{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemma-4-31b-it","slug":"google-gemma-4-31b-it","name":"Google: Gemma 4 31B","type":"model","url":"https://openrouter.ai/models/google~gemma-4-31b-it","page_url":"https://unfragile.ai/google-gemma-4-31b-it","categories":["image-generation"],"tags":["google","api-access","text","image","video"],"pricing":{"model":"paid","free":false,"starting_price":"$1.30e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemma-4-31b-it__cap_0","uri":"capability://image.visual.multimodal.instruction.following.with.text.and.image.inputs","name":"multimodal instruction-following with text and image inputs","description":"Processes both text and image inputs simultaneously within a single inference pass, using a unified embedding space that aligns visual and textual representations. The model architecture integrates a vision encoder (likely ViT-based) with the language model backbone, allowing it to reason across modalities without separate encoding steps. Supports up to 256K token context window for extended reasoning over mixed-media documents.","intents":["I need to analyze an image and ask follow-up questions about it in a single conversation","I want to extract information from documents that contain both text and diagrams","I need to describe what's happening in a screenshot and get code suggestions based on it"],"best_for":["developers building document analysis tools with visual components","teams creating accessibility tools that need to understand screenshots","researchers working on vision-language understanding tasks"],"limitations":["Image encoding adds ~500-800ms latency compared to text-only inference","No native support for video input despite tag mention — only static images","Image resolution capped at typical transformer input sizes (likely 1024x1024 or 2048x2048)","Cannot generate images, only analyze them"],"requires":["API access via OpenRouter or Google's inference endpoints","Images in standard formats (JPEG, PNG, WebP, GIF)","Base64 encoding or URL-accessible image URIs for API submission"],"input_types":["text (natural language instructions)","image (JPEG, PNG, WebP, GIF formats)"],"output_types":["text (natural language responses)","structured text (JSON, markdown, code)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-31b-it__cap_1","uri":"capability://planning.reasoning.extended.context.reasoning.with.configurable.thinking.mode","name":"extended-context reasoning with configurable thinking mode","description":"Implements a two-stage inference architecture where an optional 'thinking' mode enables the model to perform internal chain-of-thought reasoning before generating final outputs. When activated, the model allocates computational budget to explore solution spaces, backtrack, and refine reasoning before committing to a response. This is configurable per-request, allowing callers to trade latency for reasoning depth on complex problems.","intents":["I need the model to show its work and explain complex reasoning step-by-step","I want to solve a hard math or logic problem and need deeper reasoning","I need to debug a complex system and want the model to explore multiple hypotheses"],"best_for":["developers building AI tutoring or educational systems","teams working on complex reasoning tasks (math, logic, code analysis)","researchers evaluating model reasoning capabilities"],"limitations":["Thinking mode increases latency by 2-5x depending on problem complexity","Thinking tokens count against context window, reducing available space for input/output","No guarantee that thinking mode will improve accuracy on all task types","Thinking process is opaque to caller — only final output is returned by default"],"requires":["API parameter support for 'thinking' or 'reasoning' mode configuration","Sufficient context budget (thinking tokens + input + output must fit in 256K window)","Tolerance for increased latency (typically 5-15 seconds for complex reasoning)"],"input_types":["text (problem statement, question, or task description)"],"output_types":["text (final answer with optional reasoning trace)","structured reasoning (if API exposes thinking tokens)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-31b-it__cap_2","uri":"capability://tool.use.integration.native.function.calling.with.schema.based.tool.binding","name":"native function calling with schema-based tool binding","description":"Implements OpenAI-compatible function calling interface where the model can request execution of external tools by generating structured function calls based on a provided schema registry. The model learns to map natural language intents to function signatures, parameter types, and argument values during training. Supports multiple concurrent function calls per response and integrates with standard tool-use patterns (function name, arguments object, return value handling).","intents":["I want the model to call APIs or local functions to fetch real-time data","I need the model to control external systems like databases or file systems","I want to build an agentic workflow where the model decides which tools to use"],"best_for":["developers building AI agents with external tool access","teams integrating LLMs into existing API ecosystems","builders creating autonomous workflows that require tool orchestration"],"limitations":["Function