Hey folks! 👋 I've been helping our data team onboard a few different LLM APIs for internal analysis tasks—think summarizing trends, spotting outliers in customer feedback, and generating basic SQL queries from natural language.
We're hitting a wall comparing the newer "reasoning" models (like Claude 3 Opus, GPT-4-Turbo, maybe Gemini 1.5 Pro) specifically for this use case. Everyone talks about benchmarks, but I need real-world data on cost vs. output quality for messy, tabular data. Has anyone run a side-by-side test on actual business data? I'm especially curious about consistency when you throw a tricky, multi-step question at it.
What was your setup? Any surprises on latency or reliability during a batch run?
Happy customers, happy life.
We ran a comparative test last month on a few hundred messy support tickets with embedded CSV snippets. The biggest surprise wasn't output quality, it was consistency and cost.
Opus gave the most nuanced reads but was glacially slow for batch and tripled our projected API bill. GPT-4-Turbo was fast but occasionally hallucinated column names that didn't exist in the provided schema. Gemini 1.5 Pro had weird, silent truncation on long tables that corrupted the analysis.
Forget the benchmarks. You need to test against your own worst-case data shape and volume, because the failure modes are entirely different. And always, always validate the SQL it generates before letting it touch a production DB.
Just my 2 cents
Spot on about testing with your own worst-case data shape. We saw the same silent truncation issue with Gemini on wide tables, but we found a workaround.
If you pipe the table data through a quick preprocessing step to chunk it by columns instead of rows, Gemini 1.5 Pro handles it much better. The cost stays low, you just add a bit of engineering time.
The "validate the SQL before production" point can't be overstated. We built a simple step that runs every generated query against a small, mirrored sample dataset first. It catches those hallucinated column issues from GPT-4-Turbo immediately.
Automate the boring stuff.
We literally just wrapped up a similar test! Our focus was on spotting trends and outliers in sales transaction logs, which are super messy with lots of missing fields.
Our biggest takeaway aligned with your need for consistency on tricky questions. We found that for multi-step logic, like "flag orders where the discount was high but the shipping zone was expensive," GPT-4-Turbo was fastest but sometimes skipped a condition under heavy load. Claude 3 Opus nailed every single step perfectly - that model's logic is incredible - but the latency made real-time use a no-go for us.
Cost and reliability were the real shocks. Opus's cost added up so fast for batch analysis. And we had Gemini 1.5 Pro just... stop responding mid-stream twice during a large overnight job, no error message, which was a nightmare. You really do have to test with your own ugliest data at the volume you plan to use. Benchmarks didn't predict any of that.
Always testing.