GLM 5.1 — Benchmarks

GPQA Diamond is a set of extremely hard, graduate-level science questions (physics, chemistry, biology) written by domain experts and filtered so that skilled non-experts with web access still fail. It measures genuine reasoning rather than memorization.

SimpleBench is a set of everyday, common-sense and trick questions that humans answer easily but language models often get wrong. It probes basic reasoning and robustness rather than specialist knowledge.

APEX-Agents (AI Productivity Index) is Mercor's evaluation of AI agents on long-horizon, multi-application professional-services work — investment-banking, management-consulting, and corporate-law style tasks — graded against expert-produced solutions. The score is Pass@1, the fraction solved correctly on the first attempt.

Chess Puzzles is an Epoch AI evaluation that asks models to find the winning move in tactical chess positions drawn from real games. Because each puzzle has one verifiable correct answer and no natural-language shortcut, it isolates concrete multi-step planning.

CritPt (Critical Points) tests graduate-level physics problem-solving on research-style questions spanning mechanics, electromagnetism, quantum mechanics, and more. It was created by physicists to probe whether a model can reason through multi-step physics rather than pattern-match textbook answers.

Vending-Bench 2, from Andon Labs, is a long-horizon agent benchmark in which the model runs a simulated vending-machine business for a year, handling suppliers, pricing and cash flow. The score is the final money balance in US dollars.

SciCode asks a model to write real scientific-computing code — implementing multi-step numerical methods drawn from physics, chemistry, biology, and math research — and checks the output against reference solutions. It was built by a team of scientists and ML researchers to test coding ability on genuine research problems, not typical software tasks.

WeirdML asks models to write machine-learning code for small, deliberately unusual datasets, then runs the code and scores the accuracy it actually achieves. Built by Håvard Tveit Ihle, it rewards practical problem-solving over recognizing a familiar task.

ALE-Bench, from Sakana AI and AtCoder, tests long-horizon algorithmic optimisation using problems drawn from AtCoder Heuristic Contests, where there is no single correct answer and solutions are scored on quality. Results are reported as an AtCoder-style performance rating, where higher is better.

ExploitBench, from Carnegie Mellon University researchers Seunghyun Lee and David Brumley, scores AI agents on writing working exploits for 41 real, already-patched bugs in Chrome's V8 JavaScript engine, with modern mitigations switched on. Instead of pass/fail it grades 16 stages from crash up to arbitrary code execution, and Epoch AI reports the mean capability reached.

AIME (American Invitational Mathematics Examination) is a prestigious high-school competition of hard, integer-answer problems. It's a widely-cited yardstick for multi-step mathematical reasoning, here on the 2024–2025 papers.

FrontierMath is a benchmark of exceptionally hard, original research-level mathematics problems created with professional mathematicians. Even the strongest models solve only a small fraction, making it a frontier measure of genuine mathematical ability.

ProofBench evaluates a model's ability to write and check rigorous mathematical proofs, rather than just produce a final numeric answer. It probes step-by-step logical correctness, which is a harder and more distinct skill than solving for a number.

SimpleQA measures factual accuracy on short, fact-seeking questions with a single correct answer — directly probing how often a model is right versus confidently wrong (hallucination) on simple facts.

Arena (arena.ai, formerly LMArena / Chatbot Arena) ranks models by millions of blind, head-to-head human votes on real prompts. The Arena Score shown here is a Bradley-Terry rating computed with Arena's default style control, so higher means people preferred that model's answers more often — it's a relative rating, not a percentage, so compare it only to other models on this board.