{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-google-gemini-2.0-flash-001","slug":"google-gemini-2.0-flash-001","name":"Google: Gemini 2.0 Flash","type":"model","url":"https://openrouter.ai/models/google~gemini-2.0-flash-001","page_url":"https://unfragile.ai/google-gemini-2.0-flash-001","categories":["image-generation","testing-quality"],"tags":["google","api-access","text","image","audio","video"],"pricing":{"model":"paid","free":false,"starting_price":"$1.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-google-gemini-2.0-flash-001__cap_0","uri":"capability://image.visual.multi.modal.input.processing.with.unified.embedding.space","name":"multi-modal input processing with unified embedding space","description":"Processes text, images, audio, and video inputs through a shared transformer-based architecture that maps all modalities into a unified embedding space, enabling seamless cross-modal reasoning without separate encoding pipelines. The model uses interleaved attention mechanisms to handle variable-length sequences across modalities, allowing queries that reference multiple input types simultaneously (e.g., 'describe the objects in this image and relate them to the audio transcript').","intents":["I need to analyze an image and cross-reference it with a text document in a single API call","I want to process video with audio and extract insights that require understanding both modalities together","I need to build a chatbot that can handle mixed-media conversations without separate preprocessing steps"],"best_for":["teams building document intelligence systems with mixed media","developers creating accessibility tools that need to correlate visual and audio content","researchers prototyping multimodal reasoning applications"],"limitations":["Video input limited to ~1 hour duration per request","Audio processing requires 16kHz+ sample rate; lower rates may degrade accuracy","Cross-modal reasoning latency increases with input complexity (4-8 second TTFT for dense video+audio+text)","No fine-tuning support for custom modality weights or domain-specific embeddings"],"requires":["Google Cloud API key or OpenRouter API key","Video files in MP4, WebM, or MOV format","Audio in WAV, MP3, or OGG format","Images in JPEG, PNG, GIF, or WebP format"],"input_types":["text (up to 1M tokens)","image (up to 20MB per image)","audio (up to 1 hour)","video (up to 1 hour)"],"output_types":["text","structured JSON","markdown with formatting"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_1","uri":"capability://text.generation.language.optimized.low.latency.text.generation.with.speculative.decoding","name":"optimized low-latency text generation with speculative decoding","description":"Implements speculative decoding with a lightweight draft model that predicts multiple future tokens in parallel, which are then validated by the main model in a single forward pass, reducing latency by ~40-50% compared to standard autoregressive generation. The architecture uses a two-stage pipeline: draft generation (fast, approximate) followed by verification (accurate, batch-validated), enabling significantly faster time-to-first-token (TTFT) while maintaining output quality parity with larger models.","intents":["I need sub-100ms TTFT for real-time chat applications with high user concurrency","I want to stream responses faster without sacrificing output quality or coherence","I need to reduce API latency for interactive coding assistants and autocomplete features"],"best_for":["teams building real-time chat interfaces with strict latency budgets (<200ms)","developers creating interactive coding tools where TTFT directly impacts UX","companies optimizing inference costs by reducing token generation time"],"limitations":["Speculative decoding adds ~15-20MB memory overhead for draft model weights","Latency improvements diminish for very short responses (<50 tokens) where draft overhead dominates","No control over draft model selection or speculation depth from API","Batch processing may reduce per-request latency gains due to shared draft model contention"],"requires":["API key for Google Cloud or OpenRouter","Network latency <50ms to inference endpoint for optimal TTFT benefits","Streaming-capable HTTP client (Server-Sent Events support)"],"input_types":["text (up to 1M tokens)"],"output_types":["text (streamed via SSE)","text (buffered response)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_10","uri":"capability://safety.moderation.safety.aware.content.generation.with.configurable.guardrails","name":"safety-aware content generation with configurable guardrails","description":"Generates content while respecting configurable safety policies that prevent generation of harmful, illegal, or policy-violating content, using a combination of input filtering, output classification, and probabilistic rejection sampling. The model can be configured with custom safety thresholds for categories like violence, hate speech, sexual content, and misinformation, enabling organizations to enforce domain-specific safety policies without fine-tuning.","intents":["I need to ensure generated content complies with my organization's safety and compliance policies","I want to prevent the model from generating harmful content while maintaining creative freedom for legitimate use cases","I need to audit and log safety decisions for compliance and transparency"],"best_for":["teams building public-facing applications with strict safety requirements","companies in regulated industries (finance, healthcare, education) needing compliance guarantees","developers creating content moderation or safety monitoring systems"],"limitations":["Safety filtering adds ~100-200ms latency per request due to classification overhead","False positive rate ~5-10% for borderline content; legitimate content may be rejected","Custom safety policies require manual configuration; no automatic learning from feedback","Safety decisions not fully explainable; no detailed reasoning for rejections"],"requires":["API key for Google Cloud or OpenRouter","Safety policy configuration (optional; defaults to