Claude Opus 4

Crowdsourced pairwise human preference rankings of LLM responses. Higher Elo means more frequently preferred by users.

American Invitational Mathematics Examination 2024 problems. Three-digit integer answers; very hard for non-reasoning models.

Mathematical research problems spanning analysis, algebra, combinatorics and number theory. Tiers 1-3 are progressively harder; even frontier reasoning models only solve a small fraction. The hardest publicly reported benchmark for general mathematical reasoning.

AIME-style competition problems written specifically for the OTIS mock contest, then run as an evaluation by Epoch AI. Closer in spirit to the public AIME but with novel problems unlikely to appear in training data.

164 hand-written Python programming problems scored by passing unit tests. Saturated for frontier models.

Continuously refreshed coding benchmark drawing from LeetCode, AtCoder, and Codeforces; reduces benchmark contamination.

Real-world refactoring and bug-fix tasks across multiple programming languages, scored by whether the model produces a passing patch in Aider's edit format. Tests practical coding ability beyond single-file generation; harder than HumanEval and not yet saturated.

Real GitHub issues solved end-to-end. Verified subset is a 500-task human-validated slice of SWE-bench.

Long-horizon shell-and-filesystem tasks executed in a sandboxed terminal, scored by whether the agent's final state matches a target state. Tests practical tool-using ability for everyday devops and data-wrangling work; one of the hardest agentic benchmarks today.

Massive Multi-discipline Multimodal Understanding; college-exam level questions with images across 30+ subjects.

Math reasoning over visual contexts (charts, figures, geometry).

Long-context retrieval and reasoning suite. We report the 128k token effective-context score.

Median output speed in tokens per second over the last 14 days on the model maker's own API, measured nightly with a fixed ~800-token prompt and ~250-token answer. Models not yet probed fall back to a dated manual figure. Higher is faster.

Median time from request to first streamed token in milliseconds over the last 14 days on the model maker's own API, using the same nightly probe prompt. Lower is snappier; reasoning models are penalised here because they think before talking.

Contamination-controlled average across every task category in the latest rolling release (e.g. reasoning, coding, data analysis, research, browsing, long-horizon tasks). Questions are rotated with each release and graded against objective ground truth, removing the need for LLM-as-judge scoring. Only models with a score in every category get an average.

Rolling contamination-controlled data-analysis evaluation. Table comprehension, CSV / spreadsheet reasoning, SQL-style joins, and chart interpretation. Refreshed every six months with new tables and questions to minimise contamination.

A challenging multi-disciplinary exam aggregating expert-written questions from across academic fields. Designed to discriminate at the very top of the capability range when MMLU-style tests saturate.

Second-generation ARC challenge testing fluid reasoning over abstract visual puzzles. Resists training-data memorisation by construction: each puzzle is novel and solutions require multi-step pattern induction. Frontier models are only just starting to score above chance on the harder tier.

Saturation-resistant composite capability score stitched together from ~40 underlying benchmarks using Item Response Theory. Each benchmark is weighted by its fitted difficulty and discriminative slope, so doing well on hard, contamination-resistant evals (FrontierMath, ARC-AGI 2, Humanity's Last Exam) moves the score and saturated benchmarks contribute almost nothing. Imported per-model from Epoch AI's published index; we anchor it to the same min-max scale we use for every other benchmark so it's directly weightable in scenarios.