Argon undercuts frontier rivals as cost per task varies 3.8x
A Tomasz Tunguz analysis cites Artificial Analysis data (retrieved October 2026) showing five frontier models sitting within about five intelligence points of each other while cost per task differs 3.8x, with Google's Argon at the bottom at $1.99 per task — 74% below the priciest model, Fable 5.1, and 67%, 56% and 39% below Claude Opus 5.5, Muse Wonder and Astra respectively, all measured at maximum reasoning effort. The author attributes the gap to custom silicon, noting Google's TPU effort since 2013 and estimating it could serve tokens roughly 44% cheaper than peers, while Microsoft's Maia 300 (introduced October 2026, claimed 40% more tokens per watt than leading GPU-based systems) and OpenAI's first inference chip, Jalapeño (1.5x–1.9x more throughput per watt and 1.7x–3.6x lower end-to-end latency against GB200/GB300 on SemiAnalysis's InferenceX suite), follow the same playbook. The piece flags caveats: Maia 300's figure is company-reported with no independent benchmark, SemiAnalysis called the Blackwell comparison “somewhat incomplete and unfair,” and the 40–70% margin range is the author's own estimate.
Inference cost is becoming as decisive as raw capability, so custom silicon may determine who can sell frontier-level intelligence cheapest.