{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemini-3.1-flash-image-preview","slug":"google-gemini-3.1-flash-image-preview","name":"Google: Nano Banana 2 (Gemini 3.1 Flash Image Preview)","type":"model","url":"https://openrouter.ai/models/google~gemini-3.1-flash-image-preview","page_url":"https://unfragile.ai/google-gemini-3.1-flash-image-preview","categories":["image-generation","testing-quality"],"tags":["google","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemini-3.1-flash-image-preview__cap_0","uri":"capability://image.visual.text.to.image.generation.with.semantic.understanding","name":"text-to-image generation with semantic understanding","description":"Generates photorealistic and stylized images from natural language prompts using a diffusion-based architecture with semantic understanding of complex scene compositions, object relationships, and visual styles. The model processes text embeddings through a latent diffusion pipeline optimized for inference speed, enabling high-quality outputs at reduced computational cost compared to prior Gemini generations.","intents":["Generate marketing assets and product mockups from text descriptions without design tools","Create concept art and visual prototypes for game design, architecture, or product development","Produce diverse variations of a scene or object by iterating on prompt refinements","Generate training data or synthetic imagery for computer vision model development"],"best_for":["Product teams and designers prototyping visual concepts rapidly","Solo developers building image-heavy applications without design resources","Content creators and marketers generating on-brand visual assets at scale","ML engineers generating synthetic training datasets for vision models"],"limitations":["No fine-grained control over exact spatial layout or precise object positioning — composition is probabilistic","Text prompts longer than ~500 tokens may lose semantic coherence in complex multi-object scenes","Generation latency typically 3-8 seconds per image depending on complexity and model load","Cannot generate images of real identifiable people or copyrighted characters with high fidelity","Output resolution fixed at model's native dimensions; upscaling requires separate post-processing"],"requires":["API key for Google Cloud or OpenRouter access","HTTP/REST client or SDK (Python, Node.js, etc.)","Text prompt in English or supported language","Network connectivity for cloud inference"],"input_types":["text (natural language prompt)","optional: style modifiers (e.g., 'oil painting', 'cinematic', 'photorealistic')"],"output_types":["image (PNG or JPEG format)","image metadata (generation parameters, seed if exposed)"],"categories":["image-visual","content-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-flash-image-preview__cap_1","uri":"capability://image.visual.image.inpainting.and.region.based.editing","name":"image inpainting and region-based editing","description":"Edits specific regions of existing images by accepting a base image, mask, and text description of desired changes. The model uses a masked diffusion approach where only masked regions are regenerated while preserving unmasked content, enabling seamless content-aware inpainting with semantic understanding of context and style matching.","intents":["Remove unwanted objects or people from photographs while maintaining background coherence","Replace or modify specific elements in an image (e.g., change clothing color, swap backgrounds)","Extend or expand image boundaries with contextually appropriate content","Perform non-destructive edits on product photos or marketing materials"],"best_for":["E-commerce platforms editing product photography at scale","Photo editing applications and mobile apps requiring AI-assisted editing","Content creators and photographers removing unwanted elements without manual retouching","Designers iterating on visual mockups and marketing materials"],"limitations":["Mask definition must be precise; ambiguous or overly large masks may produce inconsistent results","Inpainting quality degrades when masked region is >40% of image area or contains complex textures","Style matching between inpainted region and surrounding content is probabilistic; may require multiple generations","Cannot reliably inpaint faces or hands with anatomical accuracy","Requires base image as input; cannot generate from scratch using this capability alone"],"requires":["API key for Google Cloud or OpenRouter","Base image file (PNG, JPEG, WebP)","Binary mask or region specification (same dimensions as base image)","Text prompt describing desired edit or replacement content","Image processing library for mask generation (e.g., PIL, OpenCV)"],"input_types":["image (base image to edit)","image (binary mask or region specification)","text (description of desired changes or replacement content)"],"output_types":["image (edited image with inpainted regions)"],"categories":["image-visual","content-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-flash-image-preview__cap_2","uri":"capability://image.visual.image.to.image.transformation.with.style.transfer","name":"image-to-image transformation with style transfer","description":"Transforms an input image based on a text prompt describing desired style, composition, or content changes. The model encodes the input image into latent space, then applies guided diffusion conditioned on both the image embedding and text prompt to produce a transformed output that preserves semantic content while applying stylistic or compositional modifications.","intents":["Convert photographs to artistic styles (e.g., oil painting, watercolor, anime, 3D render)","Recompose or reframe existing images based on text descriptions","Generate variations of product photos with different backgrounds or lighting","Adapt visual content across different aesthetic or brand guidelines"],"best_for":["Creative agencies and studios batch-processing visual assets across multiple styles","E-commerce platforms generating product variations for A/B testing","Content creators producing stylistic variations