Comparing LLM APIs for AI Agents: Anthropic, OpenAI, and Google AI
This article analyzes the API designs and performance of Anthropic, OpenAI, and Google AI for building AI agent systems. It provides a detailed comparison of the key differences and trade-offs between the three providers.
Why it matters
The choice of LLM API is critical for building production-ready AI agent systems, as the differences in API design and performance can significantly affect agent execution and recovery.
Key Points
- 1Anthropic's API is designed for agent-first use cases, with a focus on execution reliability and structured error handling
- 2Google AI offers strong multimodal capabilities but has a complex product surface with overlapping services
- 3OpenAI has the broadest ecosystem but its onboarding process and spend-gated rate limits create friction for new agent integrations
Details
The article uses Rhumb's 'AN Score' to evaluate the 20 dimensions of LLM API design for agent execution. Anthropic scores the highest at 8.4, with strengths in tool-use interfaces and error handling. Google AI is a close second at 7.9, benefiting from multimodal depth but suffering from product surface confusion. OpenAI ranks third at 6.3, with the largest gap in 'Access Readiness' due to its complex onboarding and spend-gated rate limits. The article highlights key friction points for each provider that can impact agent system development and reliability.
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