ChatGPT 5.2 vs Gemini 3: Spreadsheet Data Extraction Accuracy

Spreadsheet data extraction is one of the most deceptively fragile tasks in professional workflows, because errors rarely appear as obvious failures and instead surface as silent misalignment, datatype corruption, or partial omission that only becomes visible downstream, often after decisions have already been made.

In finance, operations, analytics, and reporting, spreadsheet accuracy is not about presentation or visualization, but about whether the extracted data remains structurally faithful to the original sheet, including headers, row boundaries, datatypes, and embedded exceptions.

This comparison evaluates ChatGPT 5.2 and Gemini 3 strictly on their ability to extract spreadsheet data without distortion, under real-world conditions.

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Spreadsheet extraction accuracy is about structural fidelity, not cleanliness.

A spreadsheet is not a database table.

It often contains merged cells, multi-row headers, embedded notes, totals mixed with data, and human formatting decisions that convey meaning implicitly rather than structurally.

Accurate extraction means respecting that structure, not “cleaning” it into something that looks more logical but no longer matches the source.

Most professional extraction failures happen when a system silently decides to normalize what it does not fully understand.

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Key dimensions of spreadsheet extraction accuracy

Dimension	Why it matters
Header preservation	Prevents column misalignment
Row boundary integrity	Avoids shifted records
Datatype fidelity	Preserves numeric meaning
Multi-table isolation	Prevents blending
Exception retention	Keeps footnotes and caveats

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ChatGPT 5.2 approaches spreadsheets as datasets first.

ChatGPT 5.2 tends to interpret spreadsheets as dataset-like objects, mapping them into internal table representations that resemble dataframes.

This approach works extremely well when the spreadsheet already behaves like a clean dataset, with consistent headers, one table per sheet, and uniform datatypes.

Under those conditions, extraction is typically stable, repeatable, and easy to validate.

However, this same strength becomes a liability when the sheet relies on visual structure rather than strict tabular logic.

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ChatGPT 5.2 spreadsheet extraction behavior

Aspect	Observed behavior	Practical impact
Clean tables	Very accurate	High reliability
Multi-row headers	Risky	Column drift
Merged cells	Fragile	Alignment errors
Mixed datatypes	Coerced	Loss of nuance
Best fit	Analytical datasets	Finance and BI

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Gemini 3 changes behavior when operating inside Google Sheets.

Gemini 3 exhibits two distinct extraction profiles depending on context.

When used via generic file upload, its behavior is broadly similar to other multimodal models.

When used inside Google Sheets, however, its extraction accuracy improves significantly because it can leverage sheet-native constructs such as explicit ranges, table selection, and cell references.

This native context allows Gemini to respect the spreadsheet’s original structure rather than inferring one.

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Gemini 3 spreadsheet extraction behavior

Aspect	Observed behavior	Practical impact
Range-based extraction	Strong	Boundary safety
Multiple tables	Manageable	Reduced blending
Visual structure	Better preserved	Fewer shifts
Large sheets	Selective	Scope discipline
Best fit	Operational sheets	Business workflows

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The real difference is not intelligence, but extraction philosophy.

ChatGPT 5.2 implicitly assumes that a spreadsheet should behave like a table.

Gemini 3, when embedded in Sheets, implicitly assumes that a spreadsheet is a workspace, where meaning is distributed across layout, ranges, and context.

Neither assumption is universally correct.

The risk emerges when the assumption does not match the document.

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Extraction philosophy comparison

Factor	ChatGPT 5.2	Gemini 3
Default interpretation	Dataset	Workspace
Structure inference	Automatic	Range-driven
Visual cues	Often ignored	Partially respected
Operator control	Prompt-based	UI + prompt
Error visibility	Low	Medium

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Common extraction failure modes differ in subtle ways.

ChatGPT 5.2 most often fails through silent normalization, where headers are flattened, merged cells are expanded incorrectly, or mixed columns are coerced into a single datatype.

Gemini 3 most often fails through scope truncation, where only part of the intended range is processed, especially if the user does not explicitly define boundaries.

Both failure modes can produce outputs that look plausible, which is precisely what makes them dangerous.

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Typical failure patterns

Model	Failure mode	Resulting risk
ChatGPT 5.2	Structural normalization	Hidden misalignment
Gemini 3	Range truncation	Partial extraction

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Professional reliability depends on how much control the user has.

ChatGPT 5.2 becomes reliable when the user enforces schema-first extraction, explicitly defining columns, datatypes, and null-handling rules.

Gemini 3 becomes reliable when the user enforces range-first extraction, explicitly selecting the exact cells or tables to be processed.

The model that performs better is the one whose control surface matches the user’s tolerance for upfront setup.

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Stabilization strategies

Strategy	ChatGPT 5.2	Gemini 3
Schema enforcement	Essential	Optional
Range selection	Indirect	Native
Table preview	Recommended	Often implicit
Manual verification	Periodic	Frequent

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Spreadsheet extraction accuracy reflects workflow maturity.

Neither ChatGPT 5.2 nor Gemini 3 is inherently unreliable.

They fail when used outside their ideal extraction paradigm.

ChatGPT 5.2 aligns best with teams that treat spreadsheets as proto-databases and are willing to formalize structure.

Gemini 3 aligns best with teams that treat spreadsheets as collaborative workspaces and rely on visual and spatial organization.

Accuracy emerges when the extraction method respects the spreadsheet’s original intent, not when the spreadsheet is forced to conform to the tool.

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