Google's standard policies)","Understanding of safety categories and threshold trade-offs"],"input_types":["text (prompts and instructions)"],"output_types":["text (generated content or rejection message)","structured JSON (safety classification scores)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_2","uri":"capability://code.generation.editing.context.aware.code.generation.and.analysis.with.language.agnostic.ast.reasoning","name":"context-aware code generation and analysis with language-agnostic ast reasoning","description":"Generates and analyzes code across 50+ programming languages by reasoning over abstract syntax trees (ASTs) rather than token sequences, enabling structurally-aware refactoring, bug detection, and completion that respects language semantics. The model uses a hybrid approach: token-level understanding for natural language context combined with AST-level reasoning for code structure, allowing it to generate syntactically valid code that maintains type safety and architectural patterns without explicit linting.","intents":["I need to generate code that respects my project's existing patterns and architecture without manual fixes","I want to refactor large codebases while preserving semantics and catching type errors before runtime","I need to debug code by analyzing control flow and data dependencies, not just pattern matching"],"best_for":["teams maintaining large polyglot codebases (Python, Go, Rust, TypeScript, etc.)","developers building code review automation that needs semantic understanding","companies automating legacy code modernization with structural guarantees"],"limitations":["AST reasoning adds ~200-300ms latency per code analysis request vs. pure token-based models","No built-in support for domain-specific languages (DSLs) without explicit grammar definition","Type inference limited to languages with explicit type annotations; dynamic languages (Python, JavaScript) require runtime context","Cannot guarantee type safety for generated code without external type checker integration"],"requires":["API key for Google Cloud or OpenRouter","Code context up to 1M tokens (full file or codebase snapshot)","Optional: language-specific type stubs or type definitions for improved accuracy"],"input_types":["text (source code)","text (natural language instructions)","text (error messages or test failures)"],"output_types":["text (source code)","text (refactoring suggestions)","structured JSON (AST analysis results)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_3","uri":"capability://image.visual.image.understanding.and.visual.reasoning.with.fine.grained.spatial.awareness","name":"image understanding and visual reasoning with fine-grained spatial awareness","description":"Analyzes images through a vision transformer backbone that maintains spatial locality information, enabling precise localization of objects, text, and regions without requiring bounding box annotations. The model performs dense visual reasoning by attending to specific image regions while maintaining global context, supporting tasks like OCR, scene understanding, and visual question-answering with sub-pixel accuracy for text extraction and object detection.","intents":["I need to extract text from documents, screenshots, or photos with high accuracy and preserve formatting","I want to identify and locate specific objects or regions in images for automated processing","I need to answer questions about image content that require understanding spatial relationships and context"],"best_for":["teams building document processing pipelines (invoices, receipts, forms)","developers creating visual search or image annotation systems","companies automating quality control through visual inspection"],"limitations":["OCR accuracy degrades for handwritten text or non-Latin scripts (accuracy ~85% vs. 99% for printed text)","Image resolution limited to 20MB; very high-resolution images (>8K) require downsampling","No native support for 3D spatial reasoning or depth estimation from single images","Bounding box output requires post-processing; no native format for structured spatial annotations"],"requires":["API key for Google Cloud or OpenRouter","Images in JPEG, PNG, GIF, or WebP format","Image dimensions between 32x32 and 8192x8192 pixels"],"input_types":["image (JPEG, PNG, GIF, WebP)","text (natural language questions or instructions)"],"output_types":["text (descriptions, OCR results)","structured JSON (object locations, extracted data)","markdown (formatted text extraction)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_4","uri":"capability://data.processing.analysis.audio.transcription.and.speech.understanding.with.speaker.diarization","name":"audio transcription and speech understanding with speaker diarization","description":"Transcribes audio to text while simultaneously identifying speaker boundaries and attributing speech segments to individual speakers, using a multi-task learning approach that jointly optimizes for transcription accuracy and speaker separation. The model handles variable audio quality, background noise, and multiple speakers without requiring explicit speaker enrollment or training data, producing timestamped transcripts with speaker labels and confidence scores.","intents":["I need to transcribe meetings or interviews and know who said what without manual annotation","I want to extract actionable insights from audio content while preserving speaker context","I need to process podcast or video audio with automatic speaker identification for downstream analysis"],"best_for":["teams building meeting intelligence or call center analytics platforms","developers creating podcast or video processing pipelines","companies automating interview transcription with speaker attribution"],"limitations":["Speaker diarization accuracy drops below 80% for >4 simultaneous speakers or heavy overlapping speech","Audio quality requirements: 16kHz+ sample rate; lower rates degrade accuracy by 10-15%","No speaker identification (matching speakers across files); only within-file diarization","Timestamping accuracy ±200ms; not suitable