gemini-3-flash-preview

gemini-3-flash-preview is a efficient-tier language model from Google. gemini-3-flash-preview is a proprietary model available exclusively through Google's API. The Gemini 3 family is Google's model series for this capability tier.

gemini-3-flash-preview is priced at $0.5/1M input tokens and $3/1M output tokens. Output tokens are 6× 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-3-flash-preview costs approximately $2.00 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 $0.5/1M 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 300ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 200 tokens/second — fast enough to stream responses at reading speed with headroom for concurrent requests. Maximum output length is 66K tokens — sufficient for long-form content generation, detailed code files, and extended reports.

gemini-3-flash-preview is best suited to speed, cost, vision. 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-3-flash-preview compares to similar models across these dimensions.

gemini-3-flash-preview sits in the efficient tier — optimized for speed and cost while maintaining solid quality, ideal for high-volume or latency-sensitive workloads. Unlike open-weights alternatives, gemini-3-flash-preview is only available via Google's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing gemini-3-flash-preview to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (82/100) 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.