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Frontier Model Efficiency Gains

Capability per unit of training and inference compute keeps improving, letting newer models match prior frontier performance far more cheaply and gradually loosening the link between raw scale and capability.

strengthening · confidence 100 · +8 7d · +5 30d · Medium term (3-9 months) · tracking since June 28, 2026 · updated October 12, 2026

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Score history

Daily conviction score, 0 to 100. Higher means the thesis is more strongly corroborated.

Oct 11 · 100Oct 12 · 100

Now 100

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Why the conviction moved

  • Oct 12
    Strengthened +1

    Several small-model papers, including Kairos's linear-attention hybrid and integer-arithmetic inference formats, aim at getting more capability out of limited training and inference compute. They are incremental, research-stage evidence that efficiency gains are loosening the link between scale and capability.

  • Oct 12
    Strengthened +3

    Anthropic reports that Haiku 5.5 scores 72.4% on OSWorld while costing one tenth of its predecessor. A small-tier model now delivers agentic computer-use performance near the frontier, more evidence that capability per unit of serving cost keeps improving.

  • Oct 11
    Strengthened +3

    Haiku 5.5's OSWorld score rose from 15.7% to 72.4% while its price fell 10x and its context grew to one million tokens. A small-tier model now does computer-use work that was beyond the previous generation, at a fraction of the serving cost.

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Source trail

  • Supporting · October 12, 2026

    Anthropic prices Claude Haiku 5.5 at one tenth of its predecessor and gives it a one-million-token context window

    Anthropic reports that Haiku 5.5 scores 72.4% on OSWorld while costing one tenth of its predecessor. A small-tier model now delivers agentic computer-use performance near the frontier, more evidence that capability per unit of serving cost keeps improving.

    Anthropic News
  • Supporting · October 12, 2026

    Small-model papers test linear attention, integer number formats and new sequence models for compute-limited training

    Several small-model papers, including Kairos's linear-attention hybrid and integer-arithmetic inference formats, aim at getting more capability out of limited training and inference compute. They are incremental, research-stage evidence that efficiency gains are loosening the link between scale and capability.

    arXiv cs.LG (Kairos)

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