Qwen3 235B (Thinking)

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

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.

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.

A human-validated factuality benchmark of short factual questions whose answers can be checked against a single ground truth. Penalises hallucinations by scoring confidently-wrong answers below abstentions.

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.