Categories › AI & Agents › Models, LLM APIs, routing, serving, training & GPUs
LLM rankings from public benchmarks, model comparisons and source-linked results. Updated daily.
Remote server: your requests and data go to theaggregate.ai. Claude Code: claude mcp add --transport http the-aggregate-llm-benchmark-aggregate https://theaggregate.ai/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
get_leaderboard — Top of the cross-benchmark aggregate ranking: published Aggregate Ratings combine public benchmark evidence on one scale (call about_the_aggsearch_models — Find ranked models by (partial) name or provider. Returns rank, Aggregate Rating and the model page URL. One row per model by default, fusedget_model — One model in depth: aggregate rank, Aggregate Rating with standard error, provider, what it is, cost per task where known, and its most notacompare_models — Head-to-head between 2-4 models: aggregate ranks, rating gap with a significance note based on the standard errors, and notable benchmarks tsearch_benchmarks — Find benchmarks in the aggregate by (partial) name. Returns model coverage, difficulty on the rating scale, and the benchmark page URL.get_benchmark — One benchmark in depth: what it measures, the original source leaderboard URL, IRT stats (difficulty, noise, model coverage), skill weights,get_prediction_duel — Guesswork — the public prediction duel: every day frontier LLMs and The Aggregate's own IRT model predict newly scraped benchmark scores befabout_the_aggregate — What this data is: the published rating method, current coverage counts, update cadence, and how to cite it.Not tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.