{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemini-2.5-flash","slug":"google-gemini-2.5-flash","name":"Google: Gemini 2.5 Flash","type":"model","url":"https://openrouter.ai/models/google~gemini-2.5-flash","page_url":"https://unfragile.ai/google-gemini-2.5-flash","categories":["model-training"],"tags":["google","api-access","text","image","audio","video"],"pricing":{"model":"paid","free":false,"starting_price":"$3.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemini-2.5-flash__cap_0","uri":"capability://planning.reasoning.extended.reasoning.with.native.thinking.mode","name":"extended reasoning with native thinking mode","description":"Gemini 2.5 Flash implements a built-in 'thinking' capability that enables the model to perform extended chain-of-thought reasoning before generating responses. This approach uses an internal reasoning phase where the model explores multiple solution paths, validates assumptions, and refines its approach before committing to an output, similar to process reward modeling but integrated directly into the inference pipeline rather than as a post-hoc verification step.","intents":["I need the model to show its work and reasoning steps for complex problems","I want more reliable answers for math, logic, and multi-step coding problems","I need to understand why the model chose a particular solution approach"],"best_for":["Teams building reasoning-heavy applications (theorem proving, complex debugging)","Educational platforms requiring explainable AI outputs","Scientific computing and research applications"],"limitations":["Thinking mode increases latency by 2-5x compared to standard inference","Thinking tokens are billed separately and may increase total API costs by 30-50%","Thinking output is not always human-readable; internal reasoning format may be opaque","Thinking capability cannot be disabled per-request in some API versions"],"requires":["API access to Gemini 2.5 Flash via Google AI Studio or OpenRouter","Support for extended thinking in your client library (check version compatibility)","Sufficient API quota and budget for increased token consumption"],"input_types":["text prompts","code snippets","mathematical problems","multi-step reasoning queries"],"output_types":["text with reasoning trace","structured explanations","step-by-step solutions"],"categories":["planning-reasoning","advanced-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_1","uri":"capability://code.generation.editing.multimodal.code.generation.with.context.awareness","name":"multimodal code generation with context awareness","description":"Gemini 2.5 Flash generates code across 40+ programming languages with architectural awareness of project context, including the ability to ingest images of whiteboards, architecture diagrams, and UI mockups to inform code generation. The model uses vision transformers to parse visual inputs and map them to code patterns, enabling code generation from design artifacts without manual specification.","intents":["Generate code from a whiteboard sketch or architecture diagram","Create UI components based on a screenshot or design mockup","Implement algorithms described in a visual format (flowcharts, pseudocode images)","Generate boilerplate code in multiple languages from a single specification"],"best_for":["Full-stack teams converting designs to code","Rapid prototyping workflows where visual specs exist before code","Cross-language code generation for polyglot architectures","Teams using visual design tools (Figma, Sketch) as source of truth"],"limitations":["Image-to-code accuracy degrades for complex, multi-panel designs (>5 distinct UI sections)","Generated code requires manual review for security and performance; no built-in linting","Context window limits prevent ingesting entire large codebases for architectural awareness","No built-in version control integration; generated code is stateless"],"requires":["API key for Google Gemini or OpenRouter access","Image files in JPEG, PNG, WebP, or GIF format (max 20MB per image)","Client library supporting vision input (google-generativeai SDK or OpenRouter API)","For best results: high-resolution images (1024x1024 minimum) with clear visual structure"],"input_types":["text prompts","code snippets","images (screenshots, diagrams, mockups)","mixed text + image prompts"],"output_types":["source code (Python, JavaScript, Java, C++, Go, Rust, etc.)","structured code with comments","multi-file code generation"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_10","uri":"capability://memory.knowledge.context.caching.for.reduced.latency.and.cost.on.repeated.inputs","name":"context caching for reduced latency and cost on repeated inputs","description":"Gemini 2.5 Flash supports prompt caching where frequently-used context (large documents, code repositories, system prompts) is cached on the server side. Subsequent requests with the same cached context reuse the cached tokens, reducing both latency and API costs. The caching is transparent to the application; you specify which parts of the prompt to cache, and the model handles cache hits/misses automatically.","intents":["Reduce latency and cost when processing multiple queries against the same large document or codebase","Build applications that repeatedly reference the same context (e.g., chatbots with a fixed knowledge base)","Optimize RAG systems by caching retrieved documents","Reduce costs for