Gemini 4 Argon: Pricing, Benchmarks, Access
Gemini 4 Argon costs $2 and $10 per million tokens until November 30, then $4 and $20. It ties GPT-6 Astra on the AA index and is cheaper per task until then.
Gemini 4 Argon costs $2 per million input tokens and $10 per million output until November 30, 2026, then $4 and $20 from December 1. Any paid Gemini API account can call it since October 13. On the independent Artificial Analysis index it ties GPT-6 Astra at 53, five points behind Claude Opus 5.5. Through November it finishes an index task for 61% of what Astra costs. From December it costs 22% more than Astra.
Google announced Gemini 4 Argon on September 30, 2026 for cyber defenders only, and opened it to every paid developer on October 13. It is the first model of the Gemini 4 generation, the pre-training run Google first confirmed in July.
| Gemini 4 Argon | Introductory rate | Standard rate |
|---|---|---|
| Input, per Mtok | $2.00 | $4.00 |
| Output, per Mtok | $10.00 | $20.00 |
| Cached input, per Mtok | $0.10 (95% off) | $0.20 (95% off) |
| Batch and Flex, per Mtok | $1.00 in, $5.00 out | $2.00 in, $10.00 out |
| When it applies | Through November 30, 2026 | From December 1, 2026 |
| Who can buy it | Paid Gemini API tier, since Oct 13 | Same |
| Free API tier | None | None |
| Context limit | 1M tokens, input and output shared | Same |
How much does Gemini 4 Argon cost?
Two prices, and Google publishes both on day one. Google’s announcement lists an introductory $2 per million input tokens and $10 per million output, with cached input tokens “priced at 95% off input token price”, which is $0.10. It then states that “$4 per 1M input tokens and $20 per 1M output tokens will apply” after the introductory period. The announcement did not say when that period ends. The Gemini API pricing page, which now lists Argon, does: $2 and $10 apply through November 30, 2026, and $4 and $20 from December 1.
That standard rate is exactly the Claude Opus 5.5 rate card, $4 and $20, which Anthropic launched on September 22. The introductory rate is exactly GPT-6.1 Sol’s $2 and $10, OpenAI’s reasoning tier from DevDay on September 29. So Argon opens priced like a mid-tier model and is scheduled to become priced like a flagship.
Google has run this two-card pattern before. Gemini 3.6, 3.7 and 3.8 Flash all carry an introductory $0.75 and $3.75 that Google’s own pricing page says doubles on January 1, 2027. Argon’s card now carries a date too, and a shorter one: the Flash discount runs to the end of the year, while Argon’s ends a month earlier. Any budget that runs past November should be built on $4 and $20, not $2 and $10.
When can you use Gemini 4 Argon?
Now, if you pay for the Gemini API. Google rolled Argon out globally on October 13: developers get it on the Gemini API paid tier in AI Studio and Android Studio, under the model ID gemini-4-argon, and enterprises through Gemini for business, the API and Antigravity. In the consumer apps, Google AI Pro and Ultra subscribers get it in the Gemini app and in AI Mode in Search. On the API side, the pricing page says Argon is “not available on the Free tier or on the Google AI Pro plan”, so building on it needs a paid billing account.
It got there in two steps. On September 30 Google released it only “to a set of trusted cyber defenders through our Fairwind Program”, the same gate its Gemini 3.8 Flash Cyber variant ships behind, in a version “without cyber guardrails”, while taking part in “the U.S. government’s voluntary process for pre-release model access”. The October 13 post says Fairwind now expands to hundreds of additional partners.
Two rate details matter for cost. Batch and Flex requests cost half the standard price at both rates, and Priority costs 1.8 times as much ($3.60 and $18 now, $7.20 and $36 from December). Caching is implicit only: Google applies the $0.10 cached rate automatically when a prompt prefix repeats, and there is no explicit cache to create. This site’s AI model tracker now lists Argon as released, and the cost calculator prices it at the $2 and $10 in force today.
What does a task cost on Gemini 4 Argon?
