RealDataAgentBench
Automated model-selection engine for production LLM deployments — 12 frontier models, 1,412+ runs, real-world tasks
RealDataAgentBench (RDAB) is an automated benchmarking system built to answer a production engineering question: which model should I deploy, and what will it cost?
It runs 1,412+ evaluations across 12 frontier LLMs on real data science tasks — EDA, feature engineering, statistical inference, ML modeling — and produces cost-accuracy trade-off data that directly powers CostGuard’s model-selection and recommendation engine.
What It Produces
| Finding | Production implication |
|---|---|
| GPT-4.1 = 97% of GPT-5’s score at 1/15th the cost | Default to GPT-4.1 for most production tasks |
| Free Llama 3.3-70B (0.798) outscores GPT-5 (0.780) | Open-source is viable at scale |
| Statistical validity varies 0.45–0.87 across models | Correctness alone is a misleading selection metric |
| Claude leads statistical validity; GPT-4.1-mini leads correctness | Task-aware routing beats one-model-fits-all |
Engineering Design
- Multi-dimensional scoring — correctness, code quality, efficiency, and statistical validity — because a model that gets the right answer via the wrong method fails in production
- 95% confidence intervals across 1,412+ runs for statistically reliable comparisons
- Category-level breakdowns across inference, feature engineering, EDA, modeling, and ML engineering — enabling task-aware model routing decisions
Benchmark Structure
39 tasks · 5 categories · 6 real-world datasets · 1,412+ runs · 12 models · 95% confidence intervals
Tech Stack
Python · LangChain · OpenAI API · Anthropic API · HuggingFace · scikit-learn · Pandas