applications with large system prompts"],"best_for":["RAG and knowledge base applications with repeated queries","Code analysis tools that repeatedly reference the same codebase","Chatbots with large fixed system prompts or knowledge bases","Document analysis applications processing multiple queries per document"],"limitations":["Cache hits require exact token-level matching; minor prompt variations bypass the cache","Cache TTL is limited (typically 5 minutes); long-lived caches require periodic refresh","Cache size limits apply (typically 1M tokens per cache); very large contexts may not be cacheable","Cache overhead adds ~100ms latency on first request; benefits only accrue on subsequent requests"],"requires":["API access to Gemini 2.5 Flash with caching support","Client library supporting cache control headers (google-generativeai SDK v0.3.0+)","Stable, reusable context that doesn't change between requests","Multiple requests against the same context to justify caching overhead"],"input_types":["large documents or code files (cached)","system prompts (cached)","user queries (not cached, varies per request)"],"output_types":["text responses","structured data","streaming tokens"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_11","uri":"capability://text.generation.language.cross.lingual.translation.and.multilingual.understanding","name":"cross-lingual translation and multilingual understanding","description":"Gemini 2.5 Flash supports translation and understanding across 100+ languages with context-aware translation that preserves tone, idioms, and cultural nuances. The model uses multilingual embeddings and cross-lingual attention mechanisms to understand and generate text in multiple languages, enabling applications to serve global audiences without language-specific fine-tuning.","intents":["Translate content between languages while preserving tone and context","Build multilingual chatbots that understand and respond in the user's language","Analyze sentiment or extract entities from non-English text","Generate multilingual content (e.g., product descriptions in multiple languages)"],"best_for":["Global applications serving multiple language communities","Content localization platforms","Multilingual customer support systems","International research and analysis applications"],"limitations":["Translation quality varies significantly by language pair; low-resource languages have lower accuracy","Idioms and cultural references may not translate perfectly; human review is recommended","Language detection is not 100% accurate for code-mixed or multilingual inputs","Some languages with non-Latin scripts may have rendering or encoding issues"],"requires":["API access to Gemini 2.5 Flash","Language codes or language names in prompts","UTF-8 encoding for non-Latin scripts","For best results: clear language specification in prompts"],"input_types":["text in any supported language","code-mixed text (multiple languages in one input)","language-specific formatting (RTL scripts, etc.)"],"output_types":["translated text","multilingual responses","language-specific formatting"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_2","uri":"capability://planning.reasoning.scientific.and.mathematical.problem.solving","name":"scientific and mathematical problem solving","description":"Gemini 2.5 Flash includes specialized reasoning pathways for mathematical derivations, symbolic computation, and scientific problem-solving. The model leverages its extended thinking mode to work through multi-step proofs, differential equations, and complex calculations with explicit intermediate steps, using techniques similar to neural theorem proving but applied to general scientific domains.","intents":["Solve differential equations and provide step-by-step derivations","Verify mathematical proofs and identify logical gaps","Generate scientific code (NumPy, SciPy, SymPy) for computational problems","Explain physics, chemistry, and biology concepts with mathematical rigor"],"best_for":["Academic researchers and students requiring rigorous mathematical explanations","Scientific computing teams building simulation and modeling code","Physics and engineering teams solving complex differential equations","Educational platforms for STEM subjects"],"limitations":["Symbolic computation is limited to expressions representable in text; no native CAS integration","Numerical accuracy degrades for very large matrices or ill-conditioned systems","Cannot directly interface with Mathematica, Maple, or other CAS tools","Reasoning mode required for complex problems adds 3-5x latency"],"requires":["API access to Gemini 2.5 Flash","Mathematical notation in LaTeX or plain text format","For numerical validation: external libraries (NumPy, SciPy, SymPy) for verification","Understanding of mathematical notation and domain-specific terminology"],"input_types":["mathematical expressions (LaTeX, plain text)","scientific problem descriptions","code snippets with mathematical operations","images of equations or handwritten math"],"output_types":["step-by-step derivations","mathematical proofs","scientific code (Python with NumPy/SciPy)","numerical solutions with confidence intervals"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_3","uri":"capability://image.visual.audio.and.video.understanding.with.temporal.reasoning","name":"audio and video understanding with temporal reasoning","description":"Gemini 