{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemini-3-flash-preview","slug":"google-gemini-3-flash-preview","name":"Google: Gemini 3 Flash Preview","type":"model","url":"https://openrouter.ai/models/google~gemini-3-flash-preview","page_url":"https://unfragile.ai/google-gemini-3-flash-preview","categories":["ai-agents"],"tags":["google","api-access","text","image","audio","video"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemini-3-flash-preview__cap_0","uri":"capability://tool.use.integration.multi.turn.agentic.reasoning.with.tool.use.orchestration","name":"multi-turn agentic reasoning with tool-use orchestration","description":"Gemini 3 Flash is optimized for extended agentic workflows where the model maintains context across multiple turns while dynamically calling external tools. It uses a stateless request-response pattern where each turn includes full conversation history, tool definitions via JSON schema, and execution results, enabling the model to reason about tool outputs and decide next actions without server-side session management.","intents":["Build autonomous agents that can chain multiple API calls across turns without losing context","Create chatbots that call external APIs (search, databases, webhooks) and reason about results","Implement multi-step workflows where the model decides which tools to invoke based on intermediate results"],"best_for":["Teams building LLM-powered agents with complex multi-step workflows","Developers creating autonomous systems that need fast inference for real-time decision-making","Startups prototyping agentic products where latency directly impacts user experience"],"limitations":["No built-in memory persistence — conversation history must be managed by the client application","Tool execution is synchronous within a single request-response cycle; parallel tool invocation requires explicit batching logic","Context window constraints mean very long conversation histories may require summarization or pruning strategies"],"requires":["Google API key or OpenRouter API key with Gemini 3 Flash access","HTTP client capable of streaming responses (for real-time token output)","Tool definitions formatted as JSON Schema compliant with OpenAI function-calling spec"],"input_types":["text (user messages, system prompts)","tool definitions (JSON Schema format)","tool execution results (JSON-serializable objects)"],"output_types":["text (model reasoning and responses)","tool calls (structured function invocations with parameters)","streaming tokens (for real-time output)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-flash-preview__cap_1","uri":"capability://code.generation.editing.streaming.code.generation.and.completion.with.language.agnostic.support","name":"streaming code generation and completion with language-agnostic support","description":"Gemini 3 Flash generates code across 40+ programming languages using a transformer-based approach that understands syntax, semantics, and common patterns. It supports streaming output (token-by-token delivery) for real-time IDE integration, and accepts multi-file context to generate code aware of existing codebase structure, imports, and dependencies without requiring explicit AST parsing.","intents":["Generate code snippets or functions from natural language descriptions in any major language","Complete partial code with context-aware suggestions that respect existing code style and imports","Refactor or optimize code by understanding the full function/class context and suggesting improvements"],"best_for":["IDE plugin developers integrating real-time code completion","Solo developers and small teams using LLM-assisted coding workflows","Polyglot teams working across multiple languages who need a single model for all languages"],"limitations":["No built-in linting or syntax validation — generated code may contain errors requiring manual review","Context window limits mean very large files or multi-file contexts may be truncated, losing relevant imports or type definitions","Streaming output adds latency compared to batch generation; not suitable for offline code generation at scale"],"requires":["API key (Google or OpenRouter)","HTTP client with streaming support for real-time token delivery","Optional: language-specific syntax highlighting for IDE integration"],"input_types":["text (natural language prompts, code comments)","code (partial code, full files, multi-file context)"],"output_types":["code (generated or completed code in target language)","streaming tokens (for real-time IDE display)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-flash-preview__cap_2","uri":"capability://image.visual.multimodal.input.processing.text.image.audio.video","name":"multimodal input processing (text, image, audio, video)","description":"Gemini 3 Flash accepts and processes multiple input modalities in a single request: text prompts, images (JPEG, PNG, WebP, GIF), audio files (MP3, WAV, etc.), and video