Reka API

Processes video files end-to-end through a unified multimodal architecture that natively understands temporal sequences, motion, and context across frames without requiring frame extraction or separate vision-language composition. The API accepts video inputs directly and performs frame-level analysis with temporal coherence, enabling scene understanding, action recognition, and narrative comprehension within a single inference pass rather than treating video as a sequence of independent images.

Analyzes static images through a unified multimodal encoder that performs simultaneous object detection, spatial relationship reasoning, and semantic understanding in a single forward pass. The capability extracts structured information about what objects are present, where they are located, how they relate to each other, and what activities or states they represent, without requiring separate detection models or post-processing pipelines.

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.

Processes audio files to extract semantic meaning, context, and actionable insights beyond simple transcription. The capability performs speaker identification, emotional tone analysis, topic extraction, and key insight generation from audio content in a single inference pass, treating audio as a first-class modality with native understanding rather than converting to text first.

Generates dense vector embeddings that represent the semantic content of images, video, audio, and text in a shared embedding space, enabling cross-modal similarity search and retrieval. The embeddings are produced by the same unified multimodal encoder used for understanding, ensuring that embeddings from different modalities are directly comparable and can be used for retrieval tasks like 'find images similar to this text query' or 'find videos related to this image'.

Answers natural language questions about image or video content by jointly reasoning over visual and textual information. The capability takes an image or video and a question as input, and produces an answer that demonstrates understanding of both the visual content and the semantic meaning of the question, without requiring separate visual grounding or question parsing steps.

Provides three distinct model variants (Reka Core, Flash, and Edge) that trade off between reasoning capability, speed, and cost, allowing developers to select the appropriate tier for their use case. The API likely accepts a model parameter in requests to specify which variant to use, enabling cost optimization for latency-sensitive or budget-constrained applications while preserving access to more capable models for complex reasoning tasks.

Provides a single REST API endpoint that accepts multimodal inputs (images, video, audio, text) and produces structured outputs, with a unified request/response schema that abstracts away modality-specific handling. Developers submit requests with mixed modality content and receive consistent response formats regardless of input type, simplifying integration compared to managing separate endpoints for vision, audio, and text.

Generates natural language captions and descriptions for images by analyzing visual content and producing human-readable text that summarizes what is shown. The capability can produce captions of varying length and detail level, from short single-sentence summaries to detailed multi-sentence descriptions, enabling flexible use cases from social media alt-text to comprehensive image documentation.

Generates natural language captions and descriptions for video content that capture temporal progression, motion, and narrative arc. Unlike image captioning, video captioning must understand how scenes change over time and produce descriptions that reflect the sequence of events, enabling applications that require temporal awareness of video content.

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.