{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"reka-api","slug":"reka-api","name":"Reka API","type":"api","url":"https://reka.ai","page_url":"https://unfragile.ai/reka-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"reka-api__cap_0","uri":"capability://image.visual.native.multimodal.video.understanding.with.temporal.reasoning","name":"native multimodal video understanding with temporal reasoning","description":"Processes video files natively (not as frame extraction + text model) to understand temporal sequences, motion, scene changes, and narrative flow. The API accepts video inputs directly and performs joint reasoning across visual frames, audio tracks, and temporal context in a single forward pass, enabling detection of events that require understanding of change over time rather than static image analysis.","intents":["I need to understand what happens in a video clip, including motion and scene transitions","I want to extract structured information about events that unfold over time in video content","I need to generate captions that describe video action and narrative, not just static content","I want to search or index video content by semantic meaning of events and interactions"],"best_for":["video content platforms building semantic search or recommendation systems","media companies automating video metadata generation and tagging","developers building video analysis tools where temporal understanding is critical"],"limitations":["Maximum video length, resolution, and frame rate not documented in available source material","Supported video codecs and container formats not specified","Latency for long-form video processing not published","No documented support for real-time streaming video analysis"],"requires":["API key for Reka API authentication","Video file in supported format (specific formats not documented)","Network connectivity for API calls"],"input_types":["video files","video URLs","video streams (capability unknown)"],"output_types":["text descriptions","structured event data","embeddings","JSON-formatted analysis"],"categories":["image-visual","multimodal-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_1","uri":"capability://image.visual.audio.understanding.beyond.transcription.with.semantic.extraction","name":"audio understanding beyond transcription with semantic extraction","description":"Analyzes audio content to extract meaning, emotion, intent, and semantic information rather than just converting speech to text. The API processes audio signals to understand speaker intent, emotional tone, background context, and non-speech audio elements (music, ambient sounds, effects) in a unified model, returning structured semantic understanding rather than transcription-only output.","intents":["I need to understand the emotional tone and intent behind spoken content, not just what was said","I want to extract key topics and semantic meaning from audio without transcribing every word","I need to identify and classify non-speech audio elements like music, ambient noise, or sound effects","I want to analyze customer support calls for sentiment, issue classification, and resolution tracking"],"best_for":["customer experience teams analyzing support call quality and sentiment","content platforms building audio search and recommendation systems","developers building voice-first applications requiring semantic understanding beyond transcription"],"limitations":["Supported audio formats, sample rates, and maximum duration not documented","Language support and multilingual capability not specified","Latency for real-time audio processing not published","No documented support for streaming audio or live audio analysis"],"requires":["API key for Reka API authentication","Audio file in supported format (specific formats not documented)","Network connectivity for API calls"],"input_types":["audio files","audio URLs","audio streams (capability unknown)"],"output_types":["semantic understanding text","emotion/sentiment classification","intent classification","structured JSON analysis","embeddings"],"categories":["image-visual","data-processing-analysis","multimodal-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_10","uri":"capability://data.processing.analysis.structured.data.extraction.from.multimodal.content","name":"structured data extraction from multimodal content","description":"Extracts structured information from images, video, and audio content and returns it in a machine-readable format (JSON, CSV, etc.). The capability can extract entities, relationships, attributes, and other structured data without requiring manual annotation or separate extraction models, enabling automation of data collection from unstructured multimodal sources.","intents":["I need to extract product information (name, price, description) from product images","I want to extract structured data from documents, forms, or receipts in images","I need to extract entities and relationships from video or audio content"],"best_for":["e-commerce platforms extracting product details from images","document processing systems extracting information from scanned documents","data entry automation reducing manual data collection","research teams extracting structured data from multimodal sources"],"limitations":["Output schema specification not documented — unclear how to define what data to extract","Schema validation not documented — unclear if API validates extracted data against schema","Extraction accuracy unknown — unclear precision/recall for structured extraction","Complex relationships unknown — unclear if model can extract multi-level hierarchies","No template or example-based extraction documented — likely requires explicit schema definition"],"requires":["API key for Reka API authentication","Content (image, video, or audio) to extract from","Schema or specification of what data to extract (format unknown)"],"input_types":["image file or URL","video file or URL","audio file or URL","schema or extraction instructions (format unknown)"],"output_types":["structured data in JSON format","optional CSV or other structured format"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_2","uri":"capability://memory.knowledge.unified.multimodal.embeddings.for.cross.modal.search.and.retrieval","name":"unified multimodal embeddings for cross-modal search and retrieval","description":"Generates vector embeddings that represent content across video, image, audio, and text modalities in a shared embedding space, enabling semantic search and similarity matching across different input types. A single query (text, image, or audio) can retrieve relevant results from a database containing mixed media types, with embeddings computed through the same multimodal model ensuring semantic alignment across modalities.","intents":["I want to search a video library using text queries and get semantically relevant video clips","I need to find similar images, videos, or audio content based on a reference sample","I want to build a recommendation system that suggests related content across different media types","I need to deduplicate or cluster content across mixed media types based on semantic similarity"],"best_for":["media platforms building cross-modal search and discovery features","content management systems requiring semantic indexing of mixed media libraries","developers building recommendation engines that work across video, image, and audio content"],"limitations":["Embedding dimensionality and vector size not documented","Similarity metric (cosine, euclidean, etc.) not specified","Embedding stability across model versions not documented","No published information on embedding quality or benchmark performance","Requires external vector database for production-scale retrieval (not provided by Reka)"],"requires":["API key for Reka API authentication","Vector database or similarity search infrastructure (e.g., Pinecone, Weaviate, Milvus)","Batch processing capability to embed large content libraries","Network connectivity for API calls"],"input_types":["text","images","audio files","video files"],"output_types":["vector embeddings (float arrays)","similarity scores"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_3","uri":"capability://image.visual.image.captioning.and.visual.description.generation","name":"image captioning and visual description generation","description":"Generates natural language descriptions of image content, including object identification, spatial relationships, scene context, and semantic meaning. The model analyzes visual input and produces human-readable captions that can range from short summaries to detailed descriptions, with the ability to customize caption length and detail level through API parameters.","intents":["I need to generate alt text for images in accessibility compliance","I want to create captions for image galleries or social media content","I need to generate metadata descriptions for image search indexing","I want to describe images in multiple languages for international content"],"best_for":["content platforms automating image metadata and accessibility features","e-commerce systems generating product descriptions from images","developers building image-to-text pipelines for content management"],"limitations":["Maximum image resolution and file size not documented","Supported image formats not specified","Caption length constraints not published","No documented support for batch image captioning","Language support and multilingual caption generation not specified"],"requires":["API key for Reka API authentication","Image file in supported format (specific formats not documented)","Network connectivity for API calls"],"input_types":["image files","image URLs"],"output_types":["text descriptions","structured JSON with caption and metadata"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_4","uri":"capability://image.visual.visual.object.detection.and.localization.with.bounding.boxes","name":"visual object detection and localization with bounding boxes","description":"Identifies and localizes objects within images by returning bounding box coordinates, class labels, and confidence scores. The model detects multiple object instances in a single image and provides spatial information enabling downstream applications to reference specific regions of interest, with support for custom object classes through prompt-based detection.","intents":["I need to identify and locate specific objects in images for automated processing","I want to generate bounding box annotations for computer vision model training datasets","I need to detect and count objects in images for inventory or quality control","I want to enable click-to-identify functionality in image viewing applications"],"best_for":["computer vision teams building training datasets with automated annotation","quality control and inspection systems requiring object detection","developers building interactive image analysis tools with spatial understanding"],"limitations":["Maximum number of detectable objects per image not documented","Detection confidence thresholds and filtering options not specified","Supported object classes and custom class definition not documented","Bounding box coordinate format and precision not specified","Performance on small objects or occluded instances not published"],"requires":["API key for Reka API authentication","Image file in supported format (specific formats not documented)","Network connectivity for API calls"],"input_types":["image files","image URLs","text prompts for custom object