calling adds ~100-200ms latency per tool invocation due to schema validation and parsing","Model may hallucinate function names or parameters not in the schema — requires strict validation on caller side","No built-in retry logic for failed function calls — caller must implement error handling and re-prompting","Schema complexity affects model accuracy — overly complex schemas reduce function-calling reliability"],"requires":["JSON schema definitions for all available functions","API client or runtime capable of executing called functions","Error handling logic to catch and report function execution failures back to model"],"input_types":["text (natural language request)","JSON schema (function definitions)"],"output_types":["function calls (structured JSON with function name and arguments)","text (natural language response interspersed with function calls)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-31b-it__cap_3","uri":"capability://text.generation.language.dense.31b.parameter.inference.with.256k.context.window","name":"dense 31b parameter inference with 256k context window","description":"A 30.7 billion parameter dense transformer model optimized for efficient inference on commodity hardware and cloud accelerators. The 256K token context window is achieved through efficient attention mechanisms (likely grouped query attention or similar) that reduce memory overhead while maintaining full context awareness. The dense architecture (no mixture-of-experts) ensures predictable latency and memory usage without routing overhead.","intents":["I need a capable model that runs faster than 70B+ models but smarter than 7B models","I want to process long documents (50K+ tokens) without losing context","I need predictable inference costs and latency for production systems"],"best_for":["teams deploying models on cost-constrained infrastructure","developers building real-time applications requiring <2 second latency","organizations processing long-form documents (research papers, books, code repositories)"],"limitations":["31B parameters is smaller than GPT-3.5 (175B equivalent) — may struggle with highly specialized domains","Dense architecture means no dynamic computation scaling — always uses full 31B parameters regardless of task complexity","256K context window is large but still smaller than some competitors (Claude 3.5 Sonnet: 200K, GPT-4 Turbo: 128K) — may truncate very long documents","Inference latency on CPU-only systems will be prohibitive (likely requires GPU/TPU)"],"requires":["GPU or TPU with sufficient VRAM (likely 16GB+ for full model in FP16, 8GB+ in INT8 quantization)","API access via OpenRouter or Google's endpoints (no local inference mentioned)","Batch size optimization for throughput vs latency tradeoffs"],"input_types":["text (up to 256K tokens)"],"output_types":["text (generated response)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-31b-it__cap_4","uri":"capability://safety.moderation.instruction.tuned.response.generation.with.safety.alignment","name":"instruction-tuned response generation with safety alignment","description":"The 'IT' (Instruction-Tuned) variant is fine-tuned on instruction-following datasets and RLHF (reinforcement learning from human feedback) to produce helpful, harmless, and honest responses. The model learns to refuse harmful requests, acknowledge uncertainty, and provide structured outputs when appropriate. Safety training is integrated into the model weights rather than applied as a post-hoc filter, enabling more nuanced safety decisions.","intents":["I need the model to refuse harmful requests without breaking the conversation","I want reliable, factual responses that acknowledge when the model is uncertain","I need the model to follow complex instructions without hallucinating capabilities"],"best_for":["teams deploying models in production where safety is critical","developers building customer-facing applications requiring trust","organizations subject to compliance requirements (healthcare, finance, legal)"],"limitations":["Safety training may cause the model to refuse legitimate requests if they superficially resemble harmful ones","Refusal behavior is not configurable per-request — cannot easily override safety guardrails","Safety alignment is opaque — difficult to audit exactly what triggers refusals","Over-cautious refusals may reduce usefulness on edge-case legitimate tasks"],"requires":["Understanding of model's safety boundaries before deployment","User communication strategy for handling refusals gracefully","Monitoring and logging of refusal patterns to catch over-cautious behavior"],"input_types":["text (natural language instructions)"],"output_types":["text (helpful response or refusal with explanation)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-31b-it__cap_5","uri":"capability://automation.workflow.batch.inference.with.variable.length.input.handling","name":"batch inference with variable-length input handling","description":"Supports efficient batch processing of multiple requests with different input lengths through dynamic padding and attention masking. The model can process heterogeneous batch sizes (e.g., 5 short queries and 3 long documents in the same batch) without padding all inputs to the longest sequence length. This is achieved through efficient attention implementations