frames. The model uses a unified embedding space where all modalities are converted to token representations, allowing it to reason across modalities (e.g., describe an image, transcribe audio, or answer questions about video content) without separate preprocessing pipelines.","intents":["Analyze images and answer questions about their content, layout, or text within them","Transcribe or summarize audio/video content and extract key information","Process mixed-media documents (PDFs with images, videos with captions) in a single inference pass"],"best_for":["Document processing teams handling mixed-media inputs (scanned PDFs, screenshots, diagrams)","Content moderation platforms analyzing images, videos, and text together","Accessibility applications converting video/audio to text or descriptions"],"limitations":["Audio/video processing requires file upload or base64 encoding; streaming audio input not supported","Video processing is frame-based; temporal reasoning across scenes is limited to sequential frame analysis","Image resolution limits apply; very high-resolution images may be downsampled, losing fine details"],"requires":["API key (Google or OpenRouter)","Media files in supported formats (JPEG, PNG, WebP, GIF for images; MP3, WAV for audio; MP4, WebM for video)","Base64 encoding or file upload capability for binary media"],"input_types":["text (prompts, questions, instructions)","image (JPEG, PNG, WebP, GIF)","audio (MP3, WAV, OGG, FLAC)","video (MP4, WebM, MOV)"],"output_types":["text (descriptions, transcriptions, answers, analysis)","structured data (extracted information, metadata)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-flash-preview__cap_3","uri":"capability://data.processing.analysis.structured.data.extraction.with.json.schema.validation","name":"structured data extraction with json schema validation","description":"Gemini 3 Flash can extract structured data from unstructured text or images by accepting a JSON Schema definition of the desired output format. The model constrains its output to match the schema, returning valid JSON that can be directly parsed without post-processing. This works via a constrained decoding approach where the model's token generation is guided by the schema to ensure type correctness and required field presence.","intents":["Extract entities (names, dates, amounts) from documents or images and return as structured JSON","Parse semi-structured text (logs, emails, forms) into typed objects matching a predefined schema","Convert unstructured data into database-ready records with guaranteed schema compliance"],"best_for":["Data engineering teams building ETL pipelines that need reliable structured extraction","Form processing and document digitization systems","Teams building knowledge graphs or databases from unstructured sources"],"limitations":["Schema must be provided upfront; dynamic schema inference is not supported","Complex nested schemas with many optional fields may reduce extraction accuracy","Extraction quality depends on input clarity; ambiguous or poorly formatted source data may result in null/missing fields"],"requires":["API key (Google or OpenRouter)","JSON Schema definition matching desired output structure","Input data (text or image) containing information to extract"],"input_types":["text (unstructured documents, logs, emails)","image (forms, receipts, screenshots)"],"output_types":["JSON (structured data matching provided schema)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-flash-preview__cap_4","uri":"capability://text.generation.language.real.time.streaming.response.generation.with.token.level.control","name":"real-time streaming response generation with token-level control","description":"Gemini 3 Flash supports server-sent events (SSE) streaming where tokens are delivered one-by-one as they are generated, enabling real-time display in client applications. The streaming protocol includes metadata for each token (finish reason, safety ratings) and supports cancellation mid-stream. This allows applications to display model output character-by-character without waiting for full response completion, reducing perceived latency.","intents":["Build chat interfaces that display model responses in real-time as they are generated","Create interactive coding assistants that stream code suggestions token-by-token","Implement long-form content generation (articles, stories) with live preview"],"best_for":["Web and mobile app developers building chat UIs with real-time feedback","IDE plugin developers integrating inline code suggestions","Content creators using LLM-assisted writing tools"],"limitations":["Streaming adds complexity to error handling; partial responses may be incomplete if stream is interrupted","Token-by-token delivery increases network overhead compared to batch responses; not suitable for high-latency networks","Client must buffer tokens for proper display; naive character-by-character rendering may show incomplete