Capability Index

Third-party evaluator METR disclosed three unreleased Anthropic internal models, Model 2/3/4, trained between July and October this year. The strongest, Model 4, scores 178.6 on Anthropic's AECI capability index, roughly 3.9 points above Opus 5.5 released six weeks earlier—equivalent to about three months of normal progress. Co-founder Sam McCandlish acknowledged AI is accelerating Anthropic's own R&D, while noting the public report omits substantial detail.

METR's independent assessment concludes that as of October 2026, Anthropic's rate of capability improvement on well-specified tasks is roughly 2x the March 2024 to October 2025 pace, with a range of 1.3x to 3x, and that about 80% of the acceleration may stem from heavy internal use of AI in R&D. The key quantitative evidence is a 1.9x acceleration in the AECI index (95% CI 1.5-2.8x), with no clear bottlenecks or diminishing returns observed, suggesting further acceleration over the next 12 months.

Artificial Analysis compared how many Intelligence Index tasks a fixed $100 budget buys: roughly 40 tasks with Opus 5.5 at high reasoning effort versus about 166 for DeepSeek V4.1 Flash and 2,500 for GPT-6 Luna, exposing a wide spread in per-task inference cost across frontier models. In parallel, Apple cut engineers' daily AI usage budgets from $300 to $200, with plans to drop to $100 and $25 for some teams, signalling that enterprises now treat cost-per-task as a core deployment metric. The figures are observational and come without disclosed methodology; no model release or capability upgrade is involved.