Yes. Google now lists Argon as a stable Gemini API model. To try it without building an app, open the Argon chat in Google AI Studio and check your project's access and billing. The API model uses the paid tier. Google's current pricing page excludes Argon from the Free tier and Google AI Pro plan.

For subscribers, Google confirms Argon in Antigravity desktop and CLI for Google AI Ultra users. Businesses can use Gemini for business on web, desktop, and mobile. Check the product and account you intend to use before changing plans.

The public API model ID is:

gemini-4-argon

The initial September rollout gave trusted cybersecurity partners access through the Fairwind Program. That program remains a separate route for specialist cyber defense. You can now use the ordinary developer release for the comparisons in this guide.

This table uses exact model versions from Google's developer documentation. The task examples are our suggested starting points.

Google positions Argon for sustained reasoning across complex work. Gemini 3.8 Flash already supports substantial coding and business tasks, so a difficult job can still be a reasonable Flash test.

Our rule: start with Flash when you can define and check the result. Save the failures worth testing with Argon, such as an overlooked condition in a project brief or a bug that survives a proposed fix.

Google still lists 3.1 Pro Preview for complex reasoning and coding. Its preview status matters when deciding what to use in an established app.

Keep examples from your current Pro setup. Compare whether another model catches more errors, follows the required format, or reduces editing. The model name alone gives you too little evidence to justify changing a working process.

Google designs 3.5 Flash-Lite for fast, high-volume work, including extraction and document processing.

Try it on a narrow task with clear rules. For example, extract company names and dates from approved notes, or sort support requests into five fixed categories. Send ambiguous cases for review. A smaller task makes it easier to judge whether the result meets your needs.

Argon's released specification clarifies the headline 1-million-token capability. Input, thinking, and output share a window of 1,048,576 tokens. A token is a small unit of information the model processes.

These limits apply to the Gemini Developer API:

For comparison, 3.8 Flash, 3.5 Flash-Lite, and 3.1 Pro Preview each list 1,048,576 input tokens and 65,536 output tokens in their API specifications.

The Developer API's long-decode guide explains background execution for responses beyond 65,536 tokens. Set an output limit above that threshold and allow time for continuation. A large input leaves less room for thinking and output. Google Cloud has its own long-decode setup and continuation guidance, so follow the instructions for your API surface.

Argon accepts text, images, video, audio, and PDFs and returns text. It supports function calling, structured outputs, code execution, and search grounding. For image creation or live voice, use a model designed for those outputs.

For a weekly report, you may still want 500 words. More capacity is useful only when it helps the model complete the task correctly. Keep your requested answer length tied to what someone needs to read.

The Vals Index v2.1 combines tests covering finance, coding, legal, and tax work. Its live table showed these results when we checked it on September 30:

Argon leads these Google models on this test. The result supports testing it on complex professional work. It does not establish an accuracy rate for your own documents or an equal-cost comparison: the runs use different token totals and durations.

Use the benchmark to choose candidates. Then check them on work where you know what a correct answer requires.

The chart below compares Argon with other providers' frontier models. It is a different comparison from the Flash and Pro choices above. The supplied chart matches Google DeepMind's published results. Argon leads some tests and trails others, so it is not a universal ranking of model quality.

Chart credit: Google DeepMind. Evaluation details are linked in Google's methodology page. We have not independently reproduced these results.

Two reported results are especially relevant to Argon's positioning:

Chart credit: Google. Higher is better on this specific software-engineering evaluation.

Chart credit: Google DeepMind. The chart reports Pass@1, with ties broken by Pass@4, and identifies each model's agent harness. The scores describe those specific evaluation setups. A secure workflow also needs permissions, testing, and review.

The table shows US dollars per million API tokens at Standard rates. The final column illustrates 20,000 uncached input tokens plus 5,000 billed output tokens. It holds usage equal across models and uses Google's current API pricing.

Argon's introductory Standard rates end on November 30, 2026. Cached input costs $0.10 per million tokens during that period and $0.20 from December 1. Output charges include thinking tokens. Argon supports implicit caching only; explicit cache creation is unsupported.

Flash has a separate schedule: 3.8 Flash moves to $1.50 input and $7.50 output per million tokens on January 1, 2027. Keep both dates in a longer-term budget.

For asynchronous jobs, Argon's Batch and Flex rates are $1 input and $5 output per million tokens through November 30, rising to $2 input and $10 output on December 1. Priority processing has higher rates. Choose the processing tier that fits the job before comparing costs.

The Vals leaderboard's run-cost columns use Argon's later $4/$20 rates and Flash's later $1.50/$7.50 rates. They are not priced on the same basis as the current-rate illustration above.

For 3.1 Pro Preview, prompts above 200K input tokens cost $4 input and $18 output per million. The existing Gemini 3 output rates include thinking tokens. Caching, storage, tools, and processing options can change the bill. These figures describe API use; Gemini app plans have separate allowances.

Our advice: budget for the rate that will apply when you use the model. Argon's introductory price makes a trial less costly than its announced later rate. The result still needs to earn the extra cost over a smaller model.

The example amounts are calculated token charges. Actual task costs depend on usage and processing options.

Verification: We refreshed release availability, model specifications, setup guidance, and API pricing on October 13, 2026. The benchmark tables and supplied charts retain their September 30 snapshot. We checked the price calculations. We have not run a live Argon comparison or independently reproduced the benchmark results.