When should I use the Claude API vs the OpenAI API?

Choosing between Claude and OpenAI is a technical decision that depends on your use case, the models each vendor currently offers, your cost profile and your existing tooling. Model names, context windows and prices change several times a year, so this page compares the things that change slowly and leaves the numbers to the vendors' own pages.

Models and Context Windows

• Both vendors offer a tiered lineup: a large model for the hardest reasoning and agentic work, a mid-tier model that balances speed and quality, and a small, low-cost model for high-volume tasks.
• Context window size is no longer a clear differentiator. The current flagship models from both vendors accept inputs large enough for long contracts, large codebases and long conversation histories. Check the exact limit for the specific model you plan to use on Anthropic's models overview and OpenAI's models page.
• Models are retired on a schedule. Pin a specific model version in production and plan the migration before it is retired (see Anthropic's model deprecations).

Reasoning and Instruction Following

Both vendors' current flagship models handle complex, multi-step instructions, and published benchmarks move with every release. The reliable way to choose is an evaluation on your own inputs: a few dozen real examples, scored against the output you need, run against the candidate models from each vendor. For well-structured, templated prompts at high volume, the small tier from either vendor is often the better cost choice.

Ecosystem and Tooling

• OpenAI covers more modalities under one API: text, embeddings, image generation and editing, and realtime speech.
• Anthropic does not offer its own embedding model, and its documentation points to third-party providers such as Voyage AI. A retrieval (RAG) system built on Claude therefore uses a second vendor for embeddings.
• Both APIs support tool use (function calling), structured outputs, prompt caching and batch processing, and the common frameworks (LangChain, LlamaIndex and others) support both.

Cost

Both providers price by input and output tokens, each model tier at a different rate, and both discount batch requests and cached input. Prices change often enough that any figure here would go stale, so run a cost estimate on your expected monthly token volume against each vendor's current pricing page before committing to either.

Code and Trust builds production LLM applications on both providers and helps clients choose the right model for their specific use case. Learn about our AI implementation practice or contact us to scope your project.