danielhuber.dev@proton.me Thursday, July 9, 2026

Agent Benchmarks

Original benchmarks built and run on this site — plus a curated set of external agent benchmarks, linked to their sources.


Original benchmark

LLM Trading Decisions →

5 models · 20 scenarios · 540 backtest runs

Which language models make the best crypto-futures trading calls, and under what market conditions? A deterministic backtest of agentic vs. single-call LLM traders across 20 historical scenarios, measured against buy-and-hold, technical-analysis, and random baselines. Every run is computed and aggregated here — leaderboard, per-regime performance, and per-model breakdowns.

View the benchmark →

External benchmarks

Third-party evaluations we follow. Scores and leaderboards live at each source — follow the links for current numbers.

  • SWE-bench Pro ↗

    Uncontaminated bug-fix and feature tasks across Python, Go, TypeScript, and JavaScript in actively maintained repositories.

  • SWE-bench Verified ↗

    Human-verified subset of GitHub issues; agents generate patches that must pass the repository test suite. Widely cited, though facing data-contamination concerns.

  • Terminal-Bench Hard ↗

    Terminal-environment tasks — compiling code, training models, configuring servers, debugging — executed in Docker containers (Stanford / Laude Institute).

  • LiveCodeBench ↗

    Contamination-free coding benchmark that continuously harvests fresh competitive-programming problems from LeetCode, AtCoder, and CodeForces.

  • WebArena ↗

    Realistic multi-step web tasks across five self-hosted sites (shopping, Reddit, GitLab, OpenStreetMap, Wikipedia).

  • GAIA ↗

    General-assistant questions requiring web search, file processing, and multi-step reasoning across three difficulty levels.

  • GDPval-AA ↗

    Real-world agentic work across dozens of occupations; models produce documents, slides, and spreadsheets, scored by blind pairwise comparison.

  • τ²-bench (Tau²-bench) ↗

    Dual-control conversational benchmark (Sierra Research) where both agent and user modify shared state in a telecom support domain.

  • TheAgentCompany ↗

    Tasks inside a simulated software company — browsing, coding, running programs, and messaging simulated coworkers (CMU).