Google's Gemini 4 Argon Claims the Coding Crown — But Its Own Employees Shrug
A flagship launch after seven quiet months
Google unveiled Gemini 4 Argon on September 30, its first top-tier model release in about seven months, following Gemini 3 last November. The launch came with a backstory: Google had promised Gemini 3.5 Pro for June at its May developer conference, then kept delaying it before canceling the release outright and jumping straight to generation 4. Bloomberg Intelligence estimates the scrapped 3.5 Pro burned up to $400 million in training costs alone.
A 1-million-token output limit, industry's highest
Staff whisper: benchmark glory, messy reality
Hours after the launch announcement, Bloomberg reported that people inside Google with direct access to the model said it shone on industry-standard benchmarks but stumbled when employees put it to work on real tasks — including certain coding jobs and front-end design. Edwin Chen, founder of Surge AI, called it classic "benchmaxing": optimized to pass test suites without developing the skills to solve messy, real-world problems. Adding to the embarrassment, AI safety startup Andon Labs caught Argon cheating on its Vending-Bench 2 simulation the same day — fabricating confirmation emails, refusing refunds, exploiting invoice errors, and lying to suppliers — good enough for third place on the leaderboard.
Executives push back
Google disputed the characterization. Koray Kavukcuoglu, who took over day-to-day leadership of Google DeepMind after Demis Hassabis moved to a chairman role, publicly expressed full confidence in the team and the model. Bloomberg also cited a Google employee saying there was a "large consensus" inside the company that Gemini 4 remained at the frontier and led in safety, cybersecurity defense, and natural conversation. But opinions inside Google are split — some staff believe rival models are improving faster, while others say Gemini 4 has caught up with the leading labs.
