{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"google-gemini-api","slug":"google-gemini-api","name":"Google Gemini API","type":"api","url":"https://ai.google.dev","page_url":"https://unfragile.ai/google-gemini-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":"$1.25/1M tokens"},"status":"active","verified":false},"capabilities":[{"id":"google-gemini-api__cap_0","uri":"capability://text.generation.language.multimodal.content.generation.with.native.media.fusion","name":"multimodal content generation with native media fusion","description":"Accepts text, images, audio, video, and code in a single request via a unified parts-based content model, processing them through a shared transformer architecture that maintains semantic relationships across modalities. The API uses a standardized contents/parts JSON structure where each part can be a different media type, enabling seamless cross-modal reasoning without separate preprocessing pipelines or format conversion.","intents":["Generate text responses based on images, audio transcripts, and video frames in a single API call","Analyze code snippets alongside natural language context to produce refactoring suggestions","Build applications that understand documents containing mixed text, diagrams, and embedded media","Process video content with audio to extract insights without manual frame extraction"],"best_for":["Teams building document understanding systems with mixed media","Developers creating accessibility tools that process audio and video","Builders of code analysis tools that need visual context (screenshots, diagrams)"],"limitations":["Specific file format and size constraints for audio/video/image inputs not documented","No explicit support for streaming multimodal inputs — all media must be provided upfront","Audio processing requires pre-encoded formats; real-time audio streaming not documented"],"requires":["API key from Google AI Studio","Multimodal input files in supported formats (specific formats undocumented)","One of: Python google.genai SDK, JavaScript @google/genai, Go/Java/C# SDKs, or REST HTTP"],"input_types":["text","image (format/size constraints unknown)","audio (format/size constraints unknown)","video (format/size constraints unknown)","code (as text or embedded in documents)"],"output_types":["text","structured JSON (via structured output capability)"],"categories":["text-generation-language","image-visual","multimodal-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_1","uri":"capability://text.generation.language.1m.token.context.window.with.tiered.pricing","name":"1m+ token context window with tiered pricing","description":"Supports prompts and responses up to 1 million tokens through a transformer architecture optimized for long-context attention. Pricing is tiered at the 200K token boundary, with input costs doubling and output costs increasing 50% for contexts exceeding 200K tokens, incentivizing efficient context management while enabling retrieval-augmented generation with full document sets.","intents":["Process entire codebases (100K+ lines) in a single request for refactoring or analysis","Analyze complete research papers, books, or legal documents without chunking","Build RAG systems where full document sets fit in a single context window","Maintain multi-turn conversations with extensive conversation history"],"best_for":["Teams analyzing large codebases or documents where chunking introduces context loss","Builders of document-centric AI applications (legal tech, research tools)","Developers implementing RAG systems with cost-conscious token budgets"],"limitations":["Pricing doubles for input tokens >200K ($4/1M vs $2/1M standard tier), creating cost cliffs","Output token pricing increases 50% for >200K context ($18/1M vs $12/1M standard tier)","No documented latency SLA for 1M token requests — processing time likely increases significantly","Context caching (to reduce repeated context costs) requires paid tier and adds storage costs ($4.50/1M/hour)"],"requires":["API key with paid tier access (free tier limits unknown but likely <1M tokens)","Sufficient API quota for large token volumes","Client-side token counting to avoid exceeding limits"],"input_types":["text (up to 1M tokens)","multimodal content (text + images/audio/video, total up to 1M tokens)"],"output_types":["text (up to model's max output tokens, typically 4K-8K)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_10","uri":"capability://planning.reasoning.agentic.planning.and.multi.step.execution","name":"agentic planning and multi-step execution","description":"Enables the model to decompose complex tasks into multiple steps, decide which tools to call at each step, and execute a plan across multiple API calls. The model reasons about task decomposition, tool selection, and execution order, with the client orchestrating the execution loop by feeding tool results back to the model for the next step.","intents":["Build AI agents that solve complex problems requiring multiple steps and tool calls","Create chatbots that can plan and execute multi-step workflows","Implement research assistants that gather information from multiple sources and synthesize results","Build automation systems that decide which actions to take based on current state"],"best_for":["Teams building AI agents or autonomous systems","Developers creating complex chatbots with multi-step workflows","Builders of research or analysis tools that need to gather and synthesize information"],"limitations":["Agentic planning implementation details not documented — no guidance on prompt patterns or best practices","No built-in