Gemini 1.5 Pro
First model with 2M token context window — great for massive document analysis
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Tips to reduce cost
- →Use prompt caching to reuse repeated system prompts
- →Trim whitespace and reduce verbose instructions
- →Use a smaller model for classification or routing tasks
- →Batch async requests to get 50% discount (OpenAI/Anthropic)
- →Cache identical requests at the application layer
Similar models from Google
Compared at your current token settings
About Gemini 1.5 Pro
Gemini 1.5 Pro is a mid-range large language model from google, priced at $1.25/1M input tokens and $5/1M output tokens. It is 52% cheaper than the market average and best suited for massive document analysis. The 2M context window makes it suitable for very long documents, large codebases, and book-length inputs.
For most production workloads, the cost breakdown is dominated by input tokens (system prompts, context, retrieved documents) rather than output. At this price point, Gemini 1.5 Pro is a solid choice when balancing quality and cost at scale.
Gemini 1.5 Pro supports prompt caching at $0.31/1M — a 75% discount on repeated input tokens. For applications with a fixed system prompt or repeated document context (RAG, chatbots, agents), enabling caching is the single highest-leverage cost optimization available.