detection"],"output_types":["JSON with bounding box coordinates","object class labels","confidence scores","spatial region data"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_5","uri":"capability://image.visual.visual.question.answering.on.images.and.video","name":"visual question answering on images and video","description":"Answers natural language questions about image and video content by analyzing visual information and generating contextual responses. The model accepts an image or video and a text question, then produces an answer that demonstrates understanding of visual content, spatial relationships, object properties, and temporal events (for video). Questions can range from factual identification to reasoning about relationships and implications.","intents":["I want to ask questions about image content and get accurate answers without manual inspection","I need to verify that images contain specific elements or meet quality criteria","I want to extract specific information from images through natural language queries","I need to understand what's happening in video clips by asking questions about events and interactions"],"best_for":["quality assurance teams automating image and video verification","content moderation systems requiring contextual understanding of visual content","developers building interactive image and video analysis applications"],"limitations":["Question complexity limits and reasoning depth not documented","Maximum question length not specified","Accuracy on complex multi-step reasoning questions not published","Hallucination rates and confidence scoring not documented","No documented support for follow-up questions or conversation history"],"requires":["API key for Reka API authentication","Image or video file in supported format (specific formats not documented)","Natural language question as text input","Network connectivity for API calls"],"input_types":["image files","image URLs","video files","video URLs","text questions"],"output_types":["text answers","confidence scores","structured JSON responses"],"categories":["image-visual","text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_6","uri":"capability://tool.use.integration.three.tier.model.selection.with.performance.cost.tradeoffs","name":"three-tier model selection with performance-cost tradeoffs","description":"Provides three distinct model variants (Reka Core, Reka Flash, Reka Edge) with different performance characteristics, latency profiles, and pricing tiers. Developers select the appropriate model based on their accuracy requirements, latency constraints, and cost budget, with each model supporting the full multimodal capability set but with different quality-speed-cost tradeoffs. Model selection is specified at API request time.","intents":["I need the highest quality multimodal analysis and can accept higher latency and cost","I want a balanced model that provides good quality with reasonable latency and cost","I need the fastest inference for real-time or high-throughput applications and can accept lower quality","I want to optimize costs for large-scale batch processing of multimodal content"],"best_for":["teams with variable workloads requiring different performance tiers","cost-conscious developers building large-scale content processing pipelines","real-time applications requiring sub-second latency responses"],"limitations":["Performance characteristics (latency, throughput) for each model tier not documented","Quality differences and accuracy benchmarks between models not published","Pricing differences between Core, Flash, and Edge models not specified","Guidance on when to use each model tier not provided","No documented ability to switch models mid-application or based on input characteristics"],"requires":["API key for Reka API authentication","Knowledge of which model tier is appropriate for use case (guidance not provided)","Network connectivity for API calls"],"input_types":["model selection parameter in API request"],"output_types":["same output types as other capabilities, quality varies by model"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_7","uri":"capability://automation.workflow.batch.processing.and.asynchronous.api.for.large.scale.content.analysis","name":"batch processing and asynchronous api for large-scale content analysis","description":"Supports processing multiple images, videos, or audio files in batch mode with asynchronous job submission and result polling or webhook callbacks. Developers submit batch jobs containing multiple media files and receive a job ID, then retrieve results once processing completes, enabling efficient processing of large content libraries without blocking on individual API calls. Implementation details (polling interval, webhook format, job timeout) not documented.","intents":["I need to process thousands of images or videos and want to avoid per-request latency","I want to analyze my entire content library for metadata generation without blocking my application","I need to schedule large-scale content processing jobs to run during off-peak hours","I want to process content asynchronously and receive results via webhook when ready"],"best_for":["content platforms with large media libraries requiring bulk metadata generation","batch processing pipelines for content indexing and search optimization","teams building scheduled jobs for periodic content analysis and re-indexing"],"limitations":["Maximum batch size and number of files per job not documented","Job timeout and maximum processing duration not specified","Webhook format and retry behavior not documented","Result storage duration and retrieval window not specified","No documented support for priority queuing or job scheduling","Batch processing availability and whether it's available for all