Gemini 3 Pro uses a token-based pricing model where input tokens and output tokens are billed separately. Long-context calls cost more simply because they contain more tokens. The model supports extremely large input windows, but prices scale linearly with the token volume. Deeper thinking levels also tend to generate more tokens internally, which can further increase cost if the output becomes longer.
Long context also affects cost indirectly through latency. Large inputs take longer to encode and run through the model, which can increase compute time. If your application retries or fans out multiple calls using the same long context, the cost can multiply quickly. Some implementations also allow discounted pricing for cached repeated inputs, so reusing context rather than re-sending it can reduce your costs significantly.
To manage pricing, developers often combine Gemini 3 Pro with a retrieval pipeline. Instead of sending huge documents or entire datasets, you chunk the content, embed it, and store it in a vector database such as Milvus or Zilliz Cloud. At inference time, you retrieve only the relevant pieces and send a much smaller context to the model. This keeps long-context usage as an exception rather than the default, leading to predictable performance and dramatically lower spending.