Gemini 4 Pro / Flash: benchmarks, hands-on tests and leaks

A breakdown of Sundar Pichai's interview says Google prioritizes frontier research over Cloud revenue, with Gemini 4 Argon processing 25 trillion tokens in 72 hours.

A user says Google moved student accounts into a trial tier without Opus 5.5 or Gemini 4 Argon access, and upgrading led to a forced refund and loss of student status.

On Apple's RSI-Inference benchmark, where AI modifies vLLM/SGLang engines, Gemini 4 Argon speeds inference 1.39x and passes 81% of Day-0 hidden tests, though Pass@1 is only 6.7%.

Gemini 4 Argon scores 77.9% vs Opus 5.5's 74.2% on DeepSWE real-code fixes, but trails 57.4% to 66.4% on Terminal-Bench, at half the output price.

A novice investor logs into Google AI Pro via Grok Bot and has it discuss a target with Gemini 4 Argon across rounds to produce a concrete buy plan.

In a Big Technology Podcast interview, Pichai discusses Gemini 4 Argon's ~25 trillion tokens in 72 hours, compute planned 2–3 years ahead, layered safety, and persistent agents spanning Gemini, Search and Gmail.

A user lists the past month's model releases such as Gemini 4 Argon, GPT-6.1 Sol and DeepSeek V4.1, arguing Claude Opus 5.5 still leads.

A post asking readers to pick today's best AI model among Claude Opus 5.5, Kimi K3.1 Pro, GPT-6 Astra and Gemini 4 Argon.

A user spotted a new routing model identifier, gemini-4-argon-d, in Google Antigravity and guesses it may be Gemini 4 Flash.

The poster asks Antigravity users whether Gemini Argon 4 actually solves harder coding tasks or merely does them faster.

A longtime Google skeptic subscribed to Pro and ran Gemini Argon on a Polymarket deep research task, reporting 30+ sources, a math model and rule-by-rule analysis as the best AI output so far.

A short reply claims the first problem can be solved by using Gemini 4 Argon, without specifying what the problem is.