Claude 3.5 Sonnet

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.

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 sustained output speed in tokens per second on the model's first-party API for medium-length prompts. Higher is faster.

Median time from request to first output chunk in milliseconds on the model's first-party API for medium-length prompts. Lower is snappier; reasoning models are penalised here because they think before talking.

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.

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.