The rate card is the wrong place to start, because Argon is a verbose model. Artificial Analysis, an independent benchmarking service, ran its full Intelligence Index on Argon at high effort and recorded 110 million output tokens. GPT-6 Astra at max effort needed 60 million for the same index, and Claude Opus 5.5 at max needed 260 million. Argon generates about 1.8 times Astra’s output to reach the same score.
At the introductory rate that still comes out cheap. Artificial Analysis puts Argon at $1.99 per index task, against $3.26 for Astra and $5.98 for Opus 5.5. Argon matches Astra’s score for 61% of the cost. The saving comes from the rate, not from efficiency: Astra charges five times as much per output token, and Argon uses fewer than twice as many.
On December 1 the rate doubles and the order changes. Every line of Argon’s card doubles at the standard rate (input, output and cached input), so at the same token mix the cost per task doubles exactly, to $3.98. That is 22% more than Astra for the same index score. It is still 33% less than Opus 5.5, which scores five points higher.
| Item | Introductory rate | Standard rate |
|---|---|---|
| Gemini 4 Argon (high) | $1.99 | $3.98 |
| GPT-6 Astra (max) | $3.26 | $3.26 |
| Claude Opus 5.5 (max) | $5.98 | $5.98 |
| Claude Sonnet 5.5 (max) | $5.46 | $5.46 |
The Artificial Analysis cost is one fixed workload, and your workload is not that one. Its main use is the ratio. If your prompts lean on cached context, the 95% cache discount moves Argon further ahead, because Astra’s cache read is $1.00 against Argon’s $0.10. If your tasks are output-heavy agent loops, Argon’s verbosity counts against it at either rate.
Is Gemini 4 Argon better than GPT-6 Astra?
On the independent index they are level: 53 each, Argon at high effort and Astra at max. Above them sit Claude Opus 5.5 at 58 and Claude Sonnet 5.5 at 56. Below them is GPT-6.1 Sol at 52, which charges the same $2 and $10 as Argon’s introductory rate, with no expiry.
Google’s own launch table makes the case for Argon by benchmark. As transcribed by VentureBeat (Carl Franzen, September 30, 2026), Argon leads or ties on 13 of the 18 rows Google published. All of these figures are vendor-reported.
| Benchmark (vendor-reported) | Gemini 4 Argon | GPT-6 Astra | Claude Opus 5.5 |
|---|---|---|---|
| DeepSWE v1.1 | 77.9% | 74.1% | 74.2% |
| AutomationBench | 51.3% | 41.4% | 42.5% |
| Vals Finance Agent v2 | 65.4% | 53.5% | 58.6% |
| GraphWalks (long context) | 84.2% | 71.8% | 66.8% |
| LVBench (long video) | 91.7% | 87.5% | 83.7% |
| Harvey Legal Agent | 19.6% | 5.4% | 3.8% |
| CWE-bench v1 | 68% | 68% | 67% |
| FrontierSWE v2 | 55.0% | 65.5% | not reported |
| Terminal-Bench Science 0.1 | 57.6% | 68.1% | not reported |
Two of those rows can be checked against independent boards, and they disagree. Zapier’s own AutomationBench leaderboard confirms Argon at the top: 51.29% at high effort, ahead of Claude Sonnet 5.5 at 44.75%, Opus 5.5 at 42.47% and Astra at 41.4%. The independent DeepSWE leaderboard, last updated September 22, does not list Argon at all. Its top is a three-way tie at 74% between Astra, Gemini 3.8 Flash and Claude Opus 5. The 77.9% is Google’s number, run in a harness Google does not name.
Where Astra wins, it wins by a lot: 65.5% to 55.0% on FrontierSWE v2 and 68.1% to 57.6% on Terminal-Bench Science. Those are long, open-ended engineering and research tasks, which is the work a frontier model is bought for. Google’s strongest rows are knowledge work: legal, finance and business automation.