agent loop orchestration — client must implement the execution loop","No built-in error recovery or replanning — client responsible for handling tool failures","No explicit support for parallel tool execution — tools executed sequentially","Token usage can grow quickly with multi-step planning — each step adds to context"],"requires":["API key with function calling support","Client-side agent loop implementation (orchestration logic)","Tool definitions and execution logic","SDK support for function calling (Python google.genai, JavaScript @google/genai, etc.)"],"input_types":["complex task description","tool definitions","tool execution results from previous steps"],"output_types":["tool calls (function name + parameters)","final text response after all steps complete"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_11","uri":"capability://text.generation.language.multi.language.support.across.24.languages","name":"multi-language support across 24+ languages","description":"Supports generation and understanding in 24+ languages including English, German, Spanish, French, Indonesian, Italian, Polish, Portuguese, Turkish, Russian, Hebrew, Arabic, Persian, Hindi, Bengali, Thai, Simplified Chinese, Traditional Chinese, Japanese, Korean, and others. The model handles language detection, translation, and code-switching without explicit language specification, enabling multilingual applications.","intents":["Build chatbots that serve users in multiple languages","Generate content in different languages from a single API","Analyze documents or user input in non-English languages","Create applications that support language-agnostic user interactions"],"best_for":["Teams building global applications serving multiple language markets","Developers creating multilingual chatbots or content generation systems","Builders of international customer support or analysis tools"],"limitations":["Language support list not exhaustive — only 24+ languages documented, others may or may not be supported","Language detection is automatic — no explicit language specification in API","Translation quality and accuracy not documented","Code-switching (mixing languages) behavior not documented","Right-to-left language support (Arabic, Hebrew, Persian) not explicitly confirmed"],"requires":["API key (language support available on all tiers)","Input in supported language (language detection automatic)"],"input_types":["text in any of 24+ supported languages","multimodal content (images/audio/video with text in supported languages)"],"output_types":["text in the same language as input (or specified target language)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_12","uri":"capability://text.generation.language.on.device.inference.with.gemini.nano","name":"on-device inference with gemini nano","description":"Provides Gemini Nano, a lightweight model optimized for on-device execution on Android and Chrome platforms, enabling low-latency, privacy-preserving inference without cloud API calls. The model runs directly on the user's device, eliminating network latency and keeping data local, though with reduced capabilities compared to cloud Gemini models.","intents":["Build mobile apps with instant AI responses without network latency","Create privacy-focused applications where user data never leaves the device","Implement offline-capable AI features that work without internet connectivity","Reduce cloud API costs by processing simple tasks on-device"],"best_for":["Mobile app developers building Android or Chrome applications","Teams with privacy-sensitive use cases where data cannot leave the device","Developers building offline-capable AI features"],"limitations":["Limited to Android and Chrome platforms — no iOS, Windows, or macOS support","Reduced model capabilities compared to cloud Gemini models — specific limitations not documented","No multimodal support documented — unclear if Nano supports images/audio/video","No function calling or tool use documented for Nano","Device memory and compute constraints not documented","Model updates and versioning not documented"],"requires":["Android device or Chrome browser","Gemini Nano SDK (specific SDK name and version not documented)","Sufficient device storage and memory for model (size not documented)"],"input_types":["text (multimodal support unknown)"],"output_types":["text"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_13","uri":"capability://text.generation.language.free.tier.with.limited.models.and.token.quotas","name":"free tier with limited models and token quotas","description":"Provides free API access via Google AI Studio with limited model availability (only 'some' models), free input and output tokens (quota limits unknown), and content used for product improvement. The free tier enables prototyping and low-volume use without payment, though with restrictions on model selection, token quotas, and data privacy.","intents":["Prototype AI applications before committing to paid tier","Build low-volume hobby projects or personal tools","Test Gemini API capabilities before production deployment","Learn and experiment with multimodal AI without cost"],"best_for":["Individual developers and hobbyists prototyping AI applications","Teams evaluating Gemini API before committing to paid tier","Students and researchers experimenting with AI"],"limitations":["Only 'some' models available — specific model list not documented, likely excludes latest/most capable models","Token quotas unknown — 'ample limits' mentioned but specific numbers not provided","Content used for product improvement — data privacy concern for sensitive