Text models versus multimodal models: screenshot information cannot rely solely on transcription
When processing files with screenshots, compare the differences between extracting text first and directly inputting images, explaining how to separately verify table relationships, visual cues, and character-by-character accuracy.
Choose an approach
If a question relies only on complete text, you can extract the text first and then pass it to a text model; if a question depends on chart positions or visual content, evaluate multimodal models that support the corresponding input. Multimodal does not mean any file can be read directly, and text extraction does not guarantee that layout relationships are preserved. First, identify which area the answer comes from, then choose the processing method to retain that information.
First determine which information in the screenshot affects the answer
Text models process the text provided to them; image content must first be converted by other tools. Multimodal models can receive supported image inputs but still have recognition limitations. OpenAI's vision guide specifically lists small print and spatial relationships, so you cannot interpret 'can upload images' as meaning all visual details will be read correctly.
For example, in a bar chart with only numbers and no axis labels, pure text extraction might lose the meaning corresponding to those numbers. If the task is simply to read clearly printed numbers, OCR's line-by-line text and coordinates may be easier to verify. Both approaches can be combined; the key is not to treat information lost during conversion as if the model had seen it.
Use the same screenshot to check answerable questions
Prepare a single screenshot containing both body text and charts, requesting separately to read one number and explain the relationship between numbers. The text approach saves the complete extracted content; the multimodal approach retains the actual image sent in. When comparing answers, refer back to the original chart to cite evidence, distinguishing errors in character reading from errors in understanding relationships, rather than only evaluating whether the text flows smoothly.
If small print is unclear after screenshot compression, first correct input quality before comparing models. When cropping, retain legends and headers to avoid cutting out only the target number. First check if processing can be completed using a plain text page, then add cross-column or scanned pages; this article did not run image recognition comparisons nor report accuracy rates.
When checking costs, simultaneously record verification work
Requests to read images directly and workflows that first use OCR may follow different billing methods; specifics depend on the model and tool documentation. Besides request consumption, also record time spent on image preprocessing and manual verification. If many pages contain only clear text, extracting then analyzing may facilitate retrieval; this requires confirmation using your own data.
Finally, preserve the correspondence between the original image and output, testing whether an area without an answer leads to speculation. Multimodal conclusions should be traceable back to the visual content; extracted text should match the original line by line. Content that cannot be read clearly must be explicitly marked, then provide a clearer image or confirm manually; neither of the two workflows can be treated as a no-verification option.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.