How is The Frontier AI Model Race (2026) constructed?
Published by Deepstory Research as the methodology attached to The Frontier AI Model Race (2026). Scope and source attribution are bounded by that Evidence Brief.
Procedure and scope
Source-backed values are seeded for four of the five charts: the release cadence by lab (2024 → 2026, from each lab’s release notes), the capability-vs-cost scatter (Artificial Analysis Intelligence Index vs output $/M tokens, June 2026 snapshot), training-compute growth by year (Epoch AI, corroborated by Stanford HAI), and a safety-gated capabilities matrix. Every numeric point carries a sources[].ref and a value_basis. Sources are each lab’s own model cards/release notes, Artificial Analysis, Epoch AI, and Stanford HAI — cross-checked against public release timelines. EDITORIAL ENCODING: the safety-gated matrix scores each model × domain as 3 = allowed / 2 = gated / 1 = blocked. This is an interpretation of each model’s published safety policy, not a measured benchmark. PLACEHOLDER: the per-lab benchmark-trajectory chart is left unseeded — a consistent historical per-quarter, per-lab benchmark series was not sourceable without mixing incompatible benchmarks. Re-verified 2026-06-15.
Known failure modes and limits
- Exact, lab-disclosed training compute (FLOP figures are third-party estimates).
- Real-world safety outcomes (the matrix is a posture summary on an editorial scale).
- Market share or revenue by lab (not in this index).