2.5 Flash processes audio and video inputs by extracting temporal context and semantic meaning across frames or audio segments. The model uses a multi-modal transformer architecture to align visual and audio streams, enabling it to understand dialogue, music, scene transitions, and temporal relationships within media, then generate descriptions, transcripts, or code based on that understanding.","intents":["Transcribe and summarize video content with speaker identification","Extract key moments and scenes from long-form video","Generate code or documentation based on tutorial videos","Analyze audio for tone, emotion, and content understanding"],"best_for":["Content creators and media companies processing large video libraries","Educational platforms converting video tutorials to code or documentation","Accessibility teams generating transcripts and captions","Research teams analyzing video or audio datasets"],"limitations":["Video processing is limited to files under 2 hours duration; longer videos must be chunked","Audio quality significantly impacts transcription accuracy; noisy audio may require preprocessing","Temporal reasoning is frame-based; fine-grained millisecond-level timing is not supported","No native support for multiple audio tracks or complex audio mixing scenarios"],"requires":["API access to Gemini 2.5 Flash with video/audio support","Video files in MP4, WebM, or MOV format (max file size varies by API tier)","Audio files in MP3, WAV, or OGG format","For best results: video resolution 720p or higher, audio sample rate 16kHz or higher"],"input_types":["video files (MP4, WebM, MOV)","audio files (MP3, WAV, OGG)","mixed media with text prompts for context"],"output_types":["transcripts with timestamps","summaries and key moments","code generated from tutorial videos","structured metadata (speakers, topics, scenes)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_4","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.generation","name":"structured data extraction and schema-based generation","description":"Gemini 2.5 Flash supports schema-based output generation where you define a JSON or protobuf schema and the model generates responses conforming to that schema. This uses constrained decoding techniques to ensure outputs match the specified structure, enabling reliable extraction of entities, relationships, and structured information from unstructured text or images without post-processing.","intents":["Extract structured data (entities, relationships, attributes) from documents or images","Generate JSON or protobuf responses that conform to a predefined schema","Parse natural language into structured form for database insertion","Ensure API responses match a contract schema without manual validation"],"best_for":["Data engineering teams building ETL pipelines with LLM-based extraction","API developers requiring deterministic response schemas","Teams building knowledge graphs from unstructured text","Document processing and form extraction workflows"],"limitations":["Schema complexity is limited; deeply nested schemas (>10 levels) may cause generation failures","Constrained decoding adds 10-20% latency overhead compared to unconstrained generation","Schema validation is strict; any deviation from schema causes the request to fail rather than returning partial results","No built-in support for conditional schemas or schema variants"],"requires":["API access to Gemini 2.5 Flash with schema support","JSON Schema or protobuf schema definition","Client library supporting schema parameters (google-generativeai SDK v0.3.0+)","Clear understanding of expected output structure before making requests"],"input_types":["unstructured text","documents (PDF, images of documents)","natural language descriptions","mixed text and image inputs"],"output_types":["JSON objects conforming to schema","protobuf messages","structured arrays of entities","nested objects with typed fields"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_5","uri":"capability://text.generation.language.real.time.streaming.inference.with.token.level.control","name":"real-time streaming inference with token-level control","description":"Gemini 2.5 Flash supports streaming responses where tokens are emitted in real-time as they are generated, enabling low-latency user-facing applications. The streaming API provides token-level granularity, allowing you to process partial outputs, implement custom stopping logic, or aggregate tokens into semantic chunks without waiting for full response completion.","intents":["Build chat interfaces with real-time token streaming for perceived responsiveness","Implement custom token processing or filtering during generation","Create interactive applications where users see output as it's generated","Reduce perceived latency in conversational AI applications"],"best_for":["Chat and conversational UI applications","Real-time code generation interfaces (IDEs, notebooks)","Live content generation (streaming writing assistants)","Applications requiring sub-second time-to-first-token"],"limitations":["Streaming adds complexity to error handling; mid-stream failures may leave partial outputs","Token-level streaming prevents batch optimizations; throughput is lower than non-streaming requests","Thinking mode (extended reasoning) is not compatible with streaming in some API versions","Client must handle backpressure and connection