Claude Sonnet vs GPT-4o: Better for SQL & JSON?

8/12/2025

The Nitty-Gritty: Is Claude Sonnet Actually Better Than GPT For SQL & JSON?

Alright, let's talk about the real-world, rubber-meets-the-road stuff when it comes to AI models. There's a TON of buzz, a lot of hype, & frankly, a lot of noise. Every week there's a new "best" model. The latest showdown everyone's watching is between Anthropic's Claude & OpenAI's GPT series. The original premise floating around was something like "Claude Sonnet 4 Outperforms GPT-5," but honestly, that's not the whole story. GPT-5 isn't even fully out yet, & the comparisons we can make are way more interesting & nuanced.

So, let's get into it. As someone who spends a lot of time wrangling data & making AI do useful things, I've been kicking the tires on these models, specifically for two key tasks that are crucial for a LOT of businesses: SQL generation & JSON processing. These are the building blocks of data analysis, automation, & so much more.

Here's the thing: it's not a simple knockout. It's more like a chess match. Each model has its strengths & makes strategic moves the other can't. The question isn't "which is better?" but "which is better for what?"

The Great SQL Showdown: Translating Plain English into Database Queries

Text-to-SQL is one of the holy grails of business intelligence. The dream is for any manager, marketer, or salesperson to just ask a question in plain English & get back a perfectly formed SQL query that pulls the exact data they need. No waiting for a data analyst, no fumbling with complex joins. We're getting closer to this reality, & Claude 3.5 Sonnet & GPT-4o are at the forefront.

Some super detailed benchmark tests have been run on this, & the results are pretty fascinating. It's not as simple as one model winning across the board.

Where GPT-4o Flexes Its Muscles

Let's be clear: GPT-4o is a powerhouse. When it comes to raw speed & handling massive, sprawling databases, it has a definite edge.

Speed & Efficiency: Across the board, from simple to complex queries, GPT-4o is just plain faster. In one test on complex queries, it generated the SQL 42.4% faster than Sonnet. It also tends to use fewer tokens to get the job done, which can make it more cost-effective, especially at scale. For a business building a customer-facing analytics tool where speed is critical, this is a HUGE plus.
Wrestling with Giant Schemas: Got a database with thousands of tables? The kind of complex enterprise environment that makes junior developers cry? GPT-4o seems to be the top choice here. Benchmarks show that when the number of tables gets really large (over 1,200), GPT-4o is about 5% more accurate at picking the right tables than Sonnet. It's just a bit better at seeing the whole messy picture & navigating it.
Interactive Conversations: Data analysis is rarely a one-shot deal. It's a conversation. You ask a question, get an answer, & then have a follow-up. "Great, now can you filter that by region?" or "Okay, but only show me customers who also bought product Y." In these interactive scenarios, GPT-4o currently does a better job of remembering the context from the previous question & correctly applying it to the new one.

Where Claude Sonnet Shines with Finesse

So, GPT-4o wins on speed & scale. Case closed? Not so fast. Claude Sonnet has a different set of skills that are, in some cases, even more valuable.

The Art of the Description: Here’s a subtle but CRUCIAL point. When an AI generates a query, it's not just about the code. It's about understanding the data. In tests where the models were asked to generate descriptions for the columns in a database, Sonnet was significantly better—over 6% better, in fact. Its descriptions were more detailed & easier for a human to understand. Why does this matter? Because a model that understands the nuances of your data (e.g., the subtle difference between
1total_spent
&
1total_price
) is less likely to make logical errors in the queries it writes.
Handling the "Medium-Sized" World: Not everyone is a mega-corporation with 1,200 tables. Many businesses operate on medium-sized databases (say, around 200 tables). In this common scenario, Sonnet actually performed better than GPT-4o, showing a 3.34% higher accuracy in table selection. It seems to hit a sweet spot in this range.
Code Quality & Thoughtfulness: This is a bit more subjective, but many developers, myself included, have noticed it. Claude's code, whether it's SQL, Python, or something else, often feels... cleaner. More thoughtful. It's been described as producing "nearly bug-free code on the first try." In the SQL context, this can mean avoiding unnecessary joins or choosing more logical aliases. While GPT-4o might get you a working query faster, Sonnet's query might be the one you'd prefer to maintain in a production system.

This is where the practical application really comes into play. Imagine you're trying to empower your support team. You don't want them writing SQL, but you want them to be able to answer complex customer questions about their accounts. This is a perfect use case for an internal tool powered by AI.

This is where a platform like Arsturn comes in. You could build a custom AI chatbot trained on your company's database schema & documentation. Your support agent could ask the chatbot, "How many times has customer XYZ contacted us in the last 6 months, & what were the reasons?" The AI, using a model like Sonnet or GPT-4o, would generate the SQL query behind the scenes, run it, & provide a plain-English answer. By leveraging Sonnet's strength in understanding column nuances, you could build a more reliable internal tool that makes fewer mistakes.

The JSON Juggling Act: Structuring the Unstructured

Okay, so that's SQL. What about JSON? JSON (JavaScript Object Notation) is the language of APIs, webhooks, & modern web development. Being able to correctly generate, parse, & manipulate JSON is non-negotiable for any kind of automation or integration. If an AI can't handle nested JSON objects with grace, it's not going to be very useful.

There aren't as many head-to-head "JSON processing" benchmarks as there are for SQL. But we can infer a LOT from the models' performance in coding & reasoning tasks.

Why Claude Has the Edge in Nuanced JSON Tasks

Superior Coding & Reasoning: Across multiple benchmarks, Claude 3.5 Sonnet has shown a real advantage in coding proficiency. It solved 64% of problems in an agentic coding evaluation, a significant jump over its predecessor. This ability to understand complex logic & structure translates directly to handling JSON. Generating a complex, nested JSON object is fundamentally a coding task that requires precision & an understanding of hierarchies.
Human-Like Text Generation: One of the most common praises for Claude is that its output just feels more natural & less "AI-generated." This might sound like a cosmetic point, but it's not. It points to a deeper understanding of context & nuance. When you're trying to extract information from a messy block of text & structure it into a clean JSON object, that nuanced understanding is EVERYTHING. It's the difference between correctly identifying "the main office address" & just grabbing the first address it sees.
Larger Context Window: Claude 3.5 Sonnet boasts a 200K token context window. That's massive. It means you can feed it a huge amount of information—like a long, rambling customer support ticket or a lengthy product description—& ask it to extract specific entities into a structured JSON format without losing the plot. It can "remember" details from the beginning of the document when making decisions at the end.

Let’s think about a real-world business problem. Say you want to automate lead qualification. A potential customer fills out a form on your website with a "How can we help you?" free-text field. That text is a goldmine, but it's unstructured.

You could use an AI to process this. The goal is to turn that text into a structured JSON object like this: