Standardized AI Benchmarks & Criteria

Long-horizon software engineering benchmark evolved from SWE-bench. Evaluates autonomous coding agents on end-to-end bug reproduction, multi-file codebase reasoning, complex patch generation, and test suite execution against real-world GitHub issues.

The gold-standard software engineering benchmark curating 500 validated real-world GitHub issues from top open-source Python repositories, verified by professional software engineers.

Continuously updated, contamination-free evaluation benchmark collecting real-world contest problems (LeetCode, AtCoder, Codeforces) with strict time-window partitioning.

Evaluates AI agents on interactive command-line terminal coding, package dependency resolution, environment configuration, build debugging, and automated scripting inside a live Linux shell.

Advanced terminal-based agent benchmark testing multi-step system administration, networking configuration, distributed systems debugging, and complex exploratory autonomy over long command horizons.

Realistic multi-application desktop environment benchmark testing multimodal agents operating real operating systems (Ubuntu/macOS) via keyboard, mouse, CLI, and native GUI software (VS Code, LibreOffice, GIMP, Browser).

Comprehensive evaluation of autonomous agents executing economically valuable knowledge-work tasks across consulting, strategy, operations, financial modeling, and policy analysis.

Enterprise financial analysis benchmark developed by Vals AI. Evaluates AI agents on real-world Wall Street financial analyst tasks, including 10-K/10-Q filing analysis, discounted cash flow (DCF) valuation models, earnings report reconciliation, and investment memorandum generation.

Benchmark developed in collaboration with Harvey AI to measure generative AI agents handling highly rigorous legal workflows, including cross-jurisdictional contract drafting, statutory interpretation, regulatory compliance audits, and litigation discovery.

Rigorous multimodal benchmark comprising 2,323 complex, high-resolution scientific charts extracted from arXiv papers. Tests fine-grained perceptual extraction and high-level scientific reasoning without external code interpreters.

Extreme long-video benchmark containing videos ranging from 30 minutes to several hours. Tests long-term temporal reasoning, episodic event memory, causal understanding, and cross-hour entity tracking.

Created by CAIS and Scale AI and published in Nature. Comprises 2,500 expert-crafted, closed-ended questions across 100+ academic disciplines designed to test the boundary of frontier model reasoning beyond saturated MMLU levels.

OpenAI-created standard coding benchmark comprising 164 hand-written Python programming problems assessing language comprehension, algorithms, and basic logic.

Rigorous interactive code editing benchmark measuring an agent’s ability to correctly edit existing codebases, apply unified diffs, and satisfy unit tests across 133 Exercism exercises.