{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemma-3-4b-it","slug":"google-gemma-3-4b-it","name":"Google: Gemma 3 4B","type":"model","url":"https://openrouter.ai/models/google~gemma-3-4b-it","page_url":"https://unfragile.ai/google-gemma-3-4b-it","categories":["llm-apis"],"tags":["google","api-access","text","image"],"pricing":{"model":"paid","free":false,"starting_price":"$4.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemma-3-4b-it__cap_0","uri":"capability://image.visual.vision.language.understanding.with.128k.context.window","name":"vision-language understanding with 128k context window","description":"Processes both image and text inputs simultaneously through a unified transformer architecture, maintaining coherence across up to 128,000 tokens of context. The model uses interleaved vision-language embeddings that allow it to reason about visual content and text in the same forward pass, enabling tasks like image captioning, visual question answering, and document analysis without separate encoding pipelines.","intents":["I need to analyze screenshots and code snippets together to debug UI issues","I want to extract structured data from images of documents or tables","I need to answer questions about images while maintaining long conversation history","I want to process multi-page documents with images and text mixed together"],"best_for":["developers building document processing pipelines","teams creating visual AI assistants","builders prototyping multimodal RAG systems"],"limitations":["Vision input must be provided as base64-encoded images or URLs; no streaming image input","128k context window is shared between images and text — large images consume significant token budget","Image resolution handling is optimized for standard web images; extremely high-resolution images may be downsampled","No support for video input despite multimodal architecture"],"requires":["API access via OpenRouter or direct Google endpoint","Images in JPEG, PNG, WebP, or GIF format","Valid API authentication token"],"input_types":["text","image (base64 or URL)","mixed text and image sequences"],"output_types":["text","structured descriptions","JSON-formatted analysis"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3-4b-it__cap_1","uri":"capability://text.generation.language.multilingual.understanding.across.140.languages","name":"multilingual understanding across 140+ languages","description":"The model's transformer backbone is trained on a diverse multilingual corpus covering 140+ languages, using shared token embeddings and language-agnostic attention patterns. This enables zero-shot cross-lingual transfer where the model can understand and respond in languages not explicitly fine-tuned, with particular strength in high-resource languages and emerging support for low-resource language pairs through transfer learning.","intents":["I need to build a chatbot that handles customer support in multiple languages without separate models","I want to translate and analyze content across languages in a single API call","I need to understand user intent in languages my team doesn't speak natively","I want to create a global product that adapts to user language automatically"],"best_for":["teams building global SaaS products","developers creating multilingual chatbots","companies with international customer bases"],"limitations":["Performance degrades for extremely low-resource languages (< 1M speakers) with higher error rates","Code-switching (mixing multiple languages) may reduce accuracy compared to single-language input","Language detection is implicit; ambiguous text may be misinterpreted","No explicit language tagging in API — language must be inferred from context"],"requires":["API access via OpenRouter or Google endpoint","Valid UTF-8 encoded text input","API authentication token"],"input_types":["text in any of 140+ supported languages","mixed-language text"],"output_types":["text in requested or inferred language","language-agnostic structured data"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3-4b-it__cap_2","uri":"capability://text.generation.language.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Enhanced transformer layers with specialized attention patterns for mathematical token sequences, trained on mathematical datasets including proofs, equations, and step-by-step solutions. The model learns to decompose complex math problems into intermediate symbolic steps, maintaining consistency across multi-step derivations through constrained decoding that validates mathematical syntax during generation.","intents":["I need to solve math problems step-by-step with explanations","I want to verify mathematical proofs or derivations","I need to generate homework solutions with working shown","I want to build an AI tutor that explains math concepts"],"best_for":["educators building AI tutoring systems","developers creating homework help tools","teams building STEM learning platforms"],"limitations":["Symbolic computation is limited to algebraic manipulation; no integration with computer algebra systems like SymPy","Complex multi-variable calculus may produce correct intermediate steps but incorrect final answers","No support for mathematical notation beyond LaTeX — requires text-based math input","Reasoning chains are generated sequentially; cannot parallelize multi-branch mathematical exploration"],"requires":["API access