When we started building our application portfolio, the default choice was OpenAI's GPT. It had the mindshare, the ecosystem, and the documentation. We shipped our first prototype on GPT-4.
Then we moved everything to Claude. Here's why.
Structured Output Reliability
Our applications don't generate creative writing — they produce structured risk assessments, compliance reports, and financial analyses. Every API response needs to parse cleanly into TypeScript types.
Claude's instruction-following for structured output has been measurably more consistent in our testing. When we ask for a JSON object with specific fields, we get that object. With GPT-4, we were spending significant engineering time on output validation and retry logic.
Context Window Depth
BitcoinRMF processes threat models that can span thousands of tokens of context. The quality of analysis at the tail end of a long context window matters enormously when you're scoring risk across dozens of threat vectors.
In our evals, Claude maintained analytical consistency deeper into the context window than competing models. This isn't a benchmark claim — it's what we observed in production across thousands of API calls.
The SDK Experience
Anthropic's TypeScript SDK is clean and well-typed. The streaming API works as documented. Error handling is predictable. These things matter when you're a small team shipping production software.
The Tradeoffs
Claude isn't perfect. The ecosystem is smaller — fewer community tools, fewer tutorials, fewer Stack Overflow answers. If something breaks at 2 AM, you're more likely to be on your own.
But for our use case — structured, high-stakes AI outputs in production — the reliability advantage outweighs the ecosystem gap. We'll trade community size for output consistency every time.
Our Stack
For reference, here's how Claude fits into our production architecture:
- Claude Sonnet for high-throughput analysis (threat scoring, FUD detection)
- Claude Haiku for lightweight tasks (input classification, tag generation)
- Vercel API Gateway for rate limiting and request management
- Zod for runtime validation of AI outputs
The model is a component, not the product. But choosing the right component matters more than most founders realize.