We get this question a lot: "Why don't you just pick one AI model and stick with it?" Fair point. Most tools lock you into whatever LLM they've partnered with or built on top of.
But here's why we think that's a mistake: different models are genuinely better at different things. It's not just marketing talk – we've seen it in real-world usage across thousands of queries.
The honest truth about AI models
No single model is perfect for everything. GPT-4 is incredibly versatile and great at understanding complex instructions. Claude excels at nuanced reasoning and longer context windows. Grok brings a different perspective with its real-time data training.
When you're stuck with just one model, you're betting that it's the best choice for every single question you'll ever ask. That's a big bet.
Real examples from our users
One customer told us they use GPT for quick data explorations, Claude for detailed analysis of research papers, and Grok when they need the most recent information. They're not overthinking it – they just grab whichever one feels right for the task.
Another team uses Claude exclusively because their queries often involve massive datasets with lots of context. The extended context window makes a real difference for them.
The point is: you know your work better than we do. Why should we force you into one box?
It's also a hedge against AI drift
Here's something that doesn't get talked about enough: AI models change. OpenAI updates GPT, Anthropic ships new versions of Claude, and sometimes those updates... don't work as well for specific use cases.
If you've built your entire workflow around one model and it suddenly gets worse at the thing you need most, you're stuck. With multi-model support, you've got options.
The technical side
Supporting multiple models isn't trivial. Each one has different APIs, rate limits, context window sizes, and quirks. We handle all that complexity so you don't have to think about it.
You can switch models mid-conversation if you want. Your chat history stays intact, your data connections don't break, and the transition is seamless.
Performance varies more than you'd think
We've run informal benchmarks on common data analysis tasks. For SQL generation, the models often produce similar results. But for interpreting results, explaining findings in plain language, or suggesting next steps in analysis? The differences are noticeable.
Claude tends to give more thorough explanations. GPT is usually faster and more concise. Grok sometimes catches patterns the others miss because of its training data differences.
None of this is scientific – it's just what we've observed. Your mileage will vary based on your specific data and questions.
So which one should you use?
Honestly? Try them all and see what clicks. We default to GPT because it's familiar to most people, but you can switch any time.
Some people find one model they love and stick with it forever. Others bounce between models depending on the task. Both approaches work fine.
The whole point is flexibility. Your data analysis needs are unique, and we're not going to pretend one AI model can handle everything perfectly.