applications","No context caching — cannot optimize costs for repeated contexts","No batch API — cannot use 50% cost reduction for bulk processing","No grounding features beyond free quota (5,000 queries/month shared with paid tier)","No priority tier access — requests processed at lowest priority"],"requires":["Google account","Access to Google AI Studio (free, no credit card required)"],"input_types":["text","multimodal content (images/audio/video, if supported on free tier)"],"output_types":["text"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_14","uri":"capability://automation.workflow.priority.tier.with.3.6x.standard.pricing.for.guaranteed.latency","name":"priority tier with 3.6x standard pricing for guaranteed latency","description":"Provides a Priority tier with 3.6x standard pricing that guarantees lower latency and higher throughput for time-sensitive applications. Requests are processed with higher priority in the queue, reducing wait times and enabling consistent sub-second response times for production applications that require predictable performance.","intents":["Build production chatbots or customer-facing applications requiring sub-second responses","Create real-time interactive applications where latency is critical","Implement high-volume applications with strict SLA requirements","Ensure consistent performance during traffic spikes"],"best_for":["Teams building production customer-facing applications","Developers creating real-time interactive systems","Builders of high-volume applications with strict SLA requirements"],"limitations":["3.6x cost multiplier makes Priority tier expensive for high-volume applications","Latency SLA not documented — specific response time guarantees unknown","Throughput limits not documented — maximum requests per minute unknown","No documented difference in model capabilities or output quality","Context caching and batch API still available but may not be cost-effective at Priority pricing"],"requires":["Paid tier API key with Priority tier enabled","Sufficient budget for 3.6x cost multiplier","Production application with latency requirements"],"input_types":["text","multimodal content (text + images/audio/video)"],"output_types":["text"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_15","uri":"capability://automation.workflow.enterprise.tier.with.provisioned.throughput.and.volume.discounts","name":"enterprise tier with provisioned throughput and volume discounts","description":"Provides an Enterprise tier with provisioned throughput (custom capacity reserved for the customer), volume-based discounts (custom pricing based on usage), and dedicated support. Enterprises can negotiate custom SLAs, guaranteed capacity, and discounted per-token rates based on volume commitments.","intents":["Deploy large-scale AI applications with guaranteed capacity","Negotiate volume discounts for high-volume production deployments","Access dedicated support and custom SLAs","Ensure consistent performance and availability for mission-critical applications"],"best_for":["Large enterprises with high-volume AI deployments","Teams requiring guaranteed capacity and custom SLAs","Organizations with mission-critical AI applications"],"limitations":["Pricing and terms not publicly documented — requires direct negotiation with Google","Minimum volume commitments likely required","Custom SLAs and support terms not standardized","Provisioned throughput costs not documented","Volume discount tiers not documented"],"requires":["Direct engagement with Google Cloud sales team","Volume commitment and custom contract negotiation","Enterprise Google Cloud account"],"input_types":["text","multimodal content (text + images/audio/video)"],"output_types":["text"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_2","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.registry","name":"function calling with schema-based tool registry","description":"Enables the model to invoke external functions by declaring tool schemas (function signatures, parameters, descriptions) in the request, with the API returning structured tool calls that clients execute and feed back as tool results. The implementation uses a schema-based registry pattern where tools are defined declaratively, allowing the model to reason about which tools to call and in what order without hardcoded tool logic.","intents":["Build AI agents that can call APIs, databases, or custom functions to gather information","Create chatbots that can execute code, query databases, or trigger workflows","Implement multi-step workflows where the model decides which tools to use and in what sequence","Enable the model to take actions in external systems (create tickets, send emails, update records)"],"best_for":["Teams building agentic AI systems with external tool dependencies","Developers creating chatbots that need to interact with APIs or databases","Builders of workflow automation tools where the model decides execution paths"],"limitations":["Function calling implementation details not documented — schema format, validation rules, and error handling unknown","No documented support for streaming function calls or parallel tool execution","Tool execution is synchronous — client must execute tool and return result before model continues","No built-in tool result validation or error recovery — client responsible for handling tool failures"],"requires":["API key with function calling support (available on paid tier, free tier support unknown)","Client-side tool execution logic — API only returns tool call declarations, not execution","SDK support for function calling (Python