Comparing AI Vision in 2026: Which Model Wins in Image Recognition and Text Extraction?

We tested current AI vision models on three OCR-heavy tasks: extracting a quote, turning a receipt into JSON, and converting a screenshot table into CSV. The short answer: Gemini 3.1 Pro Preview and GPT-5.5 are still the safest top picks.

Quick answer

Joint winners: Gemini 3.1 Pro Preview and GPT-5.5 passed all three tasks cleanly.
Gemini 3 Flash Preview passed all three tasks, but its receipt JSON needed light cleanup.
Claude Opus 4.7 passed the API retest, but its receipt JSON also needed cleanup.
Grok 4.20 read the data correctly, but added extra wording on the quote task.
Gemini 3.1 Flash Lite Preview is fast and capable, but failed strict CSV formatting on the table task.

Executive summary: scores and ranking

Rank	Model	Score
Joint #1	Gemini 3.1 Pro Preview	10/10
Joint #1	GPT-5.5	10/10
#2	Gemini 3 Flash Preview	9/10
#3	Claude Opus 4.7	8.5/10
#4	Grok 4.20	8/10
#5	Gemini 3.1 Flash Lite Preview	7/10

Test setup

The comparison used three practical tasks that matter for spreadsheet-ready extraction.

Quote extraction: return only the Steve Jobs quote from an image.
Receipt extraction: return vendor, date, items, subtotal, tax, total, and payment_last4 as JSON.
Table extraction: convert a warehouse exception report screenshot into CSV.

OCR test images

Steve Jobs quote image used for AI vision text extraction test — Quote example: extract only the quote text.

Receipt image used for AI vision JSON extraction test — Receipt example: extract structured fields as JSON.

Warehouse exception report image used for AI vision table extraction test — Table example: convert a screenshot into CSV.

Result details

Model	Quote	Receipt JSON	Table CSV
Gemini 3.1 Pro Preview	Passed	Passed	Passed
GPT-5.5	Passed	Passed	Passed
Gemini 3 Flash Preview	Passed	Passed, but wrapped JSON in code fences and used description instead of name	Passed
Claude Opus 4.7	Passed	Passed, but wrapped JSON in code fences and used description instead of name	Passed
Grok 4.20	Read quote, but added labels and commentary	Passed	Passed
Gemini 3.1 Flash Lite Preview	Passed	Passed, but wrapped JSON in code fences	Failed strict CSV because Blue mug, 12oz was not quoted

Receipt JSON output

The correct receipt output is:

{
  "vendor": "CAFÉ NORTH",
  "date": "2026-05-06",
  "time": "08:14",
  "items": [
    { "quantity": 2, "name": "Espresso", "price": 7.00 },
    { "quantity": 1, "name": "Almond Croissant", "price": 4.50 },
    { "quantity": 1, "name": "Oat Latte", "price": 5.25 }
  ],
  "subtotal": 16.75,
  "tax": 1.38,
  "total": 18.13,
  "payment_last4": "1234"
}

Table CSV output

The correct table output is:

SKU,Product,Qty,Due date,Status
A-104,"Blue mug, 12oz",24,2026-05-10,READY
B-210,Notebook - dotted,8,2026-05-12,BACKORDER
C-007,Travel bottle,16,2026-05-14,READY
D-332,Desk lamp matte,5,2026-05-16,CHECK PHOTO

Which model should you use in AI for Sheets?

For spreadsheet workflows, start with Gemini 3.1 Pro Preview. It tied for first overall and is the most natural fit for Google Workspace. Use GPT-5.5 if your workflow is OpenAI-first.

Use Gemini 3 Flash Preview or Claude Opus 4.7 when you can clean up formatting. Use Grok 4.20 or Gemini 3.1 Flash Lite Preview only with validation.

=VISION("Extract the text exactly and return JSON with fields: vendor, date, total", A2)

Final verdict

Gemini 3.1 Pro Preview and GPT-5.5 are joint winners because both passed every task cleanly.

Gemini 3 Flash Preview is the best lower-friction Gemini alternative from this retest. Claude Opus 4.7 also passed when each image was sent separately, so its earlier issue was image context handling rather than OCR ability. Grok 4.20 and Gemini 3.1 Flash Lite Preview are usable with validation.

Frequently Asked Questions

Which model won this AI vision test?

Gemini 3.1 Pro Preview and GPT-5.5 share first place. Both passed the quote image, receipt JSON, and table CSV tasks cleanly.

How did Gemini 3 Flash Preview perform?

It passed the quote, receipt, and table tasks. The only issue was receipt formatting: it wrapped JSON in code fences and used description instead of name for item fields.

Did Claude Opus 4.7 really fail?

It failed the earlier multi-turn image-context flow, but passed when each image was sent separately through the API. That means the issue was context handling, not raw OCR ability.

Why not rank only by OCR accuracy?

Because real workflows also need clean formatting, reliable attachment handling, and structured output that can go straight into a spreadsheet.