Claude 3.5 Sonnet vs GPT-4o for Writing API Documentation: Which LLM Explains Complex Code Without Hallucinating Parameters?

The Developer Experience Trap: Why Accurate API Docs Matter

Writing API documentation is the chore that every software engineer loves to hate. It is tedious, it requires painstaking attention to detail, and the moment you push a new feature to production, your existing docs are already out of date. Naturally, handing this mountain of grunt work over to an LLM feels like an absolute lifesaver.

But there is a catch. In the world of developer experience (DX), bad documentation is significantly worse than no documentation at all. If a developer runs into an undocumented endpoint, they will go look at the source code. If they run into a documented endpoint with a hallucinated parameter or an incorrect data type, they will waste hours debugging a ghost in their machine, cursing your platform's name.

We wanted to see which of the two heavyweights—Anthropic's Claude 3.5 Sonnet and OpenAI's GPT-4o—is actually safe to trust with your codebase's public-facing documentation. We put them both through a rigorous test using messy, real-world backend code to find out which engine builds the most reliable, accurate documentation without inventing parameters out of thin air.

The Test: Feeding the Beast Messy Backend Code

To keep things fair, we did not give the models clean, textbook examples. We handed them a sprawling, production-esque 250-line Node.js Express controller that included: * Implicit type coercion (handling strings that should be parsed into integers for pagination). * Nested database payload validations using a custom schema library rather than a standard one like Zod. * Conditional query parameters that only trigger when specific headers are present. * Undocumented error states throwing custom HTTP exceptions.

Our prompt was simple: generate a complete OpenAPI 3.0 specification in YAML format, followed by a human-friendly Markdown developer guide explaining how to authenticate, make requests, and handle errors. You can check out how we structured our system instructions on our /prompts generator page to see how to pin down strict formatting rules for technical outputs.

Claude 3.5 Sonnet: The Meticulous Architect

When we fed this codebase to Claude 3.5 Sonnet, the results were immediate and incredibly precise. Claude has earned a reputation among programmers for its exceptional reasoning capabilities, and this test proved why.

Sonnet did not just scan the code for variable names; it traced the logical paths. It correctly identified that our pagination parameter limit defaulted to 20 but was capped at 100 deep inside a utility helper file we did not even explicitly define in the main context. It deduced this constraint from a clean interpretation of our database query pattern.

Furthermore, Sonnet’s schema definition in the OpenAPI YAML was flawless. It did not guess the shape of our nested JSON request body; it parsed the validation logic loop by loop, ensuring that optional fields were properly marked as nullable. When generating the Markdown guide, it structured the error response examples exactly as our custom exception handler would output them, retaining our unique snake_case error codes.

If you find yourself running into formatting errors or truncated YAML blocks when generating large specifications with Claude, our troubleshooting guide on /platforms/claude/articles offers clean ways to handle long-context structured outputs.

GPT-4o: The High-Speed Generalist

Next, we loaded the same messy Express code into GPT-4o. OpenAI’s flagship model is lightning-fast, and its initial output looked visually stunning. It formatted the Markdown beautifully and prioritised developer onboarding with a friendly, welcoming tone.

However, once we looked closer at the actual technical parameters, the cracks began to show. GPT-4o made several lazy assumptions: * It assumed our pagination parameter was named page (a common REST convention), completely missing that our custom code actually used a cursor-based pagination key called starting_after. * It hallucinated a bearer token authentication header format that our API does not support, ignoring the custom API key validation logic clearly written in the middleware section of the file. It marked a nested user profile object in the request schema as required*, despite the code showing a fallback empty object initialiser if the field was missing.

GPT-4o is a highly capable model, but it suffers from a tendency to default to the most common pattern found in its training data rather than reading the specific logic right in front of it. It prioritised standardising the API over documenting what was actually there. If you need to debug custom behavior issues with GPT-4o's code comprehension, you can find practical architecture tips on /platforms/openai/articles.

Pricing, Context Windows, and API Limits

When choosing an LLM to automate your documentation pipeline, you must look at the economics. Documenting a large microservice ecosystem requires submitting dozens of files simultaneously, making context window size and input/output token pricing a major bottleneck.

| Metric | Claude 3.5 Sonnet | GPT-4o | | :--- | :--- | :--- | | Context Window | 200k tokens | 128k tokens | | Input Cost (per 1M tokens) | $3.00 | $2.50 | | Output Cost (per 1M tokens) | $15.00 | $10.00 | | Max Output Tokens | 8,192 tokens | 16,384 tokens |

While GPT-4o is slightly cheaper on paper and boasts a massive 16k output limit (ideal for dumping out massive, single-file Swagger documents), the cost savings are quickly cancelled out if your developers have to spend manual code-review cycles correcting hallucinated endpoints.

The Verdict: Who Should Write Your Docs?

For any team where API precision is a non-negotiable metric, Claude 3.5 Sonnet is the clear winner. It reads backend code with a level of syntactic literacy that GPT-4o simply does not match. It respects the boundaries of your code, prioritises accuracy over generic conventions, and generates OpenAPI specs that you can safely plug straight into your deployment pipelines without manual edits.

GPT-4o remains an excellent tool for drafting high-level, introductory conceptual guides, or summarizing what an API does in plain English for non-technical stakeholders. But when it comes to the raw, hard schemas that keep your systems talking to one another, trust the architect over the generalist.

Need to brush up on semantic terms like JSON Schema, validation protocols, or structured outputs? Check out our quick explanations on our /glossary page.