Anthropic claims their new Claude Sonnet 4.6 delivers "Opus-level coding at Sonnet pricing." If true, this could change how we think about AI-assisted data engineering. I spent the last week testing it against GPT-4 on real data projects.
The AI coding assistant market has been frustrating for data engineers. GPT-4 is solid but expensive for routine tasks. Claude Opus was impressive but cost-prohibitive for daily ETL work. Most of us have been stuck with good-enough solutions that don't quite justify their price tags. Sonnet 4.6 promises to bridge that gap.
What I Actually Tested
I ran both models through five scenarios I encounter regularly in client work. Complex SQL transformations taking raw sales data and building reporting tables. Python ETL scripts that clean and standardize customer data across multiple sources. Data quality checks that flag inconsistencies and missing values. Database schema design for new reporting requirements. Documentation generation for existing data pipelines.
Each test used real project requirements, anonymized but keeping the actual complexity. I measured accuracy, code quality, time to useful output, and total cost per task.
SQL Generation Results
Claude Sonnet 4.6 impressed me here. I gave it a complex requirement from a retail client: "Build a query that calculates rolling 90-day average order values by customer segment, excluding refunded orders, with month-over-month growth rates."
Sonnet 4.6 generated clean SQL with proper window functions, correct date filtering, and logical table joins. GPT-4 produced similar quality but took more back-and-forth to get the edge cases right. Both handled complex aggregations well, but Sonnet 4.6 seemed to understand the business context better on the first try.
Winner: Slight edge to Claude Sonnet 4.6 for fewer iterations needed.
Python ETL Performance
This is where the differences became obvious. I asked both models to write a script that reads customer data from three different APIs, standardizes phone numbers and addresses, deduplicates records, and loads the clean data into PostgreSQL.
GPT-4 produced solid, well-structured code with proper error handling and logging. Claude Sonnet 4.6 generated similar functionality but with better pandas optimization and more thoughtful exception handling around API rate limits.
The real difference was debugging. When I introduced a deliberate error in the data source format, Sonnet 4.6 diagnosed and fixed it faster. GPT-4 got there eventually but needed more guidance.
Winner: Claude Sonnet 4.6 by a meaningful margin.
Documentation and Schema Design
For documentation generation, I fed both models an existing ETL pipeline and asked for comprehensive documentation. GPT-4 produced thorough but somewhat generic docs. Sonnet 4.6 created more useful documentation with better explanations of business logic and clearer troubleshooting sections.
Schema design was close to even. Both models understand database normalization and can design reasonable table structures. Neither replaced the need for human judgment on indexing strategy or partition decisions.
Winner: Tie, with slight preference for Sonnet 4.6's documentation style.
Real Project Example
Last month, a healthcare client needed to migrate reporting data from an aging SQL Server instance to a modern PostgreSQL setup. The migration required changing 15 tables, updating data types, and rebuilding complex views that powered their patient analytics dashboards.
I used GPT-4 for the initial migration scripts and Claude Sonnet 4.6 for the view reconstruction. Sonnet 4.6 handled the complex nested queries and window functions better, especially for calculating patient visit patterns across different time windows. The generated code needed minimal tweaking before production deployment.
Total cost for AI assistance on this project: $23 with GPT-4, $31 with mixed usage including Sonnet 4.6. The extra cost was worth it for the reduced debugging time.
Cost Analysis That Actually Matters
Here's where Sonnet 4.6 changes the game. For typical data engineering tasks, I'm seeing about 40% lower costs compared to GPT-4, while getting equal or better output quality. On a monthly basis, that's the difference between $200 and $120 for AI-assisted development.
More significant, the faster iteration cycles mean less billable time spent wrestling with AI-generated code. When the AI gets it right on the first or second try instead of the fourth try, everyone wins.
The Practical Takeaway
Claude Sonnet 4.6 isn't game-changing, but it's meaningfully better for data engineering work. Better cost-performance ratio, fewer debugging cycles, and stronger understanding of data context. If you're doing regular ETL development, schema design, or SQL optimization, it's worth switching.
The biggest impact isn't the raw capabilities. It's that good AI assistance is now affordable enough to use on routine tasks, not just the complex problems. That changes how you approach daily data work.
Top comments (0)