Hacker News October 14, 2026 8 min signal 2026-10-14

Gemini 4 Argon

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Gemini 4 Argon

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Gemini 4 Argon

Our next era of frontier intelligence, built to solve your hardest problems

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Solving frontier problems with experts

Researchers, engineers, and security specialists are pairing with Argon to push the boundaries of science, math, robotics and cyber defense.

Unlocking new scientific breakthroughs

Argon is helping researchers at Google and the scientific community accelerate their path to discovery and real-world impact.

Accelerating robotics

Argon autonomously turns raw video to a physics-accurate simulation, estimating depth and physical boundaries, and writing the exact simulation code required to replicate those real-world environments.

Scaling cybersecurity defense responsibly

Argon ties for #1 on CWE-Bench (68%) for vulnerability remediation, is our most resilient model against indirect prompt injections, and is expanding to hundreds of additional Fairwind Program partners today.

Hands-on
Performance
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Solving complex engineering tasks

Across Google and our early access partners, Argon is quickly becoming the preferred model for engineers tackling long-horizon software engineering.

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Untangling browser feature code in Chrome

Quantum chemistry open-source package optimization

Optimizing a production Python codebase in hours

Making our open Gemma models nearly three times as fast

Migrating legacy code

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.

xTB (Semiempirical Extended Tight-Binding) is one of the most-cited open-source quantum chemistry packages for molecules. Scientists use it to predict molecules' 3D shapes and to screen large numbers of drug and materials candidates. Using Argon in Antigravity, our researchers made xTB up to 2.3x faster at predicting molecular structures and 1.8x faster at conformer search. The changes have been contributed to the official xTB repository on GitHub.

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%.

Using Argon, we achieved a 2.85x end-to-end inference speedup on an A100 via vLLM (accelerating throughput from ~37,900 to over 108,000 tokens per second on Gemma 4 E2B) with 100% token parity and zero quality drop in just a few days, a process that would usually take months. Check out the upstream pull requests (optimizing everything from attention tuning to speculative decoding) to see the exact changes.

Argon in Antigravity migrated a backlog of 700+ Google Skills training labs off of hardcoded system images and runtimes spread across five code repositories, catching 16+ hidden timeout and race-condition bugs on the way. The work saved 955 engineering hours and reduced recurring cleanup sweeps from roughly 540 hours a year to a single 3-hour update.

Delivering complex 3D visualizations

Argon brings major improvements in spatial reasoning, mathematical precision, and visual coding compared to previous Gemini models.

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Transforming critical business and professional workflows

Argon combines frontier reasoning, deep visual perception, expanded domain knowledge, and stronger writing capabilities across legal, finance, science, and coding to handle high-stakes professional workflows.

Ambience Healthcare

Amplitude

Axiom Bio

Box

Cartwheel

Deloitte

Kepler

LexisNexis

MinutalA

Quadrillion

Quora

Roboflow

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Accelerating real-world impact with researchers and domain experts

Argon is built to be an active research partner, moving beyond literature search to help scientists, mathematicians and researchers formulate, stress-test, and prove non-obvious hypotheses.

Quantum computing

Nuclear fusion

Rare disease diagnosis

Enzyme design

Argon is helping our researchers accelerate progress across both quantum error correction and algorithm design. To help error decoders keep pace with quantum hardware in real-time, Argon translated an internal version of AlphaQubit, our ML quantum error correction decoder, into a single, highly optimized hardware kernel – nearly doubling its inference throughput so we can deploy larger, more capable models and scale to larger quantum devices, while continuing to meet strict real-time thresholds. Argon is also reducing spacetime costs for quantum subroutines. For arcsin, a building block in recent quantum simulation algorithms, an implementation written by Argon and formally verified in Lean cuts spacetime cost by more than 100x compared to a published quantum CORDIC baseline, a step toward a standard library of optimized quantum circuits.

Learn about AlphaQubit

Stellarators are incredibly complex machines that represent a leading candidate for limitless clean fusion energy, but optimizing their 3D magnetic fields is famously difficult. Since 1967, a long-standing hypothesis known as “Grad’s conjecture” assumed that finding exact mathematical solutions for these fields was impossible, until recent counterexamples were found, including by Professor Matt Landreman (University of Maryland). Now, a team at Google alongside Professor Landreman has discovered a broader class of counterexamples to Grad's conjecture. These new solutions give the fusion community reliable benchmarks to test their stellarator simulations codes, which are valuable tools for future designs.

Researchers at Gamow Labs used Argon to 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. On private Gamow Labs evals derived from real clinical and lab data, Argon topped on RareBench (57% on assistance with rare-disease diagnosis) and LabBench (52.2% on wet-lab reasoning). These advances point to faster diagnosis for families facing rare conditions.

