AI21 Studio API

Generates coherent text completions using Jamba models with a 256K token context window, enabling processing of entire documents, codebases, or conversation histories in a single API call without context truncation or sliding-window approximations. The architecture supports both prompt-completion and chat-based interfaces, with streaming response support for real-time output consumption.

Provides a dedicated summarization endpoint that condenses text to specified lengths (short, medium, long) and styles (bullet points, paragraph, abstract) using task-optimized prompting and model fine-tuning. The endpoint abstracts away prompt engineering by mapping user intent directly to model behavior through parameter-driven configuration rather than requiring manual prompt crafting.

Transforms input text into alternative phrasings while maintaining semantic meaning and original tone through a dedicated paraphrasing endpoint. The implementation uses instruction-tuned models with style-preservation objectives, allowing developers to rephrase content for plagiarism avoidance, readability improvement, or audience adaptation without manual rewriting.

Identifies and corrects grammatical errors, punctuation issues, and stylistic problems in text through a specialized grammar endpoint that returns both corrected text and structured error metadata. The implementation performs multi-pass analysis (grammar, punctuation, style) and provides error classification (e.g., subject-verb agreement, comma splice) enabling downstream applications to learn from corrections.

Answers questions about provided context (documents, passages, or knowledge bases) by combining retrieval of relevant sections with generative answer synthesis. The implementation supports both direct context passing (for small documents) and retrieval-based workflows where external vector stores or search systems feed relevant passages to the model, enabling question-answering over large knowledge bases without loading entire documents into context.

Streams generated text token-by-token to clients using server-sent events (SSE) or chunked HTTP responses, enabling real-time display of model output without waiting for full completion. The implementation maintains connection state and buffers tokens for efficient transmission, allowing applications to display text as it's generated and provide responsive user experiences.

Manages conversation state across multiple turns using a standardized message format (role-based: user/assistant/system) with automatic context management. The implementation handles message history, role enforcement, and context window optimization, allowing developers to build stateless chat applications without managing conversation state manually.

Provides token counting utilities and detailed usage metadata (input tokens, output tokens, model name, cost) for each API call, enabling accurate cost prediction and budget management. The implementation returns structured usage data with each response, allowing applications to track spending and optimize token usage without external token-counting libraries.

Processes multiple text generation requests asynchronously in a single batch job, returning results via webhook or polling. The implementation queues requests, optimizes batch execution for throughput, and provides job status tracking, enabling cost-effective processing of large volumes of text without blocking on individual request latency.

Allows selection between different Jamba model variants (e.g., Jamba-Instruct, Jamba-Chat) and versions with automatic fallback routing if a model is unavailable. The implementation maintains model registry with capabilities metadata, enabling applications to select models based on task requirements and gracefully degrade to alternatives.