google.genai, JavaScript @google/genai, etc.)"],"input_types":["text prompt","tool schema definitions (JSON format, exact schema unknown)"],"output_types":["tool call declarations (function name + parameters)","text response (if model chooses not to call tools)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_3","uri":"capability://data.processing.analysis.structured.output.generation.with.json.schema.validation","name":"structured output generation with json schema validation","description":"Constrains model outputs to conform to a provided JSON schema, ensuring responses are valid, parseable structured data suitable for downstream processing. The model generates text that adheres to the schema constraints, with the API validating output before returning it to the client, eliminating the need for post-processing parsing or validation.","intents":["Extract structured data (entities, relationships, classifications) from unstructured text or images","Generate API responses in a specific JSON format without manual parsing","Create forms or data entry systems where the model fills in structured fields","Build pipelines where model outputs feed directly into databases or APIs"],"best_for":["Teams building data extraction or ETL pipelines","Developers creating APIs that need consistent JSON response formats","Builders of form-filling or data entry automation systems"],"limitations":["Schema validation implementation details not documented — constraint types, error handling, and fallback behavior unknown","No documented support for conditional schemas or dynamic schema generation","Schema complexity limits unknown — very large or deeply nested schemas may fail","No explicit support for streaming structured outputs — full output must be generated before validation"],"requires":["API key with structured output support (available on paid tier, free tier support unknown)","JSON schema definition conforming to undocumented schema format","SDK support for structured outputs (Python google.genai, JavaScript @google/genai, etc.)"],"input_types":["text prompt","JSON schema definition","multimodal content (text + images/audio/video)"],"output_types":["JSON object conforming to provided schema"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_4","uri":"capability://search.retrieval.google.search.grounding.with.factual.verification","name":"google search grounding with factual verification","description":"Integrates real-time Google Search results into the generation process, allowing the model to cite current information and ground responses in verifiable sources. The API queries Google Search, retrieves relevant results, and incorporates them into the context before generation, enabling responses about recent events, current prices, or other time-sensitive information that would be outdated in the model's training data.","intents":["Answer questions about current events, news, or recent developments","Provide up-to-date pricing, availability, or product information","Generate responses with citations to authoritative sources","Build chatbots that can verify claims against real-time information"],"best_for":["Teams building question-answering systems that need current information","Developers creating chatbots for customer support or information lookup","Builders of research tools that need to cite authoritative sources"],"limitations":["Free tier limited to 5,000 grounding queries/month (shared with Google Maps grounding)","Paid tier costs $14 per 1,000 queries after free quota exhausted","Search query formulation and result selection logic not documented","No control over which search results are used or how many results are retrieved","Latency impact of search integration not documented — likely adds 500ms-2s per request"],"requires":["API key with Google Search grounding enabled","Paid tier for production use (free tier limited to 5,000 queries/month)","Sufficient API quota for search queries"],"input_types":["text prompt (model automatically formulates search queries)"],"output_types":["text response with citations to search results"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_5","uri":"capability://search.retrieval.google.maps.grounding.for.location.based.context","name":"google maps grounding for location-based context","description":"Integrates Google Maps data (locations, directions, business information, reviews) into the generation process, allowing the model to provide location-aware responses with current business hours, directions, or local information. Similar to Search grounding, the API queries Maps, retrieves relevant location data, and incorporates it into context before generation.","intents":["Answer questions about nearby businesses, restaurants, or services","Provide directions or travel time estimates","Generate responses with current business hours or contact information","Build location-aware chatbots for travel, local services, or navigation"],"best_for":["Teams building location-based chatbots or travel assistants","Developers creating local business lookup or recommendation systems","Builders of navigation or logistics applications"],"limitations":["Free tier limited to 5,000 grounding queries/month (shared with Google Search grounding)","Paid tier costs $14 per 1,000 queries after free quota exhausted","Location query formulation and result selection logic not documented","No control over which Maps results are used or how many results are retrieved","Latency impact of Maps integration not documented — likely adds 500ms-2s per request"],"requires":["API key with Google Maps grounding enabled","Paid tier for production use (free tier