LLM landscape
LLM Insights
Data-grounded views of the model landscape, refreshed automatically on a schedule. Each insight carries its own dataset, methodology, freshness status, and agent-written takeaways.
54
1,907
385
5
Highlights
Training compute vs capability
How published training compute relates to model capability, with measured rows, explicit assumptions, and a validation review queue.
Cost vs capability
What frontier capability costs to train — and which models deliver the most capability per dollar, traced by a Pareto efficiency frontier.
Capability per inference cost
Which models give the most capability per dollar to run — Epoch capability scores against published API prices per million tokens, with a cost-efficiency frontier.
Benchmark performance
How models score on individual public evaluations — GPQA Diamond, Humanity's Last Exam, SciCode, AA-LCR and more — ranked as a per-benchmark bar chart.
Enterprise LLM adoption
Which LLM ecosystems show up in real, source-linked enterprise deployments — quarterly shares for Azure OpenAI, Bedrock, Gemini, Claude, and open weights.
Google Gemini & Vertex AI leads on discovery rate with 10 cases per 100 exploration runs in 2026-Q2.
Model release timeline
When and how often the large AI labs ship new models — release cadence per lab, kept current by the weekly catalogue refresh and the announcement-watcher agent.