Gemini 4 Argon

Gemini 4 Argon is Google DeepMind’s AI model for difficult software engineering, research and professional tasks that take many steps to complete. Google announced it on 30 September 2026, alongside a staged rollout to trusted cyber defenders through its Fairwind Program. The launch introduces a one-million-token output limit and announced introductory API prices of $2 per million input tokens and $10 per million output tokens. Google’s launch announcement gives the access plan and pricing terms.

Argon is already used inside Google for debugging, code migrations and research. The company plans to expand access to developers, enterprises and consumers, starting with paid API customers and Google AI Ultra subscribers. Its first public results cover coding, finance, legal work, science, visual understanding and vulnerability remediation.

Who can use Argon?

Google’s initial Argon rollout on 30 September 2026 serves trusted cyber defenders and testers. The Fairwind Program gives approved defenders access to the model’s full cybersecurity capabilities, and Google says this cohort and its internal teams will receive Argon with cyber guardrails removed. Access to that configuration is tied to those groups.

Google says it is participating in the US government’s voluntary process for pre-release model access while gathering tester feedback and strengthening safeguards. The planned broader release begins with paid API customers and Google AI Ultra subscribers. Those are the first groups Google names for expansion, rather than a promise of immediate access for every subscriber. Google

The output limit grows to one million tokens

Gemini 4 Argon’s announced output limit is 1M tokens, up from the previous 64K. A token is a piece of text handled by the model, such as part of a word. The larger limit gives an extended reasoning and generation run more room to continue through a complex problem.

Output and context describe different parts of the job. Output is the material the model generates during a run. Context is the material the model can work with, including the prompt and supplied information. Artificial Analysis separately lists a 1M-token context window for Gemini 4 Argon (High). These limits describe capacity; the length and usefulness of a particular answer still depend on the task.

Google announces $2 input and $10 output pricing

Google’s announced introductory Argon API prices on 30 September 2026 are $2 per million input tokens and $10 per million output tokens. Cached input receives a 95% discount on the input price, which makes the introductory cached rate $0.10 per million tokens. Caching lets eligible repeated input be reused at the discounted rate.

The launch announcement’s pricing footnote says the input and output rates become $4 and $20 per million tokens after the introductory period. These are announced API terms for the rollout. Google AI Ultra is a separate subscription route named in Google’s access plan. Google’s pricing terms

Coding results depend on the task

Google reports 77.9% on DeepSWE v1.1 for Argon at launch, ahead of GPT-6 Astra and Claude Opus 5.5 in its published comparison. DeepSWE tests long-horizon software engineering. Other coding tests reward different work, and the launch table gives a fuller view of the models’ strengths.

Figures are from Google DeepMind’s launch table. Higher scores indicate better performance within each listed test. Vals Index measures professional work; AutomationBench tests end-to-end business tasks; LVBench tests long-video understanding; CWE-bench v1 tests vulnerability remediation.

Google’s evaluation methodology says Argon generally uses the highest thinking setting and single-attempt scoring, with exceptions explained for individual tests. Google computes its own DeepSWE result using a mini-swe agent harness. Several comparison scores come from providers’ system cards or the benchmark operators’ leaderboards. The table therefore combines results from those stated evaluation sources.

The independent Artificial Analysis listing, checked on 30 September 2026, scores Gemini 4 Argon (High) at 53 on its Intelligence Index. That index is a separate assessment with its own test mix and scale. It gives readers an additional view alongside Google’s task-specific launch results.

Google is using Argon to rewrite and optimise code

Google’s 30 September 2026 announcement describes Argon agents working on C and C++ migrations to Rust, from core libraries to more than 800,000 lines in the Fuchsia Zircon kernel. Google says these critical-system rewrites undergo automated and manual audits, emulation testing and review before production deployment.

For libgav1, Google’s video decoder, agents replaced 32,000 lines of SIMD code in an existing Rust port through repeated profiling and compiler analysis. Google reports that the resulting memory-safe decoder runs 2.7 times faster than that Rust port, with identical video output. The comparison uses the earlier Rust port as its baseline.

Google also reports more than 300 TiB of freed memory from optimisations applied across its data centres. Separately, its quantum researchers used Argon to improve a subroutine’s spacetime-resource requirement by 40% against a published baseline in one example. These are Google’s internal engineering and research results. Google’s announcement

Cyber defenders get the first rollout

Google says Argon can find, validate and patch software vulnerabilities. Its launch table gives the model 68.0% on CWE-bench v1, equal to GPT-6 Astra in that table. Google’s methodology identifies CWE-bench as a public-leaderboard result, scored by single-attempt success, with ties ranked using four-attempt success.

Wiz is already using Argon through Scan for Good, its initiative to find and remediate high-risk exposures affecting public infrastructure. Google reports that Argon identified a critical vulnerability exposing personal information in healthcare software used by hospitals worldwide. The announcement also describes internal vulnerability tests spanning 20 programming languages and Wiz’s tests against web applications with source code withheld from the model. Google

Google is strengthening safeguards before wider access

Google’s Argon rollout pairs tester feedback with work on misuse prevention, prompt-injection resistance, monitoring for misalignment and secure execution environments. Google describes safeguards that monitor internal activations for misuse and systems that monitor the model’s reasoning and actions, stopping execution when needed. Its announcement also describes isolating environments used for high-risk training and evaluations.

The next named access groups are paid API customers and Google AI Ultra subscribers. For developers choosing a model, Argon’s announced output capacity, introductory token prices and task-specific results give concrete points to compare as that rollout expands. Google’s rollout plan