5-pro-preview","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-2.5-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"}}