Engineering enzymes with precise reactivity is notoriously slow, often demanding months of bespoke model tuning and candidate screening. Serine esterases are a class of enzymes that cut ester bonds on targeted molecules, widely used in recycling, and the manufacture of pharmaceuticals, biofuels and detergents. Our scientists worked with Argon to design serine esterases, targeting the benchmark molecule 4MU-acetate that glows under UV light once its ester bonds are broken. In a matter of hours, Argon assembled relevant data, performed model tuning and identified multiple biochemistry-inspired filters to get to a final set of five novel designs. Subsequent laboratory testing confirmed robust catalytic activity across these designs. The speed and ease of successful enzyme generation demonstrates the potential for rapid acceleration in enzyme engineering in fields such as biomanufacturing, green chemistry, and drug development.

Speeding up robotics development

By bringing together advanced visual-semantic reasoning, deep physical understanding, and autonomous code generation, the model introduces a new set of capabilities across the robotics stack.

Gemini 4 Argon with cybersecurity defense capabilities

Bringing state-of-the-art cyber capabilities to more trusted defenders

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Wiz is already using Argon for its “Scan for Good” initiative, which protects critical public infrastructure for free. Having had early access to the model, Wiz used Argon to protect critical healthcare infrastructure. The model went beyond just spotting a vulnerability - it proactively safeguarded hundreds of thousands of patient records.

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Unlocking new applications with a 1M-token output window

Uninterrupted frontier math and science derivations

With 1M output tokens, Argon has the headroom to finish 100K+ token reasoning trajectories and output exhaustive, step-by-step mathematical proofs, quantum error-correction decoders, and complete computational physics solvers in one go.

Full-repository generation and refactoring

Generate entire multi-file applications, scaffold complete microservices, or refactor sprawling codebases in a single pass without stitching together truncated responses.

Massive legacy translations

Translate monolithic legacy files and complex database schemas end-to-end while preserving cross-file dependencies.

Exhaustive structured synthesis

Produce comprehensive compliance audits, full-scale technical specifications, and massive structured datasets in a single turn.

Performance

Frontier performance in complex workflows

CategoryBenchmarkNotesGemini 4 ArgonGPT-6 AstraClaude Fable 5.1Claude Opus 5.5

Knowledge workVals Index68.9%63.1%65.8%67.0%

AutomationBenchScore51.3%41.4%31.4%42.5%

Vals Finance Agent v265.4%53.5%58.9%58.6%

Harvey's Legal Agent Benchmark19.6%5.4%6.7%3.8%

Agentic codingDeepSWE v1.177.9%74.1%67.4%74.2%

FrontierSWE v255.0%65.5%56.3%62.3%

Vibe Code Bench91.9%89.6%90.3%90.3%

Terminal-bench 4.057.4%58.2%57.9%66.4%

ML engineeringPostTrainBench45.3%44.3%40.2%49.3%

Science and mathTerminal-Bench Science 0.157.6%68.1%52.6%63.3%

LABBench 288.8%85.4%68.6%73.1%

RiemannBench76.0%72.0%65.6%69.6%

Long contextGraphWalksUp to 128k, BFS (F1)99.7%98.7%91.4%90.6%

256k to 1M, BFS (F1)84.2%71.8%65.0%66.8%

Computer useAgent's Last ExamPass rate39.5%34.2%—38.2%

OSWorld-2.0Offline subset

Partial score69.2%72.6%——

Multimodal understandingChartography71.6%71.0%46.2%66.3%

LVBench91.7%87.5%79.7%83.7%

CybersecurityCWE-bench v168.0%68.0%58.0%67.0%

Methodology: deepmind.google/models/evals-methodology/gemini-4-argon

Safety

Strengthening frontier safeguards before broad availability

Defending against misuse

Argon is designed to refuse harmful requests while preserving legitimate, dual-use scientific research, as per our Frontier Safety Framework.

Defending against prompt injection attacks

Argon is our most resilient model yet against indirect prompt injections.

Monitoring for misalignment

We are deploying misalignment mitigations that monitor Argon’s chain-of-thought and actions and stop execution when necessary.

Hardening systems

We are hardening our sandboxed environments by isolating and sealing them before high-risk training or evaluations begin.

Built with Argon

Gemini 4 Argon is available today for Google AI Pro and Ultra subscribers

Our flagship subscription tier, giving power users, developers, and creators highest access to advanced features and capabilities across Google to accelerate their workflows.

Upgrade today to unlock Gemini 4 Argon

Apply for research and security programs

Academic and independent scientists can apply to get the highest usage tier in Google Antigravity free of charge for 6 months (subject to eligibility, capacity, and selection) and cybersecurity defenders can apply to join the Fairwind Program.

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