Reka Flash 3

Reka Flash 3 processes multi-turn conversational inputs and generates contextually appropriate responses using a 21B parameter instruction-tuned transformer architecture. The model maintains conversation history through context windowing and applies instruction-following fine-tuning to adhere to user directives, system prompts, and role-based constraints without explicit prompt engineering overhead.

Reka Flash 3 generates syntactically correct code snippets and complete functions across multiple programming languages using transformer-based code understanding trained on diverse codebases. The model accepts natural language descriptions, partial code, or function signatures and outputs executable code with proper indentation, imports, and error handling patterns learned during pre-training.

Reka Flash 3 supports structured function calling by accepting JSON schemas that define available functions, parameters, and return types, then generating properly formatted function calls with bound arguments extracted from user intent. The model parses user requests, maps them to appropriate functions, and outputs structured JSON containing function name, arguments, and metadata without requiring manual prompt engineering for each function.

Reka Flash 3 answers factual questions across diverse domains (science, history, current events, technical topics) by retrieving relevant knowledge from its training data and synthesizing coherent responses. The model applies instruction-tuning to distinguish between confident answers and uncertain knowledge, enabling it to express confidence levels and acknowledge knowledge cutoffs without hallucinating unsupported claims.

Reka Flash 3 generates creative content (stories, poetry, marketing copy, dialogue) with controllable style and tone through instruction-based prompting. The model learns style patterns from training data and applies them consistently across generated text, enabling users to specify tone (formal, casual, humorous) and genre without fine-tuning or specialized prompt engineering.

Reka Flash 3 condenses long-form text (articles, documents, conversations) into summaries of variable length and detail through instruction-based control. The model extracts key information, preserves essential facts, and adjusts summary granularity (brief bullet points vs. detailed paragraphs) based on user specifications without requiring separate models or fine-tuning.

Reka Flash 3 translates text between languages while preserving meaning, tone, and context through multilingual transformer training and instruction-tuning. The model handles idiomatic expressions, cultural references, and technical terminology by learning translation patterns across diverse language pairs, enabling natural-sounding translations without requiring language-specific fine-tuning.

Reka Flash 3 strictly follows complex, multi-part instructions and adheres to specified constraints (output format, length limits, style requirements) through instruction-tuning that prioritizes constraint satisfaction. The model parses compound instructions, maintains constraint awareness throughout generation, and produces outputs that satisfy all specified requirements without requiring explicit constraint encoding in prompts.

Reka Flash 3 generates detailed explanations and step-by-step reasoning for answers through instruction-tuning that encourages intermediate reasoning steps and explicit justification. The model breaks down complex problems into components, explains reasoning at each step, and provides transparent decision-making processes that enable users to understand and verify conclusions.