Gemini 2.0 Flash
Previous gen workhorse — fast multimodal model with excellent price-to-performance
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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 2.0 Flash
Gemini 2.0 Flash is a budget large language model from google, priced at $0.1/1M input tokens and $0.4/1M output tokens. It is 96% cheaper than the market average and best suited for fast multimodal tasks. The 1M 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 2.0 Flash is one of the most cost-effective options for high-volume tasks.
Gemini 2.0 Flash supports prompt caching at $0.025/1M — 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.