for precise audio-visual synchronization"],"requires":["API key for Google Cloud or OpenRouter","Audio in WAV, MP3, OGG, or FLAC format","Audio duration up to 1 hour per request","Minimum 16kHz sample rate (44.1kHz or higher recommended)"],"input_types":["audio (WAV, MP3, OGG, FLAC)"],"output_types":["text (plain transcript)","structured JSON (timestamped segments with speaker labels)","VTT/SRT (subtitle format with speaker attribution)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_5","uri":"capability://image.visual.video.understanding.with.temporal.reasoning.and.scene.segmentation","name":"video understanding with temporal reasoning and scene segmentation","description":"Analyzes video by sampling keyframes and reasoning over temporal relationships between scenes, enabling understanding of narrative flow, action sequences, and scene transitions without processing every frame. The model uses a hierarchical attention mechanism that first identifies scene boundaries, then reasons about temporal dependencies within and across scenes, producing structured summaries that capture plot progression, key events, and visual changes.","intents":["I need to summarize video content and extract key moments without watching the entire video","I want to understand narrative structure and identify scene transitions for video editing or analysis","I need to search for specific events or objects within video by temporal location"],"best_for":["teams building video content management or search platforms","developers creating automated video summarization or highlight extraction tools","companies analyzing surveillance or instructional video content at scale"],"limitations":["Temporal reasoning limited to ~1 hour of video; longer videos require segmentation","Scene detection accuracy depends on visual distinctiveness; similar scenes may be merged","No frame-level precision; temporal annotations accurate to ±1-2 seconds","Action recognition limited to common activities; domain-specific actions require fine-tuning"],"requires":["API key for Google Cloud or OpenRouter","Video in MP4, WebM, or MOV format","Video duration up to 1 hour per request","Minimum 24fps frame rate"],"input_types":["video (MP4, WebM, MOV)","text (natural language questions about video content)"],"output_types":["text (video summary, scene descriptions)","structured JSON (scene boundaries with timestamps, key events)","markdown (formatted summary with temporal references)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_6","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.guided.generation","name":"structured data extraction with schema-guided generation","description":"Extracts structured information from unstructured text or images by generating output that conforms to a user-provided JSON schema, using constrained decoding to ensure valid schema compliance without post-processing. The model uses a schema-aware attention mechanism that biases token generation toward valid schema fields and values, enabling reliable extraction of complex nested structures (e.g., invoice line items with nested tax calculations) with guaranteed schema validity.","intents":["I need to extract invoice data (amounts, dates, vendor info) into a structured format for accounting systems","I want to parse form responses or survey data into a database schema without manual validation","I need to convert unstructured documents into structured records for downstream processing"],"best_for":["teams building document processing or data entry automation","developers creating form parsing or data extraction pipelines","companies automating data migration or ETL processes"],"limitations":["Schema complexity limited to ~100 fields; deeply nested schemas (>5 levels) may reduce accuracy","Extraction accuracy depends on schema clarity; ambiguous field definitions reduce precision","No support for conditional schemas or dynamic field requirements","Constrained decoding adds ~50-100ms latency per extraction request"],"requires":["API key for Google Cloud or OpenRouter","JSON schema defining extraction structure","Input text or image containing data to extract"],"input_types":["text (unstructured documents)","image (documents, forms, screenshots)"],"output_types":["structured JSON (schema-compliant extraction)","text (validation errors if schema constraints violated)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_7","uri":"capability://planning.reasoning.few.shot.learning.with.in.context.example.optimization","name":"few-shot learning with in-context example optimization","description":"Learns from a small number of input-output examples provided in the prompt (typically 2-5 examples) and applies learned patterns to new inputs, using an in-context learning mechanism that dynamically weights examples based on semantic similarity to the query. The model identifies relevant examples from the provided set and adapts its reasoning to match the demonstrated pattern, enabling task adaptation without fine-tuning or model updates.","intents":["I need to classify text or data using custom categories without training a separate model","I want to adapt the model's output format or style to match my specific requirements through examples","I need to perform domain-specific tasks (e.g., medical coding, legal analysis) by showing a few examples"],"best_for":["teams prototyping custom classification or extraction tasks quickly","developers building adaptable systems that need to handle domain-specific variations","companies avoiding fine-tuning overhead by using in-context learning"],"limitations":["Few-shot performance plateaus at 5-10 examples; adding more examples doesn't improve accuracy proportionally","Example quality critically impacts performance; poor examples degrade accuracy by 20-30%","No explicit mechanism to weight or prioritize examples; relevance is implicit","Context window usage increases linearly with example count, reducing space for input data"],"requires":["API key for Google Cloud or OpenRouter","2-5 representative input-output examples in the prompt","Clear task description or implicit pattern in