LLM Data Agents: The Benchmark That Could Change Everything

Recent advances in large language models (LLMs) are reshaping data science. Yet, the real-world problems these models tackle are often open-ended and complex, lacking standardized solutions. That's where DSAEval comes in. This new benchmark evaluates LLM-based data agents on 641 real-world data science problems, using 285 diverse datasets. From structured spreadsheets to unstructured text and images, DSAEval covers it all.

The Benchmark's Key Features

DSAEval isn't just another benchmark. It introduces Multimodal Environment Perception, allowing agents to interpret data from multiple modalities such as text and vision. This is important. Real-world data rarely fits into neat categories. Then there's Multi-Query Interactions, which simulate the iterative nature of data science work. Finally, Multi-Dimensional Evaluation provides a comprehensive assessment of reasoning, coding, and results.

This isn't about hitting a single target. It's about understanding the full scope of what these models can do, or can't, across different types of data and tasks. Here's the relevant code. You'll want to clone the repo, run the test, then form an opinion yourself.

Performance Insights

Now, let's talk numbers. Claude-Sonnet-4.5 leads the pack in overall performance. Meanwhile, MiMo-V2-Pro shines in duration efficiency, and GPT-5.2 is a step efficiency champ. MiMo-V2-Flash takes the crown for cost-effectiveness. These results highlight the varied strengths of different models.

But don't get too excited yet, multimodal perception only boosts vision tasks by 2.04% to 11.30%. It's a start, but there’s room for improvement. Current agents handle structured data with ease. However, tackling unstructured data remains a challenge. Is this where the real innovation will come?

Why It Matters

These insights aren't just academic. They're setting the stage for what's next in data science automation. If you're in this field, you need to pay attention. DSAEval isn't just a benchmark. It's a roadmap for where LLMs are heading. The potential for change is enormous, but the journey is just beginning.

So, what's the next step? More research is needed, focusing particularly on unstructured data. Multimodal capabilities must be further refined. The SDK handles this in three lines now, but how it evolves could redefine industry standards. Ship it to testnet first. Always.

Ultimately, while current data science agents are impressive, they're not infallible. They highlight the gap between structured and unstructured data tasks. And understanding this gap is the first step toward closing it.