{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemini-3-pro-image-preview","slug":"google-gemini-3-pro-image-preview","name":"Google: Nano Banana Pro (Gemini 3 Pro Image Preview)","type":"model","url":"https://openrouter.ai/models/google~gemini-3-pro-image-preview","page_url":"https://unfragile.ai/google-gemini-3-pro-image-preview","categories":["image-generation"],"tags":["google","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemini-3-pro-image-preview__cap_0","uri":"capability://image.visual.text.to.image.generation.with.multimodal.reasoning","name":"text-to-image generation with multimodal reasoning","description":"Generates images from natural language prompts using Gemini 3 Pro's multimodal reasoning engine, which processes text descriptions through a vision-language transformer architecture to produce coherent, semantically-aligned imagery. The model integrates real-world grounding through training on diverse visual datasets, enabling generation of contextually accurate scenes, objects, and compositions that respect physical plausibility and spatial relationships.","intents":["Generate product mockups and marketing visuals from text descriptions","Create concept art and design variations for iterative creative workflows","Produce training data and synthetic imagery for computer vision tasks","Rapidly prototype visual ideas without manual design work"],"best_for":["Product teams prototyping visual designs at scale","Content creators generating marketing assets and social media imagery","ML engineers building synthetic datasets for vision model training","Designers exploring multiple visual directions quickly"],"limitations":["Generation latency typically 5-15 seconds per image depending on prompt complexity","Output resolution capped at model's native training resolution (likely 1024x1024 or similar)","May struggle with highly specific brand guidelines or photorealistic human faces without extensive prompt engineering","No guarantee of consistency across multiple generations of the same prompt without seed control"],"requires":["OpenRouter API key or direct Google Cloud credentials","HTTP/REST client capability or SDK wrapper","Text prompt input (minimum 10 characters recommended for coherent output)","Sufficient API quota and rate limits (typically 1-10 requests per minute depending on tier)"],"input_types":["text (natural language prompts)","structured prompt templates with style/quality modifiers"],"output_types":["image (PNG or JPEG format, typically base64-encoded or URL-referenced)","image metadata (generation parameters, seed if applicable)"],"categories":["image-visual","content-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-pro-image-preview__cap_1","uri":"capability://image.visual.image.to.image.editing.with.semantic.understanding","name":"image-to-image editing with semantic understanding","description":"Accepts an existing image plus a text instruction and applies targeted edits by parsing the semantic intent of the instruction through Gemini 3 Pro's vision-language model, then selectively modifying image regions while preserving context and coherence. Uses attention-based masking and diffusion-guided inpainting to localize edits to relevant areas, avoiding artifacts at edit boundaries.","intents":["Modify specific objects or regions in product photos without manual masking","Apply style transfers or aesthetic changes to existing imagery","Remove or replace unwanted elements while maintaining background consistency","Iterate on design mockups by describing desired changes in natural language"],"best_for":["E-commerce teams editing product photography at scale","Designers iterating on mockups and prototypes without Photoshop","Content creators removing unwanted elements from photos","Teams needing non-destructive, instruction-based image modification"],"limitations":["Edit quality degrades with overly complex or ambiguous instructions","Cannot reliably preserve fine details (text, small objects) in edited regions","Boundary artifacts may appear at edit seams, requiring post-processing","No explicit control over which regions are edited — relies on model's semantic understanding","Editing latency 8-20 seconds depending on image size and instruction complexity"],"requires":["OpenRouter API key or Google Cloud credentials","Source image in PNG, JPEG, or WebP format (max resolution typically 2048x2048)","Text instruction describing desired edit (minimum 5 words recommended)","HTTP/REST client with multipart form-data support for image upload"],"input_types":["image (existing image to edit)","text (natural language instruction describing the edit)"],"output_types":["image (edited image in same format as input)","edit metadata (regions modified, confidence scores if available)"],"categories":["image-visual","content-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-pro-image-preview__cap_2","uri":"capability://image.visual.visual.question.answering.and.image.analysis","name":"visual question answering and image analysis","description":"Accepts an image and natural language question, then uses Gemini 3 Pro's vision-language transformer to analyze the image and generate detailed, contextually-grounded answers. The model performs