Qwen3 235B

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.

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.

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.

Contamination-controlled average across seven rolling task categories (reasoning, coding, agentic coding, mathematics, data analysis, language, instruction following). Questions are rotated every six months and ground-truth answers are objective, removing the need for LLM-as-judge scoring.

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.

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.