words"],"requires":["API key (Google or OpenRouter)","HTTP client with streaming/SSE support (fetch API, axios with responseType: 'stream', etc.)","Client-side buffering logic for word-level display (optional but recommended)"],"input_types":["text (prompts, messages, instructions)"],"output_types":["streaming tokens (SSE format with metadata)","text (complete response after stream ends)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-flash-preview__cap_5","uri":"capability://planning.reasoning.context.aware.reasoning.with.chain.of.thought.decomposition","name":"context-aware reasoning with chain-of-thought decomposition","description":"Gemini 3 Flash uses an internal chain-of-thought mechanism where the model breaks down complex problems into reasoning steps before generating final answers. While the reasoning process is not exposed by default, the model's training emphasizes step-by-step problem decomposition, enabling it to handle multi-step logic, math problems, and complex decision-making. This is particularly optimized for agentic workflows where intermediate reasoning must be reliable.","intents":["Solve multi-step math or logic problems with accurate intermediate reasoning","Make complex decisions by reasoning through multiple factors and trade-offs","Debug code by tracing through execution logic and identifying root causes"],"best_for":["Educational applications requiring step-by-step problem solving","Autonomous agents making complex decisions based on multiple data sources","Technical support systems diagnosing issues through logical deduction"],"limitations":["Internal reasoning is not exposed; users cannot inspect or validate intermediate steps","Reasoning quality degrades on problems requiring domain-specific knowledge not in training data","Very long reasoning chains may be truncated due to context window limits"],"requires":["API key (Google or OpenRouter)","Well-structured prompts that clearly define the problem and expected reasoning approach"],"input_types":["text (problem statements, questions, context)"],"output_types":["text (final answer with implicit reasoning)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-flash-preview__cap_6","uri":"capability://text.generation.language.system.prompt.customization.with.role.based.behavior.control","name":"system prompt customization with role-based behavior control","description":"Gemini 3 Flash accepts a system prompt (or 'system instruction') that defines the model's behavior, tone, and constraints for a conversation. The system prompt is processed separately from user messages and influences all subsequent responses in the conversation without being repeated. This enables role-based customization (e.g., 'You are a Python expert', 'Respond in JSON only') that persists across multiple turns without token overhead.","intents":["Create specialized chatbots with consistent personas (customer support, technical advisor, creative writer)","Enforce output format constraints (JSON-only, markdown, code blocks) across entire conversations","Build domain-specific assistants that prioritize certain knowledge areas or reasoning approaches"],"best_for":["Teams building domain-specific chatbots or assistants","Applications requiring consistent tone and behavior across conversations","Systems that need to enforce strict output formats or safety guidelines"],"limitations":["System prompt changes require a new conversation; cannot be updated mid-conversation","Very long system prompts consume context window, reducing space for user messages","Model may not perfectly adhere to system prompt constraints; explicit user instructions can override system behavior"],"requires":["API key (Google or OpenRouter)","Well-crafted system prompt defining desired behavior"],"input_types":["text (system prompt, user messages)"],"output_types":["text (responses following system prompt constraints)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-flash-preview__cap_7","uri":"capability://data.processing.analysis.batch.processing.with.cost.optimization.for.non.real.time.workloads","name":"batch processing with cost optimization for non-real-time workloads","description":"Gemini 3 Flash supports batch API processing where multiple requests are submitted together and processed asynchronously, typically at a 50% cost reduction compared to real-time API calls. Batch requests are queued and processed during off-peak hours, with results delivered via webhook or polling. This is implemented via a separate batch endpoint that accepts JSONL-formatted request files and returns results in the same format.","intents":["Process large datasets (thousands of documents, images, or code files) at reduced cost","Generate training data or synthetic examples for ML pipelines without real-time latency requirements","Perform bulk content analysis, summarization, or classification on historical data"],"best_for":["Data engineering teams processing large datasets where latency is