multi-step reasoning over visual features (objects, relationships, text, composition) to answer questions ranging from simple object identification to complex scene understanding and reasoning about implied context.","intents":["Extract structured information from screenshots, documents, or photos","Analyze product images for quality control or compliance verification","Understand scene context and relationships in complex images","Generate alt-text and accessibility descriptions for images at scale"],"best_for":["Content moderation teams analyzing images for policy violations","E-commerce platforms extracting product attributes from user-uploaded photos","Accessibility teams generating alt-text for image archives","Researchers analyzing visual datasets and extracting structured insights"],"limitations":["Accuracy on highly specialized domains (medical imaging, scientific microscopy) not guaranteed without fine-tuning","May hallucinate details not present in the image, especially for ambiguous or low-quality inputs","Response latency 2-8 seconds depending on image complexity and question length","No built-in confidence scores — difficult to programmatically assess answer reliability","Cannot reliably read handwritten text or non-standard fonts"],"requires":["OpenRouter API key or Google Cloud credentials","Image in PNG, JPEG, or WebP format (max resolution typically 2048x2048)","Natural language question (minimum 3 words)","HTTP/REST client with multipart form-data support"],"input_types":["image (image to analyze)","text (natural language question or instruction)"],"output_types":["text (natural language answer or analysis)","structured data (if question implies structured output like JSON)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-pro-image-preview__cap_3","uri":"capability://image.visual.batch.image.generation.with.api.orchestration","name":"batch image generation with api orchestration","description":"Supports submitting multiple image generation requests through OpenRouter's batch processing interface, which queues requests and executes them asynchronously with optimized throughput. Requests are processed in parallel across Gemini 3 Pro's distributed inference infrastructure, with results returned via webhook callbacks or polling endpoints, enabling cost-effective bulk generation workflows.","intents":["Generate hundreds of product variations for A/B testing and catalog expansion","Create synthetic training datasets for ML models at scale","Produce marketing assets for multiple campaigns in parallel","Batch-process image editing instructions across large image collections"],"best_for":["E-commerce platforms generating product imagery at scale","ML teams creating synthetic datasets for model training","Marketing agencies producing assets for multiple campaigns","Content platforms automating visual content creation"],"limitations":["Batch processing introduces 30-120 second latency before execution begins (queuing overhead)","No guaranteed ordering of results — responses may arrive out-of-order relative to request submission","Batch size limits typically 100-1000 requests per batch depending on API tier","Webhook callbacks require publicly-accessible endpoint for result delivery","Cost per image may be higher than individual requests due to batch overhead"],"requires":["OpenRouter API key with batch processing tier enabled","Batch submission endpoint (typically /v1/batch or similar)","Webhook endpoint or polling mechanism for result retrieval","Request format: JSON array of generation prompts with metadata"],"input_types":["text (array of prompts)","structured metadata (style parameters, seed values, quality settings)"],"output_types":["image (array of generated images with batch IDs)","batch status metadata (completion percentage, error logs)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-pro-image-preview__cap_4","uri":"capability://image.visual.multimodal.prompt.composition.with.image.context","name":"multimodal prompt composition with image context","description":"Accepts prompts that combine text descriptions with reference images, allowing users to specify generation or editing intent by providing both linguistic context and visual examples. The model uses Gemini 3 Pro's multimodal encoder to jointly embed text and image context, enabling style transfer, consistency matching, and instruction refinement based on visual reference material.","intents":["Generate images in a specific visual style by providing style reference images","Maintain visual consistency across multiple generated images using reference imagery","Refine generation intent by combining text descriptions with visual examples","Apply learned styles or compositions from reference images to new prompts"],"best_for":["Design teams maintaining visual consistency across campaigns","Content creators generating variations on existing visual themes","Brands ensuring generated imagery aligns with established visual identity","Teams iterating on designs by showing examples rather than describing them"],"limitations":["Style transfer quality depends on semantic similarity between reference and generation prompt","Multiple reference images may conflict, leading to unpredictable blending