examples"],"input_types":["text (examples and query)"],"output_types":["text (adapted output following example patterns)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_8","uri":"capability://memory.knowledge.long.context.reasoning.with.1m.token.window.and.efficient.attention","name":"long-context reasoning with 1m-token window and efficient attention","description":"Processes up to 1 million tokens (roughly 750,000 words or 100+ documents) in a single request using efficient attention mechanisms (e.g., sparse attention, hierarchical attention) that reduce memory and compute requirements while maintaining reasoning quality. The model can analyze entire codebases, long documents, or multiple files simultaneously without context truncation, enabling holistic understanding of large information spaces.","intents":["I need to analyze an entire codebase to understand architecture and identify refactoring opportunities","I want to process multiple documents or books together to find cross-document relationships","I need to maintain conversation history over hundreds of turns without losing context"],"best_for":["teams analyzing large codebases or documentation repositories","developers building research tools that need to correlate information across many documents","companies processing long-form content (books, legal documents, technical specifications)"],"limitations":["Latency increases with context size; 1M-token requests take 5-10 seconds vs. 1-2 seconds for 10K tokens","Attention quality degrades for information in the middle of context (lost-in-the-middle effect); critical info should be at start/end","Memory requirements scale linearly with context; 1M-token requests require 32GB+ GPU memory","Cost scales with token count; 1M-token request costs ~$5-10 vs. $0.01 for 1K tokens"],"requires":["API key for Google Cloud or OpenRouter","Input up to 1M tokens (text, code, or document content)","Patience for 5-10 second latency on maximum-size requests"],"input_types":["text (up to 1M tokens)"],"output_types":["text (analysis, summary, or response)"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-google-gemini-2.0-flash-001__cap_9","uri":"capability://tool.use.integration.function.calling.with.multi.provider.schema.support.and.automatic.retry","name":"function calling with multi-provider schema support and automatic retry","description":"Invokes external functions or APIs by generating structured function calls that conform to OpenAI, Anthropic, or custom schema formats, with built-in retry logic that automatically re-invokes functions if they fail or return incomplete results. The model reasons about which functions to call, in what order, and with what arguments, supporting complex multi-step workflows without explicit orchestration code.","intents":["I need to build an agent that can call APIs or tools to complete tasks (e.g., search, calculate, fetch data)","I want to enable the model to take actions in external systems based on reasoning","I need to handle function failures gracefully without breaking the conversation flow"],"best_for":["teams building AI agents or autonomous systems","developers creating chatbots that need to interact with external APIs","companies automating workflows that require tool use and error recovery"],"limitations":["Function calling latency adds 200-500ms per function invocation due to schema validation and retry logic","No built-in support for parallel function calls; sequential execution required","Retry logic limited to 3 attempts; persistent failures require manual intervention","Schema validation strict; minor deviations from schema cause function call failures"],"requires":["API key for Google Cloud or OpenRouter","Function definitions in OpenAI, Anthropic, or custom JSON schema format","Function implementations accessible via HTTP or local execution"],"input_types":["text (natural language instructions)","structured JSON (function schemas)"],"output_types":["structured JSON (function calls with arguments)","text (reasoning about which functions to call)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"low","permissions":["Google Cloud API key or OpenRouter API key","Video files in MP4, WebM, or MOV format","Audio in WAV, MP3, or OGG format","Images in JPEG, PNG, GIF, or WebP format","API key for Google Cloud or OpenRouter","Network latency <50ms to inference endpoint for optimal TTFT benefits","Streaming-capable HTTP client (Server-Sent Events support)","Safety policy configuration (optional; defaults to Google's standard policies)","Understanding of safety categories and threshold trade-offs","Code context up to 1M tokens (full file or codebase snapshot)"],"failure_modes":["Video input limited to ~1 hour duration per request","Audio processing requires 16kHz+ sample rate; lower rates may degrade accuracy","Cross-modal reasoning latency increases with input complexity (4-8 second TTFT for dense video+audio+text)","No fine-tuning support for custom modality weights or domain-specific embeddings","Speculative decoding adds ~15-20MB memory overhead for draft model weights","Latency improvements diminish for very short responses (<50 tokens) where draft overhead dominates","No control over draft model selection or speculation depth from API","Batch processing may reduce per-request latency gains due to shared draft model contention","Safety filtering adds ~100-200ms latency per request due to classification overhead","False positive rate ~5-10% for borderline content; legitimate content may be rejected","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.43,"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-2.0-flash-001","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-2.0-flash-001"}},"signature":"eXvvvU8jvIi3kXKdga59W+oQF1bqYmPImCS99Xdd/+k6JtzVrI51Im4Q02HMx5tS51URksR7xhsIYnY8c7GmDA==","signedAt":"2026-06-20T05:32:54.023Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-2.0-flash-001","artifact":"https://unfragile.ai/google-gemini-2.0-flash-001","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-2.0-flash-001","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"}}