management for long-running streams"],"requires":["API access to Gemini 2.5 Flash with streaming support","Client library with streaming support (google-generativeai SDK or OpenRouter API)","Proper error handling and connection management in client code","HTTP/2 or WebSocket support for efficient streaming"],"input_types":["text prompts","multi-turn conversation history","code snippets","mixed text and image inputs"],"output_types":["streamed text tokens","partial JSON objects (for schema-based generation)","token metadata (finish reasons, safety ratings)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_6","uri":"capability://text.generation.language.multi.turn.conversation.with.stateless.context.management","name":"multi-turn conversation with stateless context management","description":"Gemini 2.5 Flash maintains conversation state through explicit message history passed in each request, using a turn-based message format where each request includes the full conversation history. The model uses attention mechanisms to track context across turns and maintain coherence, with support for system prompts that define behavior across the entire conversation.","intents":["Build multi-turn chatbots that maintain context across user interactions","Implement conversation-based reasoning where each turn builds on previous context","Create role-playing or persona-based interactions with consistent behavior","Maintain conversation history for audit, replay, or analysis"],"best_for":["Chatbot and conversational AI applications","Customer support and Q&A systems","Interactive tutoring and educational applications","Teams building conversation-based workflows"],"limitations":["Context window is shared across history and new input; long conversations may exceed token limits","No built-in persistence; conversation state must be managed externally (database, cache)","Each request includes full history, increasing latency and API costs for long conversations","No native support for conversation branching or alternative paths"],"requires":["API access to Gemini 2.5 Flash","Client library supporting message history (google-generativeai SDK or OpenRouter API)","External storage for conversation persistence (database, cache, or session store)","Context window awareness; typical limit is 1M tokens but varies by API tier"],"input_types":["text messages","system prompts defining behavior","conversation history as message array","mixed text and image inputs in multi-turn context"],"output_types":["text responses","structured responses (with schema)","streaming tokens","metadata (finish reasons, safety ratings)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_7","uri":"capability://safety.moderation.safety.filtering.and.content.moderation.with.configurable.thresholds","name":"safety filtering and content moderation with configurable thresholds","description":"Gemini 2.5 Flash includes built-in safety filters that detect and block harmful content (hate speech, violence, sexual content, etc.) with configurable sensitivity thresholds. The model returns safety ratings for each content category in responses, enabling applications to implement custom moderation logic or adjust filtering behavior per use case without requiring external moderation services.","intents":["Filter harmful user inputs before processing","Detect and block unsafe model outputs","Implement custom moderation policies for specific domains (e.g., stricter for children's content)","Monitor safety metrics and audit model behavior"],"best_for":["Public-facing applications requiring content moderation","Platforms with specific safety requirements (education, healthcare, children's content)","Teams building compliance-heavy applications","Applications requiring audit trails of safety decisions"],"limitations":["Safety filters are not 100% accurate; false positives and false negatives occur","Thresholds are coarse-grained (BLOCK_NONE, BLOCK_ONLY_HIGH, BLOCK_MOST_LOW, BLOCK_ALL); fine-grained control is limited","Safety ratings are provided per category but not per token or phrase","No built-in support for domain-specific safety policies (e.g., medical vs general content)"],"requires":["API access to Gemini 2.5 Flash","Understanding of safety categories and threshold levels","Client library supporting safety settings (google-generativeai SDK)","External logging or monitoring for safety events if audit trails are required"],"input_types":["text prompts","user-generated content","images","mixed media"],"output_types":["safety ratings per category","blocked/unblocked status","confidence scores for safety classifications"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_8","uri":"capability://tool.use.integration.function.calling.with.multi.provider.schema.support","name":"function calling with multi-provider schema support","description":"Gemini 2.5 Flash supports function calling where you define a set of tools/functions as JSON schemas and the model decides when and how to call them. The model generates structured function calls with arguments, enabling integration with external APIs, databases, or custom logic. The implementation uses schema-based function routing with support for OpenAI, Anthropic, and Google-native function calling formats.","intents":["Build AI agents that can call external APIs or tools","Create applications where the model decides which function to call based on user intent","Implement