behavior","No explicit control over which aspects of reference image influence output","Latency increases with number of reference images (typically +2-3 seconds per additional image)","Reference image resolution and aspect ratio may constrain output characteristics"],"requires":["OpenRouter API key or Google Cloud credentials","Text prompt describing desired output","One or more reference images in PNG, JPEG, or WebP format","HTTP/REST client supporting multipart form-data with multiple image uploads"],"input_types":["text (generation prompt with style/intent description)","image (one or more reference images for style or consistency guidance)"],"output_types":["image (generated image influenced by reference context)","generation metadata (which reference images influenced output, if available)"],"categories":["image-visual","content-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-3-pro-image-preview__cap_5","uri":"capability://image.visual.real.world.grounding.and.physical.plausibility.verification","name":"real-world grounding and physical plausibility verification","description":"Validates generated or edited images against real-world constraints by analyzing spatial relationships, object interactions, and physical plausibility through Gemini 3 Pro's vision understanding. The model can detect physically impossible configurations, inconsistent lighting, or semantically incoherent scenes, providing feedback on generation quality without manual review.","intents":["Verify generated product mockups respect physical constraints and spatial coherence","Detect and flag unrealistic or physically impossible generated scenes","Assess whether generated images would be useful for training computer vision models","Provide automated quality feedback on bulk-generated imagery before human review"],"best_for":["QA teams validating synthetic training data for vision models","E-commerce platforms filtering generated product images for realism","Content moderation teams detecting AI-generated or manipulated imagery","ML teams assessing synthetic data quality before model training"],"limitations":["Plausibility assessment is subjective and may not align with domain-specific requirements","Cannot detect subtle manipulations or high-quality deepfakes","Latency 3-8 seconds per image for detailed plausibility analysis","No quantitative confidence scores — output is primarily qualitative feedback","May flag stylized or artistic imagery as implausible due to training on photorealistic data"],"requires":["OpenRouter API key or Google Cloud credentials","Image to analyze in PNG, JPEG, or WebP format","Optional: domain-specific constraints or plausibility criteria as text input","HTTP/REST client with multipart form-data support"],"input_types":["image (image to assess for plausibility)","text (optional domain-specific constraints or quality criteria)"],"output_types":["text (plausibility assessment and feedback)","structured data (JSON with plausibility scores, detected issues, recommendations)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key or direct Google Cloud credentials","HTTP/REST client capability or SDK wrapper","Text prompt input (minimum 10 characters recommended for coherent output)","Sufficient API quota and rate limits (typically 1-10 requests per minute depending on tier)","OpenRouter API key or Google Cloud credentials","Source image in PNG, JPEG, or WebP format (max resolution typically 2048x2048)","Text instruction describing desired edit (minimum 5 words recommended)","HTTP/REST client with multipart form-data support for image upload","Image in PNG, JPEG, or WebP format (max resolution typically 2048x2048)","Natural language question (minimum 3 words)"],"failure_modes":["Generation latency typically 5-15 seconds per image depending on prompt complexity","Output resolution capped at model's native training resolution (likely 1024x1024 or similar)","May struggle with highly specific brand guidelines or photorealistic human faces without extensive prompt engineering","No guarantee of consistency across multiple generations of the same prompt without seed control","Edit quality degrades with overly complex or ambiguous instructions","Cannot reliably preserve fine details (text, small objects) in edited regions","Boundary artifacts may appear at edit seams, requiring post-processing","No explicit control over which regions are edited — relies on model's semantic understanding","Editing latency 8-20 seconds depending on image size and instruction complexity","Accuracy on highly specialized domains (medical imaging, scientific microscopy) not guaranteed without fine-tuning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.27,"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-pro-image-preview","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-3-pro-image-preview"}},"signature":"yMKDQe6K4gLEdg2bV6sM0zIqGJ61aEeZ25pheeMsXnYpINKlauUo1lJz/29P557EhhblQQFtklvaEaLcDRZRBA==","signedAt":"2026-06-21T18:22:26.959Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-3-pro-image-preview","artifact":"https://unfragile.ai/google-gemini-3-pro-image-preview","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-3-pro-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"}}