Gemini 4 Argon: A Highly Capable Model for Complex Tasks

Globally available today, Gemini 4 Argon is a highly capable model designed to tackle complex tasks across various domains. It can be accessed via the API in AI Studio and the Gemini platform, as well as through Google Antigravity for Google AI Ultra subscribers. Additionally, it’s available in the Gemini app and AI Mode in Search for Google AI Ultra and Pro subscribers.

Gemini 4 Argon excels at deep work in complex tasks across domains such as coding, finance, legal, cybersecurity, and science. Its depth and precision across multi-step tasks enable it 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’s introductory price is $2 per million input tokens and $10 per million output tokens (with cached input tokens at 95% off). This pricing structure makes it an attractive option for developers and enterprises looking to leverage its capabilities. Teams at Google have already been using Argon to achieve significant impact across various fields.

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.

One notable example of Argon’s capabilities is its work on untangling browser feature code in Chrome. The model 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. This achievement will unlock the ability for live webpages to move fluidly between tabs, side panels, and floating windows without reloading.

Argon also demonstrated its capabilities in optimizing production Python codebases. In one instance, it helped a team behind Google’s open-source Agent Development Kit (ADK) push their performance engineering further by cutting in-memory session prep time by 40% in long sessions and speeding up cold imports by 21%. This achievement is particularly notable given the complexity of the task.

Quantum chemistry researchers have also been leveraging Argon to optimize quantum chemistry packages. Using the model, they were able to make xTB (Semiempirical Extended Tight-Binding) up to 2.3x faster at predicting molecular structures and 1.8x faster at conformer search. These improvements will enable scientists to accelerate their research and discovery.

Argon’s capabilities extend beyond engineering tasks. It has also been used in the field of quantum computing, where it helped researchers accelerate progress across both quantum error correction and algorithm design. The model translated an internal version of AlphaQubit into a single, highly optimized hardware kernel – nearly doubling its inference throughput.

In nuclear fusion research, Argon was used to optimize 3D magnetic fields for stellarators. A team at Google alongside Professor Matt Landreman discovered a broader class of counterexamples to Grad’s conjecture, providing reliable benchmarks for future designs. This achievement has significant implications for the development of limitless clean fusion energy.

Argon is also being used in rare disease research, where it helped researchers analyze de-identified genomes from newborns with rare developmental lung disorders. In one case, Argon pinpointed a chromosomal rearrangement that routine software missed for years, and explained why it went undetected.

The model’s capabilities extend to enzyme design as well. Using Argon, scientists were able to design serine esterases in just hours – a process that would normally take months of bespoke model tuning and candidate screening. This achievement demonstrates the potential for rapid acceleration in enzyme engineering across fields such as biomanufacturing, green chemistry, and drug development.

Argon is also being used in robotics research, where it’s helping to accelerate progress toward physical AGI. The model outperforms all other frontier models on the Embodied Reasoning (ERQA) benchmark, which tests a model’s ability to understand and solve physical, spatial problems in 3D environments.

To demonstrate Argon’s capabilities in action, researchers tested its ability to learn an out-of-distribution physical skill simply by watching. In an internal test, Argon watched a raw video of a human playing cornhole, mapped the spatial coordinates of the environment, and calculated the required whole-body joint movements. It then autonomously built the simulation code.

Argon’s capabilities are not limited to these areas alone. The model is also being used in cybersecurity research, where it helped cyber defenders test hypotheses, audit critical codebases, and catch subtle zero-day vulnerabilities and logic flaws. This achievement has significant implications for the development of more effective cybersecurity defenses.

The safety and security stack for Argon represents a comprehensive approach to ensuring the responsible use of this highly capable model. The team collaborated with third-party partners on measurement of certain model capabilities and safeguards, deploying a multi-layer defense stack combining jailbreak resistance, monitoring, and enforcement.

Argon’s enhanced capabilities across several domains have greatly improved its robustness – for instance, external bio red teaming could only unlock less than 1% of bio sub-tasks we test for. Additionally, the model is significantly better at not fulfilling harmful requests compared to previous models.

The development of Argon was guided by a Frontier Safety Framework that prioritizes safety and security. This framework ensures that the model’s capabilities are aligned with human values and goals, while also providing robust safeguards against malicious actors.

Argon works longer and harder to solve highly complex scientific, coding, and agentic problems. Its industry-leading 1M unified input-output token limit via the Gemini API enables it to tackle tasks that would be impossible for other models. The model is rolling out today globally – available through various channels including AI Studio, Android Studio, Google Antigravity, and the Gemini app.

Developers can access Argon’s capabilities by building with the Gemini API paid tier in AI Studio and Android Studio. Enterprises can try it in Gemini for business, the API, and in Google Antigravity. Everyday users can start using it today in the Gemini app and AI Mode in Search if they’re a Google AI Ultra or Pro subscriber.

Researchers and scientists interested in leveraging Argon’s capabilities can apply to get access to Google Antigravity’s highest usage tier free of charge for 6 months (onboarded on a rolling basis), subject to eligibility, capacity, and selection. Applications are now open.