Anthropic API

Access Claude models via API with 200K-token context

Some setup needed Web · API

assistant #llm-api#claude-models#developer-tools

About

Call Claude models from your app to generate text, analyze long documents, and answer questions. Developers use it to build chatbots, automate customer support, and run batch data extraction, choosing Opus, Sonnet, or Haiku to balance quality, speed, and cost. All Claude 3 models offer a 200,000-token window for processing lengthy files and extended threads.

Editor's Take

Worth trying if you need to process very long documents or want per-request control over cost vs. capability; best suited for developers building production-grade extraction, QA, or support workflows.

Key Features

Send a prompt with long context (up to 200K tokens) → get a single response that accounts for the entire document
Choose Opus, Sonnet, or Haiku per request → match complexity with cost and latency needs
Post to the Messages endpoint → receive structured JSON suitable for logging, evaluation, and downstream automation
Paste a 150-page policy or contract → get a section-by-section summary in one API call

Use Cases

A product engineer shipping a customer support assistant that answers from long policy docs
A data operations analyst running nightly classification and extraction across large text batches
A solutions architect prototyping an internal Q&A tool over multi-chapter manuals

Try It Like This

1
Build a policy-aware support chatbot
Collect company policies (PDFs) → upload & include full text in a single Messages call using the 200k-token window → receive answers grounded in the exact policy sections for customer support integration.
2
Nightly batch data extraction
Schedule a job that reads nightly text/CSV batches → call the Messages endpoint with extraction instructions and choose Haiku/Sonnet for cost/latency balance → get structured JSON responses ready for DB ingestion and downstream automation.
3
Prototype internal Q&A over manuals
Concatenate multi-chapter manuals into one long context → POST once to Messages asking section-by-section summaries or targeted Q&A → display concise, document-backed replies in an internal chat UI.
4
Automate contract summarization pipeline
Ingest 100–150 page contracts and send as prompt context → request clause-level summaries and risk flags in a single API call → store the parsed JSON output for review and compliance workflows.
5
Adaptive model selection per request
Detect request complexity in your app (cheap classification vs deep analysis) → route simple tasks to Haiku and complex reasoning to Opus per request → optimize cost and latency without changing integration code.

Pros & Cons

Pros

All Claude 3 models support a 200,000-token context window, enabling single-call processing of very long documents.
Three selectable tiers (Opus, Sonnet, Haiku) let developers balance quality, latency, and cost per request.
Messages endpoint returns structured JSON suitable for logging, evaluation, and downstream automation.

Cons

Models are closed-source (Anthropic’s Claude family), which may limit customization compared with open models.
High-volume or very frequent large-context usage can become costly without careful model-tier routing and batching.
No explicit information about Korean language support was found in available sources.

Getting Started

1 Create a developer account at console.anthropic.com and generate an API key
2 Make your first POST request to the Messages API with a short prompt and chosen model (Opus, Sonnet, or Haiku)
3 See the JSON response with the model’s output and iterate on parameters in minutes