Best Data analytics AI prompts (2026)

Benchmark for measuring how well AI agents perform at machine learning engineering. Evaluates agents on 75 Kaggle competitions covering diverse ML tasks. MIT licensed

Benchmarking system for evaluating LLM models as OpenClaw coding agents. Built with Rust by the kilo.ai team. MIT licensed

Comprehensive benchmark to evaluate LLMs as agents across 8 diverse environments including household, web shopping, OS interaction, and database tasks. ICLR 2024. Apache 2.0 licensed

Continuously updated benchmark with 21,000+ real-world SWE tasks for evaluating agentic LLMs. Decontaminated, mined from GitHub

Leaderboard comparing LLM performance at producing hallucinations when summarizing short documents. Systematic evaluation of factual consistency across major models. Apache 2.0 licensed

Open-source evaluation toolkit for large multi-modality models (LMMs). Supports 220+ LMMs and 80+ benchmarks including MMMU, MathVista, and ChartQA. Powers the OpenVLM Leaderboard. Apache 2.0 licensed

Industry-standard ML training benchmarks from MLCommons. Reference implementations for training AI models at scale across image classification, object detection, NLP, and recommendation tasks. Apache 2.0 licensed

Industry-standard ML inference benchmarks with reference implementations for AI accelerators

Evaluation platform for benchmarking language and multimodal models across large benchmark suites

Real-world multi-step agentic benchmark

Evaluates LLMs on real-world GitHub issues from 15+ Python repositories

More challenging MMLU-style benchmark suite for evaluating advanced language models with expert-level reasoning questions

Holistic Evaluation of Language Models

De-facto standard for generative model evaluation

Contamination-free LLM benchmark with objective ground-truth scoring. ICLR 2025 spotlight paper featuring frequently-updated questions from recent sources. Tests math, coding, reasoning, language, instruction following, and data analysis