Reka API vs WorkOS

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.

Unique: Processes video as a native modality with temporal reasoning built into the model architecture, rather than extracting frames and processing them independently through a text-with-vision model. This enables understanding of motion, scene transitions, and events that require temporal context.

vs alternatives: Differs from frame-extraction approaches (used by most vision APIs) by maintaining temporal coherence, enabling detection of motion-dependent events and narrative understanding that single-frame analysis cannot achieve.

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.

Unique: Integrates audio understanding as a first-class modality in the multimodal model rather than using separate speech-to-text + NLP pipelines. This enables joint reasoning across audio semantics, speaker intent, and emotional context in a single inference pass.

vs alternatives: Goes beyond speech-to-text APIs (like Whisper or Google Cloud Speech-to-Text) by providing semantic understanding and emotion detection without requiring separate NLP models, reducing latency and improving coherence of multi-step analysis.

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.

Unique: Structured extraction is performed by the unified multimodal model with schema-aware output generation, rather than separate extraction models per modality

vs alternatives: More flexible than OCR-based extraction (Tesseract, AWS Textract) because it understands semantic meaning and relationships, not just text recognition

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.

Unique: Generates embeddings from a unified multimodal model that processes video, image, audio, and text, placing all modalities in the same vector space. This differs from approaches that use separate embedding models per modality or bolt vision onto text embeddings.

vs alternatives: Enables true cross-modal search (e.g., text query finding video results) by design, whereas most embedding APIs either handle single modalities or use separate embedding spaces that require alignment techniques.

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.

Unique: Integrated as a native capability of the multimodal model rather than a separate vision-to-text pipeline, enabling consistent semantic understanding across the full multimodal context.

vs alternatives: Part of a unified multimodal model that can reason about images in context with video, audio, and text, whereas specialized captioning APIs (like AWS Rekognition or Google Vision) handle images in isolation.

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.

Unique: Integrated into the multimodal model architecture, enabling object detection to leverage context from video, audio, and text understanding rather than operating as an isolated vision task.

vs alternatives: Provides object detection as part of a unified multimodal system, whereas specialized detection APIs (YOLO, Faster R-CNN services) operate independently without cross-modal context.

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.

Unique: Extends visual question answering to video with temporal reasoning, enabling questions about events, sequences, and changes over time rather than just static image content.

vs alternatives: Handles both images and video in a unified model with temporal understanding for video, whereas most VQA APIs (like Google Cloud Vision or AWS Rekognition) focus on static images.

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.

Unique: Offers three explicit model tiers with documented multimodal capabilities across all tiers, rather than a single model or separate specialized models for different tasks.

vs alternatives: Provides explicit performance-cost tradeoff options at the API level, whereas most multimodal APIs offer a single model or require using different APIs entirely for different performance requirements.

Enables SaaS applications to integrate enterprise SSO by accepting SAML assertions and OIDC authorization codes from 20+ identity providers (Okta, Azure AD, Google Workspace, etc.). WorkOS acts as a service provider that normalizes identity responses across heterogeneous enterprise directories, exchanging authorization codes for user profiles and access tokens via language-specific SDKs (Node.js, Python, Ruby, Go, PHP, Java, .NET). The implementation uses a per-connection pricing model where each enterprise customer's identity provider is registered as a distinct connection, allowing multi-tenant SaaS platforms to onboard customers without custom integration work.

Unique: Normalizes SAML/OIDC responses across 20+ heterogeneous identity providers into a unified user profile schema, eliminating per-provider integration code. Uses per-connection pricing model where each enterprise customer's identity provider is a billable unit, enabling SaaS platforms to scale enterprise sales without custom engineering per customer.

vs alternatives: Faster enterprise onboarding than building native SAML/OIDC support (weeks vs months) and cheaper than hiring dedicated identity engineers; more flexible than Auth0's rigid provider list because it supports custom SAML/OIDC endpoints with manual configuration.

Automatically synchronizes user and group data from enterprise HR systems and directories (Workday, SuccessFactors, BambooHR, etc.) into SaaS applications using the SCIM 2.0 protocol. WorkOS acts as a SCIM service provider that receives provisioning/de-provisioning events from customer directories via webhooks, normalizing user lifecycle events (create, update, suspend, delete) and group memberships into a consistent schema. The implementation uses event-driven architecture where directory changes trigger webhook deliveries in real-time, eliminating manual user management and keeping application user rosters synchronized with authoritative HR systems.

Unique: Implements SCIM 2.0 as a service provider (not just client), allowing enterprise HR systems to push user lifecycle events via webhooks in real-time. Uses normalized event schema that abstracts away differences between Workday, SuccessFactors, BambooHR, and other HR systems, enabling single integration point for SaaS platforms.