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Venkata Manideep Patibandla
Venkata Manideep Patibandla

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I Built a Benchmark That Proves Most LLM Agents Are Statistically Blind And Why That Costs Companies Real Money

#agents #datascience #llm #showdev

RealDataAgentBench forces agents to think like actual data scientists, not just copy answers. Here’s what I learned after running 163 experiments across 10 models.

Two months ago I got tired of watching LLM agents ace toy benchmarks but fall apart on real data science work.They could write code. They could get the final number right.

But when it came to statistical validity proper uncertainty reporting, avoiding data leakage, understanding confounding variables, or choosing the right method they were guessing.

So I built RealDataAgentBench.

It is not another “does the model get the right answer?” benchmark.

It is a test track that grades LLM agents on four dimensions that actually matter in production:

  1. Correctness - does it match ground truth?
  2. Code Quality - is the code vectorized, readable, and professional?
  3. Efficiency – how many tokens and dollars does it burn?
  4. Statistical Validity – does it think like a careful statistician or just hallucinate confidence?

Every task uses fully reproducible seeded datasets. Every run is scored automatically. The leaderboard updates itself via GitHub Actions.

I currently have 23 tasks across EDA, Feature Engineering, Modeling, Statistical Inference, and ML Engineering. I have run 163+ experiments across 10 models (Claude Sonnet, GPT-4o, GPT-4o-mini, Grok models, Gemini 2.5, Llama via Groq, and more).

Some results surprised me:

  1. - GPT-4o and Claude Sonnet are extremely close in overall score.
  2. - GPT-4o is dramatically cheaper per task.
  3. - Groq Llama models are fast and cheap but sometimes skip statistical rigor.
  4. - The biggest failures are not in correctness they are in statistical validity and code quality.

That is expensive for companies. Choosing the wrong model can easily waste thousands of dollars per month in API costs and produce analyses that look correct but are statistically flawed.How the benchmark actually works (real example.

Take task eda_003 — E-Commerce Confounding Variable Detection (Hard).

The agent is given sales data that exhibits Simpson’s Paradox. It must detect the confounding variable, compute partial correlation, and explain the result correctly.

Most agents fail here. They report the aggregate correlation and confidently declare “positive relationship” while completely missing the reversal when you control for the confounder. My scoring engine catches that instantly in the Statistical Validity dimension.

The agent also has to write clean, vectorized code and stay within the token budget.This single task reveals more about a model’s real capability than 50 simple math questions.

Why this matters for companies

Small and medium companies cannot afford to test 10 different models manually. RealDataAgentBench lets them drop their own dataset in and get an immediate recommendation:

“Use GPT-4o for this data best statistical validity at 60% lower cost than Claude Opus.”I added a budget flag so even tiny teams can test safely without surprise bills. Groq support makes the first tests completely free.

What I learned building it

  1. Different models need different system prompts.
  2. Claude loves strict instructions; Grok is creative but lazy on stats.
  3. Reproducible seeded datasets are non-negotiable for fair comparison.
  4. The hardest part was not the code it was making the scoring engine statistically honest.

Open-source done right (clean README, Makefile, .env.example, proper CI) gets you real contributors and stars.

The project is 100% open source:
→ https://github.com/patibandlavenkatamanideep/RealDataAgentBenchLive leaderboard: https://patibandlavenkatamanideep.github.io/RealDataAgentBench/

Try it yourself in under 5 minutes:bash

- git clone https://github.com/patibandlavenkatamanideep/RealDataAgentBench
- cd RealDataAgentBench
- pip install -e ".[dev]"
- cp .env.example .env
- dab run eda_001 --model groq --budget 0.05
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If you work with data and LLMs, I would love your feedback. Star the repo, open an issue for a new task you want, or tell me which model surprised you the most.

This is just the beginning. I am actively expanding the task suite and adding more enterprise features.

What real data-science failure have you seen LLMs make lately? Drop it in the comments I might turn it into the next task.

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