tool-use workflows without manual prompt engineering","Enable the model to fetch real-time data or perform actions"],"best_for":["AI agent and autonomous workflow applications","Applications requiring real-time data fetching or API integration","Teams building tool-use systems without manual prompt engineering","Multi-step reasoning applications where tools are called iteratively"],"limitations":["Function calling requires explicit schema definition; complex or dynamic schemas are difficult to express","No built-in retry logic; failed function calls must be handled by the application","Function execution is not atomic; partial failures in multi-step workflows require custom handling","No built-in support for function result validation or error recovery"],"requires":["API access to Gemini 2.5 Flash with function calling support","JSON Schema definitions for each function","Client library supporting function calling (google-generativeai SDK v0.3.0+)","Implementation of actual functions/tools that the model can invoke"],"input_types":["text prompts with function definitions","conversation history with function calls","function results to feed back into the model"],"output_types":["function calls with arguments (JSON)","text responses interspersed with function calls","structured tool-use workflows"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.5-flash__cap_9","uri":"capability://automation.workflow.batch.processing.api.for.high.volume.cost.optimized.inference","name":"batch processing api for high-volume, cost-optimized inference","description":"Gemini 2.5 Flash offers a batch processing API where you submit multiple requests in a single batch file, and the model processes them asynchronously with lower per-token costs (typically 50% discount). Requests are queued and processed during off-peak hours, trading latency for cost savings. The batch API returns results in a structured format that maps to input requests.","intents":["Process large volumes of data (thousands of documents, code files, images) at lower cost","Perform bulk content generation or analysis where latency is not critical","Reduce API costs for non-real-time applications","Analyze datasets or perform batch translations/summarizations"],"best_for":["Data processing and analytics teams with large datasets","Content generation platforms processing bulk requests","Research teams analyzing large corpora","Cost-sensitive applications where latency is acceptable (hours to days)"],"limitations":["Batch processing introduces 1-24 hour latency; not suitable for real-time applications","Batch size limits apply (typically 100K requests per batch); very large jobs must be split","No streaming support in batch mode; full responses are returned","Batch requests cannot use thinking mode or other premium features in some API versions"],"requires":["API access to Gemini 2.5 Flash batch API","Batch file in JSONL format (one request per line)","Batch size within API limits (typically 100K requests)","Patience for asynchronous processing (1-24 hour turnaround)"],"input_types":["JSONL batch files with multiple requests","text, code, images, or mixed media per request"],"output_types":["JSONL results file with responses mapped to input requests","structured data with request IDs for matching"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["API access to Gemini 2.5 Flash via Google AI Studio or OpenRouter","Support for extended thinking in your client library (check version compatibility)","Sufficient API quota and budget for increased token consumption","API key for Google Gemini or OpenRouter access","Image files in JPEG, PNG, WebP, or GIF format (max 20MB per image)","Client library supporting vision input (google-generativeai SDK or OpenRouter API)","For best results: high-resolution images (1024x1024 minimum) with clear visual structure","API access to Gemini 2.5 Flash with caching support","Client library supporting cache control headers (google-generativeai SDK v0.3.0+)","Stable, reusable context that doesn't change between requests"],"failure_modes":["Thinking mode increases latency by 2-5x compared to standard inference","Thinking tokens are billed separately and may increase total API costs by 30-50%","Thinking output is not always human-readable; internal reasoning format may be opaque","Thinking capability cannot be disabled per-request in some API versions","Image-to-code accuracy degrades for complex, multi-panel designs (>5 distinct UI sections)","Generated code requires manual review for security and performance; no built-in linting","Context window limits prevent ingesting entire large codebases for architectural awareness","No built-in version control integration; generated code is stateless","Cache hits require exact token-level matching; minor prompt variations bypass the cache","Cache TTL is limited (typically 5 minutes); long-lived caches require periodic refresh","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-flash","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-2.5-flash"}},"signature":"SbtKccyZu/26y3m6mdGLFClLxjiifnhXCSSLhOZvp+qcYNYTF73LCOpxZPtSXxby1BqQGQC0+hKeU53+OT66CQ==","signedAt":"2026-06-21T08:49:27.103Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-2.5-flash","artifact":"https://unfragile.ai/google-gemini-2.5-flash","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-2.5-flash","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"}}