Gemini 4 Argon cost & VRAM calculator
What does Gemini 4 Argon cost to run for your workload, and can you run it on your own hardware? Set your workload below - we compute per-provider API cost live and tell you honestly whether local hardware can run it.
This is where owning stops making sense
Gemini 4 Argon is a large-billion-parameter model. No rig an individual can buy runs it - so unlike a workstation GPU model, there's no break-even to compute. The honest answer for nearly everyone is the per-provider API cost below.
The defense-first frontier model, priced like the middle tier. Gemini 4 Argon (September 30) is Google’s new frontier: real-world software engineering, legal and finance knowledge work, and a defensive-cybersecurity lead - rolled out first to vetted cyber defenders through the Fairwind Program while Google iterates guardrails under the US government’s voluntary pre-release process. Broad developer access follows “as soon as possible”; nobody outside Fairwind can run it today. The pricing datapoint. Introductory $2 per million input tokens and $10 output, cached input at 95 percent off - the same slot OpenAI’s GPT-6.1 Sol opened the day before. Two flagship-class labs pricing their best defensive models at the cheap tier in two days is the month’s thesis datapoint: the frontier competes on price now, and gates its autonomy instead (OpenAI shelved the GPT-6.1 Astra upgrade the same week; Google gates Argon’s access rather than shipping it wide). Google’s fielded receipts, not benchmarks: Argon agents applied memory optimizations across Google’s own data centers - 300+ TiB freed at rollout - and beat a published quantum-algorithm baseline by 40 percent in minutes. These are internal claims, no external replication, no published eval tables at announcement. Hosted only. No weights, no local run.
Your workload
API cost for your workload
| Provider | Rate ($/1M) | Monthly cost |
|---|---|---|
| Google Gemini | $2.00 in · $10.00 out | $0.05 cheapest |
Monthly cost is an estimate from list prices and your workload - verify against the provider before committing. Cached fraction applies the cache rate to that share of input.
Can you run it locally?
Gemini 4 Argon has no published quantization that fits a rig one person can buy, so there's no local-hardware recommendation and no break-even to compute. The honest answer is the per-provider API cost above.
The defense-first frontier model, priced like the middle tier. Gemini 4 Argon (September 30) is Google’s new frontier: real-world software engineering, legal and finance knowledge work, and a defensive-cybersecurity lead - rolled out first to vetted cyber defenders through the Fairwind Program while Google iterates guardrails under the US government’s voluntary pre-release process. Broad developer access follows “as soon as possible”; nobody outside Fairwind can run it today. The pricing datapoint. Introductory $2 per million input tokens and $10 output, cached input at 95 percent off - the same slot OpenAI’s GPT-6.1 Sol opened the day before. Two flagship-class labs pricing their best defensive models at the cheap tier in two days is the month’s thesis datapoint: the frontier competes on price now, and gates its autonomy instead (OpenAI shelved the GPT-6.1 Astra upgrade the same week; Google gates Argon’s access rather than shipping it wide). Google’s fielded receipts, not benchmarks: Argon agents applied memory optimizations across Google’s own data centers - 300+ TiB freed at rollout - and beat a published quantum-algorithm baseline by 40 percent in minutes. These are internal claims, no external replication, no published eval tables at announcement. Hosted only. No weights, no local run.
See the model card for the full architecture notes and any cloud subscription plans.