gemini-2.0-flash-lite

gemini-2.0-flash-lite is a budget-tier language model from Google. gemini-2.0-flash-lite is a proprietary model available exclusively through Google's API. Gemini 2.0 is Google's next-generation multimodal family, featuring improved reasoning, native tool use, and a new Flash variant optimized for speed and cost.

gemini-2.0-flash-lite is priced at $0.075/1M input tokens and $0.3/1M output tokens. Output tokens are 4× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, gemini-2.0-flash-lite costs approximately $0.23 per 1,000 API calls. Google does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.075/1M input rate.

Its 1.0M-token context window is among the largest available — sufficient for processing entire codebases, book-length documents, or hours of transcribed audio in a single API call. Median latency (P50 time-to-first-token) is approximately 180ms — fast enough for real-time interactive applications. Throughput is approximately 400 tokens/second — fast enough to stream responses at reading speed with headroom for concurrent requests. Maximum output length is 8K tokens — adequate for most generation tasks including essays, code functions, and detailed responses.

gemini-2.0-flash-lite is best suited to cost, speed. Supported capabilities include vision inputs (images alongside text), function calling / tool use for agentic workflows, streaming token output for real-time UIs. Vision support enables document analysis, screenshot understanding, diagram interpretation, and visual question answering — images are converted to tokens at the standard input rate based on resolution. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how gemini-2.0-flash-lite compares to similar models across these dimensions.

gemini-2.0-flash-lite sits in the budget tier — the most cost-efficient models, best for high-volume simple tasks, classification, extraction, and applications where cost per call is the primary constraint. Unlike open-weights alternatives, gemini-2.0-flash-lite is only available via Google's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing gemini-2.0-flash-lite to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (50/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.