for social media or marketing","Game developers and artists generating concept art from reference images"],"limitations":["Semantic content preservation is probabilistic; significant prompt changes may alter or distort original subjects","Style transfer strength cannot be precisely controlled; output is binary (apply or not apply)","Transformation quality degrades with low-resolution input images (<512px)","Cannot reliably preserve fine details like text, logos, or small objects during transformation","Latency typically 4-10 seconds depending on image complexity and transformation intensity"],"requires":["API key for Google Cloud or OpenRouter","Input image file (PNG, JPEG, WebP, minimum 256x256 resolution recommended)","Text prompt describing desired transformation or style","Network connectivity for cloud inference"],"input_types":["image (source image to transform)","text (style description or transformation prompt)"],"output_types":["image (transformed image with applied style or composition changes)"],"categories":["image-visual","style-transfer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-flash-image-preview__cap_3","uri":"capability://image.visual.multi.modal.image.understanding.and.captioning","name":"multi-modal image understanding and captioning","description":"Analyzes images to generate natural language descriptions, extract visual information, and answer questions about image content. The model uses a vision encoder to process image pixels, then generates text through a language decoder conditioned on visual embeddings, enabling detailed scene understanding, object detection, and contextual reasoning about image content.","intents":["Generate alt text and captions for accessibility and SEO purposes","Extract structured information from images (e.g., product details, text, objects present)","Answer natural language questions about image content and relationships","Analyze visual content for moderation, quality assessment, or categorization"],"best_for":["Content management systems and DAM platforms requiring automated image tagging and captioning","Accessibility teams generating alt text at scale for web and document content","E-commerce platforms extracting product attributes from images","Research teams analyzing visual datasets or conducting image-based surveys"],"limitations":["Captioning quality varies with image clarity; low-resolution or heavily compressed images produce generic descriptions","Spatial reasoning is approximate; precise object localization requires bounding box output (not always available)","Cannot reliably read small text or handwriting in images","May hallucinate objects or relationships not present in image, especially with ambiguous or abstract content","Latency typically 1-3 seconds per image depending on complexity"],"requires":["API key for Google Cloud or OpenRouter","Image file (PNG, JPEG, WebP, GIF)","Optional: natural language question or prompt for specific analysis","Network connectivity for cloud inference"],"input_types":["image (image to analyze)","text (optional: question or analysis prompt)"],"output_types":["text (caption, description, or answer)","structured data (optional: extracted attributes, object lists)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-flash-image-preview__cap_4","uri":"capability://image.visual.batch.image.processing.with.api.orchestration","name":"batch image processing with api orchestration","description":"Processes multiple images sequentially or in parallel through the API, with support for batching requests and managing rate limits. The implementation handles request queuing, error retry logic, and response aggregation, enabling efficient processing of image collections without manual orchestration or timeout management.","intents":["Process large image datasets (100s-1000s of images) for captioning, analysis, or transformation","Generate variations or edits across product catalogs or content libraries","Implement image processing pipelines in applications without managing concurrency manually","Monitor and log processing results across batch operations for quality assurance"],"best_for":["Data engineering teams processing large image datasets for ML training or analysis","E-commerce platforms batch-generating product variations or descriptions","Content platforms automating image processing workflows at scale","Developers building image processing microservices or background job systems"],"limitations":["API rate limits apply; batch processing speed is constrained by quota (typically 100-1000 requests/minute depending on tier)","No built-in persistence or checkpointing; failed batches require manual retry or external state management","Latency per image is cumulative; processing 1000 images at 3 seconds each requires ~50 minutes","Cost scales linearly with image count; no volume discounts or batch pricing","Requires external orchestration for complex workflows (e.g., conditional branching, error handling)"],"requires":["API key with sufficient quota for batch operations","HTTP client or SDK supporting concurrent requests","Image file collection (local storage or cloud bucket)","Optional: job queue system (e.g., Celery, Bull, AWS SQS) for production deployments","Monitoring and logging infrastructure for tracking batch progress"],"input_types":["image (multiple images in collection)","text (optional: prompts or parameters per image)"],"output_types":["image (processed/transformed images)","text (captions, descriptions, or analysis results)","structured data (batch processing logs, error reports)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-flash-image-preview__cap_5","uri":"capability://image.visual.prompt.engineering.and.iterative.refinement","name":"prompt engineering and iterative refinement","description":"Supports iterative prompt refinement through API feedback loops, where users can adjust text prompts and regenerate outputs based on quality assessment. The model maintains semantic understanding across iterations, allowing users to guide generation toward desired results through natural language feedback without retraining