There is one more independent signal. The Decoder (Matthias Bastian, October 1, 2026) reports Artificial Analysis’s AA-Omniscience results: Argon’s hallucination rate is 15% against 51% for Astra at max, but its accuracy is 50% against Astra’s 63%. Argon knows less and invents less. Whether that is a better model depends on whether a wrong answer or no answer costs you more.
How does Gemini 4 Argon compare with Claude Opus 5.5?
Opus 5.5 is the stronger model on the independent index, 58 against 53, and the more expensive one per task, $5.98 against Argon’s $1.99 now and $3.98 later. At the standard rate the two have identical rate cards, $4 and $20, so the whole difference in cost per task comes from token consumption: Opus 5.5 generated 260 million output tokens on the index, more than twice Argon’s 110 million.
Google’s table gives Opus 5.5 two outright wins: Terminal-Bench 4.0 (66.4% to 57.4%) and PostTrainBench (49.3% to 45.3%). Both are agent-in-a-terminal tests, the closest of Google’s rows to day-to-day coding agent work. On the independent Vals Index, which Vals scores at Argon’s $4 and $20 standard rate, Argon ranks first of 41 at 68.90% for $15.68 per test, ahead of Sonnet 5.5 (67.04%, $21.34), Opus 5.5 (66.97%, $32.14) and Fable 5.1 (65.83%, $28.71).
So the two independent boards point in different directions. Artificial Analysis, whose index is weighted toward reasoning and agentic evaluations, puts Opus 5.5 five points clear. Vals, whose index weights finance evaluations such as Finance Agent v2 at more than half the score, puts Argon first and cheaper. Neither is wrong. They measure different work, and why the Vals Index and Artificial Analysis rank models differently walks through the weighting that causes the split.
Should you plan around Gemini 4 Argon?
For November, yes. For December, only for the right work. Until November 30, Argon at $2 and $10 is the cheapest way to reach 53 on the Artificial Analysis index. From December 1, at $4 and $20, GPT-6 Astra reaches the same score for less per task, and GPT-6.1 Sol gets within a point of both at a fifth of Astra’s rate with no expiry. For general agentic work, Claude Opus 5.5 still holds the top independent score.
Argon earns its standard rate in legal, finance and business automation, the rows where both Google’s table and the independent Vals and Zapier boards put it first. At $4 and $20 it lands between Astra and Opus 5.5 on cost per index task, and on Vals, which already prices it at the standard rate, it is still the cheapest of the top four per test.
Update, 2026-10-29: Google announced Gemini 4 Carbon on October 28 at Argon’s exact rate card, $2 and $10 per million tokens and then $4 and $20, for Fairwind cyber defenders only. Artificial Analysis launched its Intelligence Index v5 on October 27, and on that scale Carbon scores 59 against Argon’s 50, at $7.76 and $7.68 per index task at the $4 and $20 standard rate. The v4 figures above (53, $1.99 and $3.98) are as read on October 14. See Gemini 4 Carbon: pricing, benchmarks, access.
How do you start using Gemini 4 Argon?
- Turn on billing for the Gemini API. Argon is not on the free API tier, so a free AI Studio key will not reach it.
- Call the model ID
gemini-4-argon. It is the ID the Gemini API pricing page and Google’s announcement both list. - Route anything that can wait through Batch or Flex. Both cost half the standard rate, which keeps a December budget at the $2 and $10 you pay interactively today.
- Put December 1 in the budget. Cost a workload at $4 and $20 before committing to it, and re-run the cost calculator once the tracker moves to the standard rate.
For live rates as each model changes, the AI model tracker carries Argon as released at the introductory rate and will move it to $4 and $20 on December 1. The day each model launched is in the AI model release timeline, the rest of the month’s launches are in new AI models released in September 2026, and the general release is logged in new AI models released in October 2026.
Frequently asked questions
- How much does Gemini 4 Argon cost?
- Gemini 4 Argon costs $2 per million input tokens and $10 per million output tokens at its introductory rate, with cached input 95% off at $0.10. The Gemini API pricing page lists that rate through November 30, 2026, and standard pricing of $4 and $20 per million tokens from December 1. Batch and Flex requests cost half.