limited to 5,000 queries/month)","Sufficient API quota for Maps queries"],"input_types":["text prompt with location context (model automatically formulates Maps queries)"],"output_types":["text response with location information and directions"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_6","uri":"capability://memory.knowledge.context.caching.for.repeated.prompt.reuse","name":"context caching for repeated prompt reuse","description":"Caches large prompt contexts (system instructions, documents, code, etc.) on Google's servers, allowing subsequent requests with the same context to reuse the cached version instead of reprocessing. The API charges a one-time cache write cost ($0.20-0.40/1M tokens depending on context size) plus hourly storage costs ($4.50/1M/hour), with subsequent requests paying only for new input tokens, reducing latency and cost for applications with repeated contexts.","intents":["Build chatbots with large system prompts or knowledge bases that are reused across many conversations","Analyze multiple documents against a fixed set of analysis instructions","Implement RAG systems where the same document set is queried repeatedly","Create code analysis tools that reuse large codebases across multiple analyses"],"best_for":["Teams with high-volume applications that reuse large contexts","Developers building chatbots with extensive system prompts or knowledge bases","Builders of RAG systems with large, stable document sets"],"limitations":["Requires paid tier — not available on free tier","Storage costs ($4.50/1M/hour) accumulate continuously, making long-lived caches expensive","Cache invalidation and update mechanisms not documented","Cache hit detection logic not documented — unclear how the API determines if a context matches a cached version","Minimum cache size not documented — very small contexts may not benefit from caching overhead"],"requires":["Paid tier API key with context caching enabled","Stable, reusable prompt contexts (system instructions, documents, code, etc.)","SDK support for context caching (Python google.genai, JavaScript @google/genai, etc.)"],"input_types":["large prompt context (system instructions, documents, code, etc.)","new input tokens to process against cached context"],"output_types":["text response"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_7","uri":"capability://automation.workflow.batch.processing.api.with.50.cost.reduction","name":"batch processing api with 50% cost reduction","description":"Accepts asynchronous batch requests via a separate Batch API endpoint, processing them at lower priority with 50% cost reduction compared to standard on-demand pricing. Clients submit batches of requests, poll for completion status, and retrieve results asynchronously, enabling cost-effective processing of non-time-sensitive workloads at half the per-token cost.","intents":["Process large volumes of documents or data overnight without paying premium on-demand rates","Analyze thousands of customer support tickets or feedback items cost-effectively","Generate content in bulk (product descriptions, email templates, etc.) with lower costs","Run periodic analysis jobs that don't require real-time responses"],"best_for":["Teams with high-volume, non-time-sensitive processing needs","Developers building content generation pipelines or data analysis workflows","Builders of batch processing systems for customer data or document analysis"],"limitations":["Asynchronous processing — no real-time responses, requires polling for completion","Batch submission and polling mechanism not documented — API contract unknown","Maximum batch size and request limits not documented","Processing latency not documented — could be hours or days depending on queue","No priority queue or expedited batch processing option documented","Context caching still available but storage costs ($4.50/1M/hour) may offset batch savings for long-running batches"],"requires":["Paid tier API key with batch API access","Batch request formatting (exact format not documented)","Client-side polling logic to check batch completion status"],"input_types":["batch of requests (format unknown)","each request can contain text or multimodal content"],"output_types":["batch of responses (format unknown)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_8","uri":"capability://planning.reasoning.extended.reasoning.with.thinking.tokens","name":"extended reasoning with thinking tokens","description":"Enables the model to perform extended reasoning before generating a response by allocating 'thinking tokens' that are used for internal reasoning steps not shown to the user. The model spends thinking tokens on complex reasoning, planning, and verification before producing the final output, improving accuracy on difficult problems at the cost of additional output tokens (thinking tokens are charged at the same rate as regular output tokens).","intents":["Solve complex math problems or logic puzzles with higher accuracy","Generate code for difficult algorithmic problems with better correctness","Analyze complex documents or scenarios that require multi-step reasoning","Improve accuracy on tasks where the model would normally make mistakes"],"best_for":["Teams solving complex reasoning problems (math, logic, algorithms)","Developers building code generation systems for difficult problems","Builders of analysis tools that need high accuracy on complex inputs"],"limitations":["Thinking tokens are charged at full output token rate ($12-32.40/1M depending on tier and context size)","No control over thinking token allocation — model decides how many to use","Thinking