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 request without context truncation. The architecture supports both prompt-completion and chat-based interfaces, with streaming responses for real-time output delivery and batch processing for high-volume requests.

Provides dedicated API endpoints for common NLP tasks (summarization, paraphrasing, grammar correction) that are fine-tuned for each task rather than using a single general-purpose model. Each endpoint accepts task-specific parameters and returns optimized outputs, leveraging instruction-tuned variants of Jamba models trained on task-specific datasets.

Answers questions about provided documents or context by leveraging the 256K context window to include full source material in the request, enabling retrieval-augmented generation (RAG) without external vector databases. The API accepts a document or context block alongside a question and returns answers grounded in that context with optional citation support.

Supports both real-time streaming responses (Server-Sent Events) for interactive applications and batch processing for high-volume, non-time-critical requests. Streaming returns tokens incrementally as they are generated, while batch mode queues requests and returns results asynchronously, optimizing for throughput and cost.

Provides access to multiple Jamba model variants (base, instruction-tuned, task-specific) through a unified API, allowing developers to select models based on latency, cost, and quality requirements. The API abstracts model selection and routing, with automatic fallback and version management handled server-side.

Provides token counting endpoints that calculate exact token consumption for prompts before making API calls, enabling accurate cost estimation and quota management. The API uses the same tokenizer as the inference models, ensuring consistency between estimated and actual token usage.

Supports constrained generation where outputs conform to a provided JSON schema, ensuring responses are parseable and structured. The API validates generated output against the schema and re-generates if validation fails, with configurable retry logic and fallback behavior.

Allows developers to define custom system prompts and role instructions that guide model behavior across requests, enabling persona-based generation and domain-specific instruction following. System prompts are applied at the model level and persist across conversation turns in chat-based interactions.

Manages multi-turn conversations by automatically handling context windows, including or truncating conversation history based on token limits. The API tracks conversation state server-side (optional) or client-side, with configurable strategies for deciding which messages to retain when approaching token limits.

Provides rate limiting enforcement and quota tracking at the API level, with per-user, per-application, and per-organization limits configurable through the dashboard. The API returns usage metadata in responses and enforces limits with clear error messages indicating remaining quota.