via OpenRouter or Google endpoint","Math problems in text or LaTeX format","Valid API authentication token"],"input_types":["text-based math problems","LaTeX equations","step-by-step problem descriptions"],"output_types":["text explanations","LaTeX-formatted equations","step-by-step solutions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3-4b-it__cap_3","uri":"capability://text.generation.language.instruction.following.chat.with.context.awareness","name":"instruction-following chat with context awareness","description":"The 4B model is instruction-tuned using reinforcement learning from human feedback (RLHF) to follow complex multi-step instructions while maintaining awareness of conversation history and user intent. The chat interface uses a sliding context window that prioritizes recent messages and system prompts, with attention masking that prevents the model from attending to irrelevant historical context beyond a certain age threshold.","intents":["I need a conversational AI that remembers context across multiple turns","I want to give complex instructions and have them executed precisely","I need to build a chatbot that adapts its tone based on user preferences","I want to create a multi-turn dialogue system for customer support"],"best_for":["developers building conversational AI applications","teams creating customer support chatbots","builders prototyping interactive AI agents"],"limitations":["Context window is 128k tokens total; very long conversations will lose early context","Instruction following degrades with ambiguous or contradictory instructions","No persistent memory between separate conversation sessions — each API call is stateless","System prompts are limited to ~2k tokens; very detailed system instructions may be truncated"],"requires":["API access via OpenRouter or Google endpoint","Properly formatted conversation history with role tags (user/assistant)","Valid API authentication token"],"input_types":["text messages","conversation history arrays","system prompts"],"output_types":["text responses","structured JSON (with proper prompting)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3-4b-it__cap_4","uri":"capability://text.generation.language.efficient.inference.at.4b.parameter.scale","name":"efficient inference at 4b parameter scale","description":"A lightweight transformer model with 4 billion parameters optimized for inference speed and memory efficiency through quantization-aware training and architectural pruning. The model uses grouped query attention (GQA) to reduce KV cache size, enabling deployment on consumer GPUs and edge devices while maintaining competitive performance with larger models through knowledge distillation from larger Gemma variants.","intents":["I need to deploy an AI model on edge devices or mobile phones","I want to run inference locally without cloud API costs","I need to build a real-time conversational system with low latency","I want to fine-tune a model on limited hardware"],"best_for":["developers building on-device AI applications","teams with limited cloud budgets","builders creating real-time interactive systems","companies with privacy requirements preventing cloud inference"],"limitations":["4B parameters limits reasoning depth compared to 70B+ models; complex multi-step reasoning may fail","Knowledge distillation trade-offs mean some nuance is lost compared to larger models","Quantization to INT8 or lower reduces accuracy by 1-3% on benchmarks","Grouped query attention reduces model expressiveness slightly for very long sequences"],"requires":["GPU with 8GB+ VRAM (for full precision) or 4GB+ (for quantized)","PyTorch or TensorFlow runtime","API access via OpenRouter for cloud inference, or local model weights"],"input_types":["text","images (for multimodal variant)"],"output_types":["text","structured data"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3-4b-it__cap_5","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.validation","name":"structured output generation with schema validation","description":"The model can be constrained to generate outputs matching a provided JSON schema through constrained decoding, where a token-level validator prevents generation of tokens that would violate the schema. This enables reliable extraction of structured data (JSON, XML) without post-processing, using a grammar-based approach that enforces valid syntax during generation rather than validating after the fact.","intents":["I need to extract structured data from unstructured text reliably","I want to generate valid JSON responses without parsing errors","I need to build a form-filling AI that outputs valid structured data","I want to ensure API responses conform to my data model"],"best_for":["developers building data extraction pipelines","teams creating structured output APIs","builders prototyping form-filling or data entry automation"],"limitations":["Schema validation adds ~10-20% latency overhead due to token-level constraint checking","Complex nested schemas may cause the model to generate incomplete or truncated output","Schema must be provided in JSON Schema format; other formats require conversion","Constrained decoding may force the model to choose invalid values if the schema is too restrictive"],"requires":["API access via OpenRouter or Google endpoint","Valid JSON Schema definition","API authentication token"],"input_types":["text","JSON