gemini 1.5 pricing

Gemini 1.5 Pro Pricing (Gemini)

Gemini 1.5 Pro pricing shifts can silently increase monthly spend. This page gives a stable baseline for planning and routing decisions.

Validate Gemini 1.5 Pro token economics before scaling usage in production workflows.

Live pricing snapshot

Input / 1K

$0.0013

Prompt tokens

Output / 1K

$0.0050

Completion tokens

Input / 1M

$1.2500

Large-volume planning

Catalog models

144

Current pricing catalog size

Proof from the product

Real UI snapshot from AI Cost Board used in production workflows.

AI Cost Board pricing table and model comparison view

Live pricing and comparison workflow for model selection decisions.

When to choose Gemini 1.5 Pro

  • Use Gemini 1.5 Pro when the quality profile fits your production workload and cost per request stays inside target margin.
  • Benchmark with the same prompt mix you plan to run in production rather than generic sample prompts.
  • Track provider-level latency, retries, and errors with cost to validate real routing performance.

Common cost drivers to monitor

  • Retries and fallback loops can multiply spend even when token pricing looks cheap.
  • Prompt/context growth silently increases input token cost over time.
  • Latency regressions often correlate with retries and timeout-driven duplicate requests.

Recommended next steps

  • Use this pricing baseline to align engineering and finance assumptions.
  • Track actual request logs and provider analytics after launch.
  • Add budget alerts and anomaly detection for the owning project/workspace.

Last updated: Mar 5, 2026, 04:00 AM

ProviderModelInput $/1MOutput $/1MAction
Geminigemini-1.5-pro$1.2500$5.0000Open calculator

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Frequently asked questions

How is Gemini 1.5 Pro pricing calculated?

Start with one production workflow and then scale assumptions. This page uses live input/output rates for Gemini and converts them into per-1K and per-1M views.

Can I use this Gemini 1.5 Pro page for production budgeting?

Yes. Use the embedded calculator or pricing table first, then multiply by expected request volume and retry behavior.

What should I monitor besides token rates?

Track retries, latency, and error rate. Production spend is pricing multiplied by operational behavior.

How do I turn this into continuous tracking?

Use AI Cost Board to monitor cost per team, project, model, and provider with budget alerts and anomaly detection.

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