- Is Gemini 4 Argon available yet?
- It is available now. Google released Gemini 4 Argon on September 30, 2026 to cyber defenders in its Fairwind Program, then rolled it out globally on October 13: on the paid Gemini API tier under the model ID gemini-4-argon, to enterprises, and to Google AI Pro and Ultra subscribers in the Gemini app and AI Mode. It is not on the free API tier.
- Is Gemini 4 Argon better than GPT-6 Astra?
- They tie on the Artificial Analysis Intelligence Index at 53, Argon at high effort and Astra at max. Argon is cheaper per index task at the introductory rate, $1.99 against $3.26, but costs $3.98 at the standard rate from December 1, 2026, 22% more than Astra. Google reports Argon ahead on 13 of 18 benchmarks, while Astra leads on FrontierSWE v2 and Terminal-Bench Science.
- Is Gemini 4 Argon better than Claude Opus 5.5?
- Claude Opus 5.5 scores higher on the Artificial Analysis Intelligence Index, 58 against 53, and leads on Terminal-Bench 4.0 in the table Google published. Argon ranks first on the Vals Index at 68.90% against 66.97% for Opus 5.5, and costs less per task. At the standard rate both models charge $4 and $20 per million tokens.
- What is the Gemini 4 Argon context window?
- The Google model card lists a context window of up to 1 million tokens shared between input and output, so a long output uses up room that input could have had. The knowledge cutoff is March 2026, and the API model ID is gemini-4-argon.
Sources
Google (2026). Gemini 4 Argon: our next era of frontier intelligence. Google blog announcement, September 30, 2026. https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/ Verified 2026-10-01.
Google (2026). Introducing Gemini 4 Argon. Google blog announcement, October 13, 2026: global availability, access surfaces, introductory and standard rates. https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/ Verified 2026-10-14.
Google (2026). Gemini Developer API pricing. Google AI for Developers documentation: Argon rates through November 30, 2026 and from December 1, Batch, Flex and Priority tiers, implicit caching. https://ai.google.dev/gemini-api/docs/pricing Checked 2026-10-14.
Google DeepMind (2026). Gemini 4 Argon Model Card. Model card: 1M-token combined input and output context, March 2026 knowledge cutoff. https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-4-Argon-Model-Card.pdf Read 2026-10-14.
Artificial Analysis (2026). Gemini 4 Argon (high), GPT-6 Astra (max) and Claude Opus 5.5 model pages, and the models leaderboard. Independent benchmarking service. https://artificialanalysis.ai/models/gemini-4-argon and https://artificialanalysis.ai/leaderboards/models Read 2026-10-01, re-read 2026-10-14 (unchanged).
Vals AI (2026). Gemini 4 Argon benchmarks, cost and capabilities. Independent evaluation, Vals Index. https://www.vals.ai/models/google_gemini-4-argon Read 2026-10-01.
Zapier (2026). AutomationBench leaderboard, v1.0.6. Benchmark owner’s leaderboard. https://zapier.com/benchmarks Read 2026-10-01.
Datacurve (2026). DeepSWE leaderboard, updated September 22, 2026. Independent benchmark leaderboard. https://deepswe.datacurve.ai/ Read 2026-10-01.
Franzen, C. (2026). Google unveils Gemini 4 Argon, retaking benchmark lead over OpenAI and Anthropic, but in limited release. VentureBeat, secondary transcription of Google’s benchmark charts. https://venturebeat.com/technology/google-unveils-gemini-4-argon-retaking-benchmark-lead-over-openai-and-anthropic-but-in-limited-release
Bastian, M. (2026). Google Gemini 4 Argon closes the gap with OpenAI and Anthropic but doesn’t take a clear lead. The Decoder, secondary reporting of Artificial Analysis results. https://the-decoder.com/google-gemini-4-argon-closes-the-gap-with-openai-and-anthropic-but-doesnt-take-a-clear-lead/