Today · event date October 13

Google opens Gemini 4 Argon at US$2 per million input tokens

Google opens Gemini 4 Argon's paid API at an introductory US$2 per million input tokens and US$10 per million output tokens, with prices doubling after November 30

Concept illustration: on the left, a multi-storey white office building lettered GOOGLE near the top, with a multicoloured letter G emblem beside the word and low shrubs at its base. On the right, a person with dark wavy hair, a black turtleneck, black trousers and white sneakers sits on a chair at a table typing on a red laptop whose lid carries a black label reading GEMINI 4 ARGON, set against a pale pink circle.

Why this matters to you: Google opened the paid API on launch day, and two outside testers who had early access each found a weak spot. Shipper found Argon weak at reworking existing code but fast at new projects, and Mollick found it lags at complex multi-agent work.

Google DeepMind released Gemini 4 Argon on October 13 and opened the paid API the same day. The launch post lists an introductory price of US$2 per million input tokens and US$10 per million output tokens, with cached input a further 95% cheaper. After November 30 the price becomes US$4 input and US$20 output. Google's own post puts Argon first on the Vals Index, a model leaderboard we have not read (Google DeepMind, 2026-10-13).

Dan Shipper, CEO of the tech publication Every, says six people tested it for a week and found it fast at writing new projects from scratch. His "senior engineer benchmark" asks the model to rework a codebase an AI had cobbled together on the fly. Argon ran for 4 hours 47 minutes and "scored just 21%". The four rivals Shipper listed scored 81% to 85% (Dan Shipper, 2026-10-13). Shipper also says it cuts corners on checking its own work. Ethan Mollick, a Wharton professor, says it is the first model outside OpenAI and Anthropic to cross what Mollick calls the frontier threshold. It is at par in some areas, while "in others it lags, including at complex multi-agent work" (Ethan Mollick, 2026-10-13).

Our reading: both outside readings come from tasks one person or one organization built, and an overall leaderboard and a code-rewrite test measure different things. In Google's overnight demo in item 1, four of six proposals were sent back in human review. That may point the same way as Shipper's finding that Argon is weaker at reworking existing code, though the four may have been sent back for other reasons. Google's demo and Shipper's test are each a single run. Whether Argon can rework existing code well has to wait for a third party to rerun the same task set.

Not proven yet: the Vals Index first place is Google's own claim, and we haven't read the leaderboard How we checked →

Source and checks

This item ran in the brief of Oct 14, 2026, alongside 14 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). "Google opens Gemini 4 Argon at US$2 per million input tokens." https://secondsource.io/en/news/2026-10-14-2/. Accessed access date.