or fine-tuning.","intents":["Refine image generation results by iterating on prompts until desired output is achieved","Explore creative variations by systematically adjusting style, composition, or content descriptors","Develop prompt templates and best practices for consistent results across use cases","Optimize prompts for specific visual outcomes without manual parameter tuning"],"best_for":["Creative professionals and designers exploring visual concepts interactively","Product teams developing prompt templates for consistent brand-aligned outputs","Researchers studying prompt engineering and model behavior","Developers building interactive image generation applications with user feedback loops"],"limitations":["No explicit feedback mechanism; users must manually assess output quality and adjust prompts","Prompt sensitivity is high; small wording changes may produce significantly different results","No built-in prompt optimization or suggestion system; refinement is manual and iterative","Latency accumulates with multiple iterations; exploring 5-10 variations requires 15-80 seconds","No version control or history tracking for prompts and outputs without external tooling"],"requires":["API key for Google Cloud or OpenRouter","Interactive client or application supporting prompt input and output display","User capability to assess visual quality and articulate refinements in natural language","Optional: prompt management system or database for tracking iterations"],"input_types":["text (initial prompt)","text (refined prompts based on feedback)"],"output_types":["image (generated output)","text (optional: generation metadata or quality metrics)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3.1-flash-image-preview__cap_6","uri":"capability://image.visual.api.based.integration.with.sdks.and.rest.endpoints","name":"api-based integration with sdks and rest endpoints","description":"Exposes image generation and editing capabilities through REST API and language-specific SDKs (Python, Node.js, etc.), enabling integration into applications and workflows. The implementation provides standardized request/response formats, authentication via API keys, and error handling patterns consistent with Google Cloud and OpenRouter conventions.","intents":["Integrate image generation into web applications, mobile apps, or backend services","Build custom image processing pipelines and workflows using standard HTTP clients","Automate image generation in CI/CD pipelines or scheduled jobs","Expose image generation capabilities through custom APIs or microservices"],"best_for":["Full-stack developers building image-heavy applications (e.g., design tools, content platforms)","Backend engineers integrating image generation into microservices or APIs","DevOps teams automating image processing in CI/CD pipelines","Startups and small teams leveraging cloud APIs to avoid GPU infrastructure costs"],"limitations":["API latency (3-10 seconds per request) makes real-time interactive use cases challenging","Network dependency; offline usage not supported","Rate limiting applies; high-volume applications may require quota increases or load balancing","Cost per request; no local caching or memoization without external infrastructure","Authentication via API keys; requires secure key management in production environments"],"requires":["API key from Google Cloud or OpenRouter","HTTP client library or SDK (e.g., requests in Python, axios in Node.js)","Network connectivity and firewall rules allowing outbound HTTPS","Optional: authentication middleware for multi-user applications","Optional: caching layer (Redis, CDN) for frequently requested outputs"],"input_types":["text (JSON request body with prompts, parameters)","image (base64-encoded or URL reference for editing tasks)"],"output_types":["image (base64-encoded or URL reference)","JSON (metadata, generation parameters, error details)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API key for Google Cloud or OpenRouter access","HTTP/REST client or SDK (Python, Node.js, etc.)","Text prompt in English or supported language","Network connectivity for cloud inference","API key for Google Cloud or OpenRouter","Base image file (PNG, JPEG, WebP)","Binary mask or region specification (same dimensions as base image)","Text prompt describing desired edit or replacement content","Image processing library for mask generation (e.g., PIL, OpenCV)","Input image file (PNG, JPEG, WebP, minimum 256x256 resolution recommended)"],"failure_modes":["No fine-grained control over exact spatial layout or precise object positioning — composition is probabilistic","Text prompts longer than ~500 tokens may lose semantic coherence in complex multi-object scenes","Generation latency typically 3-8 seconds per image depending on complexity and model load","Cannot generate images of real identifiable people or copyrighted characters with high fidelity","Output resolution fixed at model's native dimensions; upscaling requires separate post-processing","Mask definition must be precise; ambiguous or overly large masks may produce inconsistent results","Inpainting quality degrades when masked region is >40% of image area or contains complex textures","Style matching between inpainted region and surrounding content is probabilistic; may require multiple generations","Cannot reliably inpaint faces or hands with anatomical accuracy","Requires base image as input; cannot generate from scratch using this capability alone","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.37,"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-3.1-flash-image-preview","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-3.1-flash-image-preview"}},"signature":"BTkbKk5s8qCEuSIBHO68AqeUZpv9Y319WsR80vxfrpSjJT3o0eBb+BQAdbljua8Lp/c0skcsgF5NAALQYiZ0CA==","signedAt":"2026-06-22T09:21:58.116Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-3.1-flash-image-preview","artifact":"https://unfragile.ai/google-gemini-3.1-flash-image-preview","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-3.1-flash-image-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"}}