model tiers unknown"],"requires":["API key for Reka API authentication","Batch processing API endpoint (if separate from standard API)","Webhook endpoint for receiving results (if using webhook callbacks)","Network connectivity for API calls and webhook delivery"],"input_types":["batch job specification with multiple media files","media file URLs or uploaded content"],"output_types":["job ID for tracking","batch results in JSON format","webhook callbacks with results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_8","uri":"capability://image.visual.multimodal.context.window.with.cross.modal.reasoning","name":"multimodal context window with cross-modal reasoning","description":"Maintains a context window that can simultaneously hold text, images, video, and audio content, enabling the model to reason across modalities within a single inference pass. The model can answer questions about relationships between visual and textual content, reference specific moments in video while discussing text, or correlate audio tone with visual events, all without separate API calls or external coordination logic.","intents":["I want to ask questions that require understanding both text documents and images together","I need to correlate events in video with spoken dialogue and understand their relationship","I want to verify that image content matches accompanying text descriptions","I need to analyze how visual, audio, and textual elements work together in multimedia content"],"best_for":["content verification and quality assurance requiring cross-modal consistency checks","multimedia analysis applications requiring holistic understanding of mixed content","developers building intelligent document and media processing systems"],"limitations":["Context window size (maximum tokens/content) not documented","Maximum number of images, videos, or audio files per request not specified","How context is prioritized or truncated when exceeding limits unknown","Cross-modal reasoning quality and limitations not published","No documented support for conversation history or multi-turn reasoning"],"requires":["API key for Reka API authentication","Multiple media files (text, images, video, audio) in supported formats","Network connectivity for API calls"],"input_types":["text","images","video files","audio files","mixed combinations of above"],"output_types":["text responses","structured JSON analysis","cross-modal reasoning explanations"],"categories":["image-visual","text-generation-language","planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__cap_9","uri":"capability://safety.moderation.content.moderation.and.safety.classification.for.multimodal.content","name":"content moderation and safety classification for multimodal content","description":"Analyzes images, video, and audio content to detect and classify potentially harmful, inappropriate, or policy-violating material. The capability performs safety classification across multiple dimensions (violence, sexual content, hate speech, etc.) and can be used to flag content for human review or automatically reject submissions that violate platform policies.","intents":["I need to automatically detect and flag inappropriate user-generated content","I want to classify content safety across multiple dimensions (violence, sexual, hate speech, etc.)","I need to moderate multimodal content (images, video, audio) with a single system"],"best_for":["social media platforms moderating user uploads at scale","content platforms enforcing community standards","marketplaces preventing prohibited items from being listed","messaging apps filtering harmful content"],"limitations":["Safety categories not documented — unknown what types of harm are detected","Confidence scores not documented — unclear if model provides certainty levels","False positive/negative rates unknown — unclear accuracy of classifications","Cultural context handling unknown — unclear if model understands regional norms","No appeal or override mechanism documented — unclear how to handle misclassifications","Bias and fairness properties unknown — unclear if model has documented fairness properties"],"requires":["API key for Reka API authentication","Content (image, video, or audio) to classify","Policy definitions for what content to flag (not provided by API)"],"input_types":["image file or URL","video file or URL","audio file or URL"],"output_types":["safety classification labels","optional confidence scores per category","optional severity levels"],"categories":["safety-moderation","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"reka-api__headline","uri":"capability://llm.apis.multimodal.ai.api.for.vision.audio.and.video.understanding","name":"multimodal ai api for vision, audio, and video understanding","description":"Reka API is a multimodal AI API designed for comprehensive understanding of vision, audio, and video, emphasizing a multimodal-first approach rather than just text integration.","intents":["best multimodal AI API","multimodal API for video analysis","AI API for audio understanding","top APIs for vision and audio tasks","multimodal perception API for developers"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["llm-apis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key for Reka API authentication","Video file in supported format (specific formats not documented)","Network connectivity for API calls","Audio file in supported format (specific formats not documented)","Content (image, video, or audio) to extract from","Schema or specification of what data to extract (format unknown)","Vector database or similarity search infrastructure (e.g., Pinecone, Weaviate, Milvus)","Batch processing capability to embed large content libraries","Image 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