that skip padding tokens and optimize memory layout.","intents":["I need to process thousands of documents efficiently without waiting for sequential inference","I want to maximize GPU utilization by batching requests of varying lengths","I need to reduce per-request latency by amortizing model loading costs"],"best_for":["teams processing large document corpora (search indexing, content moderation)","developers building batch processing pipelines for analytics","organizations optimizing inference costs through batching"],"limitations":["Batch processing introduces latency (typically 5-30 seconds per batch) — unsuitable for real-time applications","Memory overhead increases with batch size — maximum batch size depends on available VRAM","Variable-length batching adds complexity to request scheduling and result ordering","No built-in batching API — requires custom orchestration or third-party batch service"],"requires":["Batch orchestration layer (custom code or service like Replicate, Modal, or Baseten)","Sufficient VRAM to hold multiple sequences in memory simultaneously (typically 32GB+ for large batches)","Tolerance for latency (batch collection time + inference time)"],"input_types":["text (variable-length sequences, up to 256K tokens each)"],"output_types":["text (generated responses, one per input)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemma-4-31b-it__cap_6","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"The model can be constrained to generate outputs matching a provided JSON schema, ensuring structured data extraction without post-processing. This is implemented through constrained decoding where the model's token generation is restricted to valid continuations that maintain schema compliance. The model learns during training to map natural language to structured outputs, and inference-time constraints prevent invalid JSON or schema violations.","intents":["I need to extract structured data from unstructured text reliably","I want to generate API responses that always conform to a specific schema","I need to parse natural language into database records without manual validation"],"best_for":["developers building data extraction pipelines","teams integrating LLMs into structured APIs","organizations requiring guaranteed output format compliance"],"limitations":["Constrained decoding adds ~50-150ms latency per request due to schema validation overhead","Complex schemas may reduce model accuracy — overly strict constraints can force incorrect data into wrong fields","Schema must be defined upfront — cannot dynamically generate schemas based on input","Model may struggle with ambiguous mappings between natural language and schema fields"],"requires":["JSON schema definition for desired output structure","API support for schema-constrained generation (not all inference endpoints support this)","Validation logic to handle edge cases where constrained generation fails"],"input_types":["text (natural language or unstructured data)","JSON schema (output structure definition)"],"output_types":["JSON (guaranteed to match provided schema)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or Google's inference endpoints","Images in standard formats (JPEG, PNG, WebP, GIF)","Base64 encoding or URL-accessible image URIs for API submission","API parameter support for 'thinking' or 'reasoning' mode configuration","Sufficient context budget (thinking tokens + input + output must fit in 256K window)","Tolerance for increased latency (typically 5-15 seconds for complex reasoning)","JSON schema definitions for all available functions","API client or runtime capable of executing called functions","Error handling logic to catch and report function execution failures back to model","GPU or TPU with sufficient VRAM (likely 16GB+ for full model in FP16, 8GB+ in INT8 quantization)"],"failure_modes":["Image encoding adds ~500-800ms latency compared to text-only inference","No native support for video input despite tag mention — only static images","Image resolution capped at typical transformer input sizes (likely 1024x1024 or 2048x2048)","Cannot generate images, only analyze them","Thinking mode increases latency by 2-5x depending on problem complexity","Thinking tokens count against context window, reducing available space for input/output","No guarantee that thinking mode will improve accuracy on all task types","Thinking process is opaque to caller — only final output is returned by default","Function calling adds ~100-200ms latency per tool invocation due to schema validation and parsing","Model may hallucinate function names or parameters not in the schema — requires strict validation on caller side","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.3,"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":"2026-05-03T15:20:45.775Z","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-4-31b-it","compare_url":"https://unfragile.ai/compare?artifact=google-gemma-4-31b-it"}},"signature":"w1+5Gqeo1hs5Z35Zj04koymSj60r0QGXQUFumE50xRL7z4tcKKb3jP0IXC6P6Fipq9zpSxKvpWAICdwnMYuoDw==","signedAt":"2026-06-20T05:32:54.993Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemma-4-31b-it","artifact":"https://unfragile.ai/google-gemma-4-31b-it","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemma-4-31b-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"}}