Gemini 4 Argon: Built to solve your hardest problems
Google's Gemini 4 Argon significantly accelerates open Gemma models, achieving a 2.85x end-to-end inference speedup on an A100 via vLLM. This boosts throughput from approximately 37,900 to over 108,000 tokens per second on Gemma 4 E2B, with no loss in token parity or quality. Google is also offering 10,000 scientists free access to the highest usage tier of Google Antigravity for six months to further research.
This report details a nearly three-fold inference speedup for Gemma models, unlike previous updates that focused on model size or new capabilities.
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PublishedOffset at this time: UTC+0Oct 13, 2026, 16:00 UTC
IngestedOffset at this time: UTC+0Oct 13, 2026, 17:00 UTC
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- Oct 13, 2026, 16:00
- Ingested
- Oct 13, 2026, 17:00
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Oct 13, 2026
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We’re giving developers, enterprises, scientists, and consumers access to our latest frontier model.
In this article
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Built to solve your hardest problems
Solving complex engineering tasks
Transforming critical business and professional workflows
Accelerating real-world impact with researchers and domain experts
Developing Argon responsibly and safely
Get started today
Gemini 4 Argon is now globally available today for developers and enterprises via the API in AI Studio and Gemini platform , for Google AI Ultra subscribers in Google Antigravity , and Google AI Ultra and Pro subscribers in the Gemini app and AI Mode in Search .
Argon excels at deep work in complex, real-world tasks across domains including coding, finance, legal, cybersecurity, and science. Its depth and precision across multi-step tasks leads Argon to outperform other frontier models on benchmarks across domains , ranking #1 on the Vals Index , which measures model performance across critical economic sectors like finance, coding, legal, and tax work.
The model launches today at an introductory price
1 of $2 per million input tokens and $10 per million output tokens (with cached input tokens at 95% off).
Teams at Google, across enterprises, and in the research community have already been using Argon to land significant impact across fields:
- To solve major engineering problems like large codebase refactoring and optimizations
- To optimize business workflows across domains like legal, finance, and science
- To find and patch critical cybersecurity vulnerabilities
- To enable novel breakthroughs in quantum computing, nuclear fusion, and life sciences
Solving complex engineering tasks
At Google, we’ve been using Argon on our hardest engineering problems. Over the past few weeks, software engineers have been shipping high-quality code faster than ever. Argon in Antigravity has helped teams increase agentic code submissions by more than 35% while reducing code rollbacks by nearly a third.
- Untangling browser feature code in Chrome: Argon helped the Chrome team separate core webpage handling from a ~6 million line legacy folder of browser features held within Chromium’s 40 million-line codebase. Piping multiple clean code changes at a time , Argon helped the team finish an architectural refactor across hundreds of files, completing months of work in weeks. For Chrome's billions of global users, this will unlock the ability for live webpages to move fluidly between tabs, side panels, and floating windows without reloading, eliminating an entire category of browser crashes.
- Optimizing a production Python codebase in hours: Argon in Antigravity is helping the team behind Google's open-source Agent Development Kit (ADK), a framework powering enterprise agents, push their performance engineering further. The team gave Argon their .agents/skills rulebook and a one-paragraph goal to eliminate framework overhead without breaking existing apps. Working autonomously overnight, Argon surfaced optimizations, now live in google/adk-python , that cut in-memory session prep time by 40% in long sessions and sped up cold imports by 21%. Read the engineering deep dive to learn more.