Choose a document processing function

This document provides a comparison of the document processing functions available in BigQuery ML, which are ML.GENERATE_TEXT and ML.PROCESS_DOCUMENT. You can use the information in this document to help you decide which function to use in cases where the functions have overlapping capabilities.

At a high level, the difference between these functions is as follows:

ML.GENERATE_TEXT is a good choice for performing natural language processing (NLP) tasks where some of the content resides in documents. This function offers the following benefits:
- Lower costs
- More language support
- Faster throughput
- Model tuning capability
- Availability of multimodal models
For examples of document processing tasks that work best with this approach, see Explore document processing capabilities with the Gemini API.
ML.PROCESS_DOCUMENT is a good choice for performing document processing tasks that require document parsing and a predefined, structured response.

Function comparison

Use the following table to compare the ML.GENERATE_TEXT and ML.PROCESS_DOCUMENT functions:

	`ML.GENERATE_TEXT`	`ML.PROCESS_DOCUMENT`
Purpose	Perform any document-related NLP task by passing a prompt to a Gemini or partner model or to an open model. For example, given a financial document for a company, you can retrieve document information by providing a prompt such as `What is the quarterly revenue for each division?`.	Use the Document AI API to perform specialized document processing for different document types, such as invoices, tax forms, and financial statements. You can also perform document chunking.
Billing	Incurs BigQuery ML charges for data processed. For more information, see BigQuery ML pricing. Incurs Vertex AI charges for calls to the model. If you are using a Gemini 2.0 or greater model, the call is billed at the batch API rate. For more information, see Cost of building and deploying AI models in Vertex AI.	Incurs BigQuery ML charges for data processed. For more information, see BigQuery ML pricing. Incurs charges for calls to the Document AI API. For more information, see Document AI API pricing.
Requests per minute (RPM)	Not applicable for Gemini models. Between 25 and 60 for partner models. For more information, see Requests per minute limits.	120 RPM per processor type, with an overall limit of 600 RPM per project. For more information, see Quotas list.
Tokens per minute	Ranges from 8,192 to over 1 million, depending on the model used.	No token limit. However, this function does have different page limits depending on the processor you use. For more information, see Limits.
Supervised tuning	Supervised tuning is supported for some models.	Not supported.
Supported languages	Support varies based on the LLM you choose.	Language support depends on the document processor type; most only support English. For more information, see Processor list.
Supported regions	Supported in all Generative AI for Vertex AI regions.	Supported in the `EU` and `US` multi-regions for all processors. Some processors are also available in certain single regions. For more information, see Regional and multi-regional support.