not critical","ML teams generating synthetic training data at scale","Content platforms performing bulk moderation or analysis on archived content"],"limitations":["Batch processing introduces 1-24 hour latency; not suitable for real-time applications","Batch requests must be formatted as JSONL; complex nested structures require careful serialization","No streaming support in batch mode; responses are returned as complete text"],"requires":["API key (Google or OpenRouter) with batch API access","JSONL-formatted request file with proper schema","Webhook endpoint or polling mechanism to retrieve results"],"input_types":["JSONL (batch request file with multiple prompts/inputs)"],"output_types":["JSONL (batch results file with responses)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-flash-preview__cap_8","uri":"capability://safety.moderation.safety.filtering.and.content.moderation.with.configurable.thresholds","name":"safety filtering and content moderation with configurable thresholds","description":"Gemini 3 Flash includes built-in safety filters that detect and block harmful content (hate speech, violence, sexual content, etc.) before generation. The model returns safety ratings for each content category along with a block reason if content is filtered. Applications can configure safety thresholds per category (BLOCK_NONE, BLOCK_ONLY_HIGH, BLOCK_MEDIUM_AND_ABOVE, BLOCK_LOW_AND_ABOVE) to customize filtering strictness without retraining.","intents":["Ensure generated content complies with platform policies and legal requirements","Detect and flag potentially harmful user inputs before processing","Build moderation dashboards that categorize content by safety risk level"],"best_for":["Content platforms and social networks requiring automated moderation","Enterprise applications with strict compliance requirements","Teams building safety-critical systems (education, healthcare, finance)"],"limitations":["Safety filtering is not perfect; some harmful content may pass through, and some benign content may be incorrectly blocked","Configurable thresholds apply globally; cannot set different thresholds per user or context","Safety ratings are returned post-generation; cannot prevent generation of borderline content before tokens are generated"],"requires":["API key (Google or OpenRouter)","Understanding of safety categories and threshold levels"],"input_types":["text (user prompts, content to analyze)"],"output_types":["text (generated content or block reason)","safety ratings (per-category risk scores)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Google API key or OpenRouter API key with Gemini 3 Flash access","HTTP client capable of streaming responses (for real-time token output)","Tool definitions formatted as JSON Schema compliant with OpenAI function-calling spec","API key (Google or OpenRouter)","HTTP client with streaming support for real-time token delivery","Optional: language-specific syntax highlighting for IDE integration","Media files in supported formats (JPEG, PNG, WebP, GIF for images; MP3, WAV for audio; MP4, WebM for video)","Base64 encoding or file upload capability for binary media","JSON Schema definition matching desired output structure","Input data (text or image) containing information to extract"],"failure_modes":["No built-in memory persistence — conversation history must be managed by the client application","Tool execution is synchronous within a single request-response cycle; parallel tool invocation requires explicit batching logic","Context window constraints mean very long conversation histories may require summarization or pruning strategies","No built-in linting or syntax validation — generated code may contain errors requiring manual review","Context window limits mean very large files or multi-file contexts may be truncated, losing relevant imports or type definitions","Streaming output adds latency compared to batch generation; not suitable for offline code generation at scale","Audio/video processing requires file upload or base64 encoding; streaming audio input not supported","Video processing is frame-based; temporal reasoning across scenes is limited to sequential frame analysis","Image resolution limits apply; very high-resolution images may be downsampled, losing fine details","Schema must be provided upfront; dynamic schema inference is not supported","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.33,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=google-gemini-3-flash-preview","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-3-flash-preview"}},"signature":"LhA74t5xCVlfMSy1N/6Jfetq3eMqBEZ+2XTBOZvZx53d858BbEhApM5gC2r9yScIqjcULfopRjSt1WwwObBrAQ==","signedAt":"2026-06-19T22:54:51.194Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-3-flash-preview","artifact":"https://unfragile.ai/google-gemini-3-flash-preview","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-3-flash-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"}}