process is not visible to the user — only final output is returned","Latency impact not documented — extended reasoning likely adds significant processing time","Thinking token usage not documented — unclear how many tokens are typically used or how to estimate costs"],"requires":["Paid tier API key with extended reasoning support","Complex reasoning tasks that benefit from additional processing","SDK support for thinking tokens (Python google.genai, JavaScript @google/genai, etc.)"],"input_types":["text prompt with complex reasoning requirements","multimodal content (text + images/audio/video)"],"output_types":["text response (thinking process hidden from user)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__cap_9","uri":"capability://code.generation.editing.code.execution.and.verification","name":"code execution and verification","description":"Enables the model to write and execute code (Python, JavaScript, etc.) within the API request, with the execution environment returning results back to the model for verification or iteration. The model can generate code, execute it, see the results, and refine the code based on execution output, enabling more reliable code generation and problem-solving.","intents":["Generate and verify code correctness by executing it and checking output","Solve programming problems by iterating on code based on execution results","Analyze data by writing and executing analysis code","Debug code by running it and examining error messages"],"best_for":["Teams building code generation or programming tutoring systems","Developers creating data analysis tools that need code execution","Builders of debugging or code verification systems"],"limitations":["Supported languages not fully documented — Python and JavaScript mentioned, others unknown","Execution environment sandboxing and security model not documented","Maximum execution time, memory limits, and resource constraints not documented","External library availability not documented — unclear which packages are available","File system access and persistence not documented","Network access from execution environment not documented"],"requires":["API key with code execution support (availability on free tier unknown)","Code generation prompt that triggers code execution","SDK support for code execution (Python google.genai, JavaScript @google/genai, etc.)"],"input_types":["text prompt requesting code generation","data or context for code to analyze"],"output_types":["generated code","code execution results","refined code based on execution feedback"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-gemini-api__headline","uri":"capability://text.generation.language.multimodal.ai.content.generation.api","name":"multimodal ai content generation api","description":"The Google Gemini API offers a powerful, multimodal platform for generating text, images, audio, video, and code, with a massive 1M+ token context window and seamless integration with Google Search.","intents":["best multimodal AI API","multimodal API for content generation","top API for text and image generation","AI API for video and audio content","freemium multimodal AI solutions"],"best_for":["developers seeking a versatile AI API","projects requiring large context windows"],"limitations":[],"requires":["API key for access"],"input_types":["text","images","audio","video","code"],"output_types":["text","images","audio","video"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key from Google AI Studio","Multimodal input files in supported formats (specific formats undocumented)","One of: Python google.genai SDK, JavaScript @google/genai, Go/Java/C# SDKs, or REST HTTP","API key with paid tier access (free tier limits unknown but likely <1M tokens)","Sufficient API quota for large token volumes","Client-side token counting to avoid exceeding limits","API key with function calling support","Client-side agent loop implementation (orchestration logic)","Tool definitions and execution logic","SDK support for function calling (Python google.genai, JavaScript @google/genai, etc.)"],"failure_modes":["Specific file format and size constraints for audio/video/image inputs not documented","No explicit support for streaming multimodal inputs — all media must be provided upfront","Audio processing requires pre-encoded formats; real-time audio streaming not documented","Pricing doubles for input tokens >200K ($4/1M vs $2/1M standard tier), creating cost cliffs","Output token pricing increases 50% for >200K context ($18/1M vs $12/1M standard tier)","No documented latency SLA for 1M token requests — processing time likely increases significantly","Context caching (to reduce repeated context costs) requires paid tier and adds storage costs ($4.50/1M/hour)","Agentic planning implementation details not documented — no guidance on prompt patterns or best practices","No built-in agent loop orchestration — client must implement the execution loop","No built-in error recovery or replanning — client responsible for handling tool failures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.066Z","last_scraped_at":null,"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-api","compare_url":"https://unfragile.ai/compare?artifact=google-gemini-api"}},"signature":"sjVhn4Zedpjo3XpYIveK/zsYDrIamL2BGSlR1rsuI5KnF+6GpbTTK8o0L8cjRSi59jByfBAuy/9cgKTCuOM8Ag==","signedAt":"2026-06-21T03:29:40.256Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-gemini-api","artifact":"https://unfragile.ai/google-gemini-api","verify":"https://unfragile.ai/api/v1/verify?slug=google-gemini-api","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"}}