Today · event date October 13

Tunguz finds a 3.8x cost-per-task gap; it's his own math

Venture investor Tomasz Tunguz works out that five frontier models within five intelligence points of each other differ 3.8x in cost per task, with Gemini 4 Argon the cheapest

Concept illustration: five open laptops stand in a row on a long white table that recedes to the right, each screen showing a white speech bubble with grey lines; the nearest one, at front left, is red with ARGON lettered on its front edge, and the other four are silver. A white tent card with a multicoloured G symbol and the lettering GOOGLE stands to the left of the red laptop, and a pale pink circle sits in the background behind the front laptops.

Why it matters: Frontier models within five points of each other on intelligence can differ 3.8x in cost per task, though the gap mixes list price with how many tokens each model burns.

Tomasz Tunguz, a partner at Theory Ventures, wrote on October 13: "Five frontier models now sit within five points of each other in terms of intelligence, but their cost per unit differs by 3.8x." (Tomasz Tunguz, 2026-10-13) The data come from the evaluator Artificial Analysis, and the cost is a weighted average across ten evaluations. Google's Argon is the lowest at US$1.99 per task. The most expensive model with the same score of 53 is US$7.63. Our October 14 issue covered Argon's pricing. What is new today is cost per task across models.

Tunguz attributes the gap to Google's in-house TPU. He estimates that Google's cost to supply tokens may be 44% below its peers'. That is Google's own cost of producing tokens, not its list price. In a footnote he writes that the figure "rests on the midpoint of a range, not a measured serving result".

A second reading points to usage. Signal65, a measurement firm, runs its own leaderboard of agent tasks. It says Argon uses about 10,549 output tokens per correctly answered question, and Claude Sonnet 5.5 at its highest reasoning setting uses about 63,020 (Signal65, 2026-10-14). Signal65 says Argon's list price per token is higher than its rivals', but it uses far fewer tokens per question, so each question ends up cheaper. The two datasets differ in test questions, reasoning settings and cost method, so Signal65's usage figures can't be used to break down Tunguz's 3.8x directly.

Our reading: cost per task is the unit price of each kind of token multiplied by how much of it is used, then summed. By that arithmetic, comparing models means looking at what each question costs, not the list price alone. The two datasets can't yet separate how much of the gap comes from chips and how much from token usage. The chip explanation is Tunguz's view.

Not proven yet: the cost per task is Tunguz's own calculation; the evaluator's page doesn't list it How we checked →

Source and checks

This item ran in the brief of Oct 15, 2026, alongside 18 other items. How we checked it, the quote checks and what was still missing that day are on that issue's How we checked page.

Cite this item

SecondSource (2026). "Tunguz finds a 3.8x cost-per-task gap; it's his own math." https://secondsource.io/en/news/2026-10-15-2/. Accessed access date.