Gemini 4 Argon costs $2 per million input tokens and $10 per million output tokens through November 30, 2026, then doubles to $4 and $20. The introductory pricing is the main reason to build now, because the model's benchmark position is good rather than dominant, and coding still favors Anthropic newest models.
What Gemini 4 Argon Is and Who Can Use It
Gemini 4 Argon is a Google model built for long, complex work in software, law and finance, and it became available to Google AI Pro and Ultra subscribers on October 13, 2026. Google announced the model on September 30, 2026 to a limited group before opening it to paid plans and the Gemini API.
The two dates matter because launch news and general access are separate events. The September 30 announcement covered a restricted audience; the October 13 change put Argon in the Gemini app for subscribers and in the API for developers. Google's positioning is professional work rather than chat: long documents, multi-step analysis and tasks that span several tools.
The app changed alongside the model. A switch now separates chat from a Tasks mode, the new name for what Google previously called Spark, and Tasks keeps working across Google apps while you are away from your computer. On a Mac, the Antigravity coding tool sits inside the Gemini app. Treat any brand-new mode as unproven until you have run it on something you can afford to redo.
Gemini 4 Argon Pricing: Subscription Tiers and API Rates
Gemini 4 Argon starts at the Google AI Pro tier, which runs about $20 a month, while the Antigravity coding tool is reported to need Google AI Ultra at roughly $100 a month. Builders pay separately through the API, at $2 per million input tokens and $10 per million output tokens until November 30, 2026.
After that date the API rate doubles to $4 in and $20 out, and there is no free tier for this model. Thinking tokens count as output, so reasoning costs money even when the final answer is short. Google publishes the current numbers on its Gemini developer pricing page, and plan prices change often enough that you should check the subscription page on the day you buy.
A worked example makes the price change concrete. A job that sends 20,000 tokens in and returns 5,000 tokens costs about $0.09 at the introductory rate and about $0.18 after December 1, 2026. That is small for one job and large across thousands. The useful metric is cost per finished job, not cost per month, because a model that bills twice as much can still be cheaper if it saves an hour of checking.
Benchmark Scores: Where Argon Leads and Where It Trails
Gemini 4 Argon ranks first on the Vals Index, which weights finance, coding, law and tax work, at about 69%, and it trails Anthropic newest models on the Artificial Analysis Intelligence Index. Both results point the same way: strong on office-style tasks, not dominant everywhere.
On the Artificial Analysis Intelligence Index, Argon scores 53, level with GPT Astra and Fable at a lower cost per task. Google's own table adds an Automation Bench score near 51%, ahead of the other models Google listed, alongside a Terminal Bench coding result where Argon trails Claude. Artificial Analysis publishes the independent index, and Google's model card carries the vendor-computed rows.
Some rows in Google's comparison table were computed by Google, which does not make them wrong but does mean they deserve a check against independent boards. Vendor benchmarks also reflect the tasks and configurations the vendor chose. Argon looks strong on broad professional reasoning and weaker on terminal-style coding.
Coding Is Still the Weak Spot
Coding is the clearest gap in the Gemini 4 Argon review: it trails Claude on Terminal Bench, a coding test, and it posted one of the lowest fabricated-answer rates among top models at 15%. A low hallucination rate is not a zero rate, so anything that matters still needs verification.
The direction of the gap is consistent across sources. Terminal-style coding favors Anthropic newest models, and reviews cited in the video describe Argon as weaker on code than on analysis. If coding is your daily work, run your own test before switching, because a general benchmark cannot tell you how the model handles your language, framework and codebase conventions.
Hallucination numbers deserve careful reading. A 15% rate among top-scoring models says Argon invents fewer unsupported claims than some peers on that particular test; it does not say the model is safe to trust without review. The test shape, graders and prompt set all affect the result, and the score applies to that benchmark rather than to your workflow.
How to Test Any Model on Three Job Types
A benchmark cannot predict your results, so the practical test is three jobs of different shapes, scored the same way on the model you already pay for. The whole exercise takes roughly an hour, and it produces better evidence than any leaderboard.
Make something
Ask for a simple drawing as code, such as an SVG picture of a cartoon chef, then open the file in a browser. Check the shapes, the spacing and whether the file renders at all. This job tests visual reasoning and instruction following without exposing you to much risk.