Schema definition"],"output_types":["JSON","XML (with appropriate schema)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3-4b-it__cap_6","uri":"capability://tool.use.integration.api.based.inference.with.openrouter.integration","name":"api-based inference with openrouter integration","description":"Gemma 3 4B is accessible via OpenRouter's unified API endpoint, which abstracts away model-specific implementation details and provides a standardized interface for text and vision inputs. The integration handles authentication, rate limiting, and request routing through OpenRouter's infrastructure, enabling seamless switching between Gemma 3 and other models without code changes.","intents":["I want to use Gemma 3 without managing my own infrastructure","I need to switch between multiple models without rewriting my code","I want to avoid vendor lock-in by using a model aggregator","I need to monitor and control API costs across multiple models"],"best_for":["developers prototyping AI features quickly","teams evaluating multiple models","builders with limited DevOps resources","companies wanting to avoid cloud vendor lock-in"],"limitations":["OpenRouter adds ~50-100ms latency overhead compared to direct API calls","Rate limiting is enforced at OpenRouter level; may be more restrictive than direct access","No direct access to model internals (embeddings, attention weights, hidden states)","Pricing may be higher than direct Google API access due to OpenRouter's margin"],"requires":["OpenRouter API key","HTTP client library (curl, requests, axios, etc.)","Network connectivity to OpenRouter endpoints"],"input_types":["text","images (base64 or URL)"],"output_types":["text","streaming text (with streaming parameter)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-3-4b-it__cap_7","uri":"capability://text.generation.language.streaming.response.generation.for.real.time.applications","name":"streaming response generation for real-time applications","description":"The model supports server-sent events (SSE) streaming where tokens are emitted as they are generated, enabling real-time display of model output without waiting for full completion. The streaming implementation uses chunked HTTP transfer encoding with newline-delimited JSON events, allowing clients to display partial responses and cancel requests mid-generation.","intents":["I need to show users AI responses in real-time as they're generated","I want to build a chat interface that displays tokens as they arrive","I need to cancel long-running requests if the user navigates away","I want to reduce perceived latency by showing partial results immediately"],"best_for":["developers building interactive chat UIs","teams creating real-time AI applications","builders optimizing for user experience and perceived latency"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream after partial output","Token-by-token streaming prevents batch optimizations that could improve throughput","Client must handle incomplete JSON if connection drops mid-event","Streaming responses cannot be retried from the point of failure; must restart from beginning"],"requires":["OpenRouter API key with streaming support enabled","HTTP client with SSE support (fetch API, axios, requests library)","Proper error handling for stream interruptions"],"input_types":["text","images"],"output_types":["streaming text (newline-delimited JSON events)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or direct Google endpoint","Images in JPEG, PNG, WebP, or GIF format","Valid API authentication token","API access via OpenRouter or Google endpoint","Valid UTF-8 encoded text input","API authentication token","Math problems in text or LaTeX format","Properly formatted conversation history with role tags (user/assistant)","GPU with 8GB+ VRAM (for full precision) or 4GB+ (for quantized)","PyTorch or TensorFlow runtime"],"failure_modes":["Vision input must be provided as base64-encoded images or URLs; no streaming image input","128k context window is shared between images and text — large images consume significant token budget","Image resolution handling is optimized for standard web images; extremely high-resolution images may be downsampled","No support for video input despite multimodal architecture","Performance degrades for extremely low-resource languages (< 1M speakers) with higher error rates","Code-switching (mixing multiple languages) may reduce accuracy compared to single-language input","Language detection is implicit; ambiguous text may be misinterpreted","No explicit language tagging in API — language must be inferred from context","Symbolic computation is limited to algebraic manipulation; no integration with computer algebra systems like SymPy","Complex multi-variable calculus may produce correct intermediate steps but incorrect final answers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"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-gemma-3-4b-it","compare_url":"https://unfragile.ai/compare?artifact=google-gemma-3-4b-it"}},"signature":"Tgcsj6hacaYCREZ0Cc7agvOwY1T3myla1cuf3ZBIKIscq3x3UGRNvuC+QJT9uMKtKAWyFl/YvxQWtTwCz8hmCg==","signedAt":"2026-06-22T06:52:38.007Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemma-3-4b-it","artifact":"https://unfragile.ai/google-gemma-3-4b-it","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemma-3-4b-it","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"}}