ChatGPT vs Claude: which one to choose for your AI projects?

The question comes up in almost every project: “What do we use, ChatGPT or Claude?” Here’s my honest answer after working with both.

What I look at as a PO

I’m not here to run perplexity benchmarks. What matters to me as a PO:

• Reliability of structured outputs (JSON, precise formats)
• Context retention over long conversations
• Behavior with complex instructions
• Cost and predictability of results
• Surrounding tool ecosystem

ChatGPT (GPT-4o) — strengths

The ecosystem. OpenAI has a head start on third-party integrations, plugins, and enterprise recognition. If your client already has an OpenAI license, the decision is often already made.

Versatility. GPT-4o excels at a wide range of tasks without sophisticated prompt engineering. For generic use cases, it delivers good results quickly.

Built-in DALL-E. If your agent needs to generate images, this is a meaningful advantage.

Claude — strengths

Following complex instructions. With long, precise system prompts with many constraints, Claude holds up better. It “forgets” rules defined early in the context less often.

Context window. Claude 3.5 Sonnet supports up to 200k tokens. For agents that need to ingest large documents, this is a decisive advantage.

JSON output consistency. In my tests, Claude produces malformed JSON less frequently than GPT-4o when schemas are complex.

Native MCP. For projects with Claude Code and MCP servers, the integration is obviously native.

What I recommend by use case

For a generic customer service agent → GPT-4o. Cheaper, sufficient for the job, larger ecosystem.

For an agent with complex business rules → Claude. Instruction retention over long contexts makes the difference.

For an agent processing large documents → Claude, without hesitation. The context window changes everything.

For a team already in the Microsoft/Azure ecosystem → GPT-4o via Azure OpenAI. Enterprise integration is better.

What I’ve learned to avoid

Choosing an LLM by default without evaluating on your real cases. Generic benchmarks don’t predict behavior on your specific domain. Test both on 20 representative examples before deciding.

And above all: architect your agents so you can switch models. If your code is decoupled from the LLM, switching from GPT-4o to Claude (or vice versa) should be a configuration change, not a refactoring.