Build something
Ask for a small game in one file, such as a basic arcade shooter or a ball physics playground, then play it for two minutes and write down every bug. Ask for one fix and see how the second answer compares with the first. Evaluate the revision, not just the first draft.
Boring work
Paste in a real week of tasks and meeting times and ask for a schedule with breaks, plus a note on what the model left out. For research, ask it to compare three products and flag any claim it cannot source. This is the job most people actually pay for, and it exposes both scheduling judgment and citation discipline.
Score each job from one to five on accuracy, speed and cost, and record the date and model version next to every score. Models change every few weeks, so a result from October 2026 may not hold by December.
Limits That Show Up on Real Jobs
Gemini 4 Argon works inside a context window of about 1 million tokens, shared between what you send and what the model writes back, so long inputs eat into the room available for answers. By default a single answer tops out near 65,000 tokens, and longer outputs need a special long decode mode.
Argon is also wordy. Artificial Analysis counted more output tokens from it than from the typical model, which raises the bill because output tokens cost five times the input rate at the introductory price. Watch usage in the first week and split large jobs into steps you can check.
Tasks mode is brand new, so start with something low-risk such as sorting a calendar before handing it anything that matters. Count the full job when you compare costs: your review time, retries and the subscription all belong in the total. Antigravity only makes sense if the hours it saves exceed the Ultra price.
Comparison: Argon Against ChatGPT and Claude
Gemini 4 Argon competes on cost and on office-style reasoning rather than on coding, and the evidence supports keeping your existing model where it already works. The table compares only dimensions the available sources actually measure.
| Dimension | Gemini 4 Argon | Anthropic newest Claude models | GPT Astra |
|---|---|---|---|
| Vals Index (office work) | ~69%, first place | Lower than Argon | Lower than Argon |
| Artificial Analysis Index | 53 | Ahead of Argon | 53, tied |
| Terminal Bench (coding) | Trails Claude | Leads Argon | Not reported here |
| API cost per million tokens | $2 in / $10 out until Nov 30, 2026 | Not covered in source | Not covered in source |
| Best use case | Long documents, finance, tax, law, scheduling | Coding-heavy work | Comparable general tasks at similar cost |
Read the table as scoped to the named tests, not as a general ranking. Vals and Artificial Analysis weight different things, and the Terminal Bench row reflects a coding-shaped task where command-line work dominates. GPT Astra ties Argon on the intelligence index; the source does not establish a winner between them.
Anthropic the company behind Claude its newest models lead on the coding test cited here. OpenAI publishes GPT models including ChatGPT, though the source refers only to GPT Astra on the intelligence index. Neither comparison covers end-to-end cost per finished job, which depends on your review time far more than on per-token rates.
FAQ
- Is Gemini 4 Argon free to use?
No. Argon requires a paid Google AI Pro or Ultra subscription in the Gemini app, and the API has no free tier for this model. Google listed introductory API pricing of $2 per million input tokens and $10 per million output tokens through November 30, 2026, doubling after that date.
- Does Gemini 4 Argon beat Claude coding?
The available evidence says no. Argon trails Claude on Terminal Bench, a coding test, and reviews cited in the source describe it as weaker on code than on analysis. Run your own coding task on both models before switching your daily workflow.
- How much does the API cost after the introductory period?
From December 1, 2026, the rate rises to $4 per million input tokens and $20 per million output tokens. Thinking tokens count as output, so reasoning-heavy prompts cost more than the visible answer length suggests. Check Google's pricing page before you commit to a budget.
- What is the context window and default output limit?
Argon works within a window of about 1 million tokens shared between input and output, with a default single-answer limit near 65,000 tokens. Longer outputs require a special long decode mode, so large jobs work better as checked steps than as one request.
- Should I cancel ChatGPT or Claude for Argon?
Not on benchmark evidence alone. Argon leads an office-work index and ties on a general intelligence index while trailing on coding, so it fits as an additional tool for long documents, finance and scheduling. Keep the model that already handles your main workload until your own three-job test says otherwise.
Fork this article
Start a new branch from the same video, shaped your way. You keep the credit; the original keeps the attribution.
A fork in another language is filed as a translation of this article, so the two pages point at each other. You can unlink it later from the editor.
0/240
You are creating
- Format
- For
- Language
- Source
- Your angle
You will be asked to sign in before it is generated.
Buy credits