A Comprehensive Guide to Accessing OpenAI, Anthropic, and Google Gemini LLMs via Typescript

I. Introduction

Purpose

This document serves as a comprehensive, standalone technical specification for developers utilizing Typescript to access and integrate Large Language Models (LLMs) from three major providers: OpenAI, Anthropic, and Google Gemini. The proliferation of LLM APIs and their associated documentation across various platforms, guides, and community forums necessitates a unified resource. This guide aims to consolidate critical information, providing a "proper spec" as requested, covering API access methods, model characteristics, core features, pricing structures, operational details, and known considerations, thereby functioning as a replacement for disparate API documentation for the purpose of building multi-provider integrations.

Scope Revisited

The scope of this report is strictly defined to cover the practical aspects of integrating with OpenAI, Anthropic, and Google Gemini APIs using the Typescript language. It delves into:

• API Access: Authentication, SDK installation, and client initialization.
• Model Landscape: Available models, selection criteria, and listing mechanisms.
• Core Features: Text generation, streaming, JSON/structured outputs, tool/function calling, multimodal capabilities (input and output), reasoning/thinking visibility, context/prompt caching, and grounding.
• Pricing: Detailed breakdown of costs associated with token usage, specialized features, and different models.
• Operational Details: Token counting, cost calculation, rate limits, usage tiers, and error handling.
• SDK Nuances: Version differences, specific configurations, and provider compatibility layers.
• Known Issues & Gaps: Highlighting common problems, documented limitations, contradictory information, and areas requiring further developer investigation.

This report focuses on the technical implementation details and does not delve into the theoretical underpinnings of LLMs or advanced AI research concepts beyond their direct impact on API usage.

Target Audience

The intended audience comprises software developers, engineers, and technical leads who possess proficiency in Typescript and are tasked with building applications that leverage LLM capabilities from one or more of the specified providers. It assumes a working knowledge of modern software development practices, including API integration, asynchronous programming, and environment management.

Methodology

The information presented herein is synthesized from a variety of sources, primarily official provider documentation (API references, SDK guides, pricing pages), official SDK repositories on platforms like GitHub, and relevant discussions within developer community forums. Details have been corroborated across sources where possible. Where discrepancies, ambiguities, or gaps in publicly available information exist, they are noted to provide a transparent and accurate picture of the current landscape.

II. Foundational Setup (Typescript)

Establishing a secure and correctly configured development environment is paramount before interacting with any LLM API. This section covers the essential prerequisites for Typescript-based integration.