Google Gemini API Pricing: Pro vs Flash Costs
Explore the pricing structure of Google's Gemini API, comparing the massive 1M context window of Gemini Pro with the ultra-fast Gemini Flash.
Google's Gemini API ecosystem offers some of the most competitive pricing in the generative AI market, largely due to Google's massive custom silicon (TPU) infrastructure.
Gemini's standout feature is its massive 1 Million to 2 Million token context window, allowing you to upload entire books, hour-long videos, or massive datasets in a single prompt. Here is how much it costs to use.
1. Gemini 3.1 Pro Pricing
Gemini 3.1 Pro is Google's flagship reasoning model, designed to compete directly with GPT-5.5 and Claude Opus. It handles complex multi-modal tasks (video, audio, and text reasoning) seamlessly.
- Input Cost: $2 per 1 Million tokens.
- Output Cost: $12 per 1 Million tokens.
- Cached Input Cost: $0.5 per 1 Million tokens.
Gemini Pro is aggressively priced. At $2 per million input tokens, it is often cheaper than its direct flagship competitors, making it an excellent choice for heavy data processing.
2. Gemini 3.1 Flash Pricing
Gemini Flash is Google's lightweight, ultra-fast model. It retains the massive 1M token context window but sacrifices some deep reasoning capability for sheer speed and cost-efficiency.
- Input Cost: $0.35 per 1 Million tokens.
- Output Cost: $1.05 per 1 Million tokens.
- Cached Input Cost: $0.0875 per 1 Million tokens.
If you are building a high-volume chatbot, processing millions of simple text documents, or generating rapid classifications, Gemini Flash is one of the cheapest and fastest models available today.
3. Multi-Modal Pricing (Images & Video)
Unlike some competitors that charge a flat fee per image, Google converts media into tokens. When you upload an image or video to the Gemini API, it is automatically converted into a fixed number of tokens based on its resolution and length, and then billed at the standard input token rate. This makes processing thousands of images highly predictable.
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