This page compares two models on one job: turning a pasted PDF page into clean rows and columns ready for a spreadsheet. It covers row alignment, merged cells, cost and prompting, and ends with a fair way to test both on your own documents.
Aug 18, 2026 · 12 min read
GPT-5.6 Sol is the safer bet when a PDF table is visually difficult and keeping rows aligned matters most, since it leads by a wide margin on the closest exact-model document benchmark. Gemini 3.1 Pro is the better-value pick for clean, regularly structured tables, where it has its own strong direct extraction evidence and costs less than half Sol's rate.
That split rests on GDP.pdf, the only located benchmark that publishes both exact models on a professional-document task1, Gemini's dedicated table-extraction results on benchmarks that do not include Sol2, 3, and the published token prices4, 6, not on one benchmark that scores full-table reconstruction for both models under the same conditions.
The practical rule is to match the model to how visually difficult the page is. If the source is a clean, borderless or ruled table with a regular structure, Gemini's direct extraction evidence and lower price make it the better default. If the page has irregular spacing, scans, small print or two-level headers, Sol's stronger showing on hard professional documents is the safer starting point.
Your PDFs are scanned statements or reports with messy formatting. Sol's stronger showing on hard professional documents suits this the closest.
You extract clean, regularly structured tables at volume. Gemini's direct extraction evidence and lower price fit repeated, well-formed sources.
Your documents mix clean schedules with dense, multi-level headers. Neither model has proven itself safe as a sole extractor, so review disagreements between them.
Your source documents span several languages. Gemini has the broader published multilingual table-extraction evidence, though the exact scripts involved still need testing.
This page compares the two models through their API in one neutral setup: the same one-page PDF or page image, the same resolution, the same prompt and the same output schema for both, not one model inside a document-processing app against the other inside a different one.
The parts that matter for this task are preserving column identity, empty cells, repeated headers, merged cells, footnotes and row boundaries. Official docs and the closest independent table-extraction benchmarks come first.
We left tools out of the spec table on purpose. A document-processing platform's upload flow, OCR pipeline or export format depends on the app around the model, so the same model can behave differently in a chat product, the API or a workspace. Judging those here would compare software, not table extraction.
The model facts that actually affect pulling a table out of a PDF page. Tool features are left out, since they change with the app around the model.
Figures from OpenAI and Google documentation, checked August 18, 2026.
The answer changes by part of the job, not by brand. This is the main analysis: which model has the edge on each part of extracting a table, and what backs it up.
Better-choice calls map to dimensions the sources actually evaluated. Where the evidence is a benchmark that does not yet include both models, the row says so.
A useful test feels boring. Same page, same schema, no editing before scoring. Then judge what your team actually pays for: exact row and column counts, blanks preserved as blanks, and header-to-column alignment.
Include a clean ruled table, a borderless financial table, a table with two-level or merged headers, and a scan with small or faint text.
The same page image or one-page PDF, at the same effective resolution, with one shared prompt and one JSON Schema. Disable unrelated tools for both.
Perform the final evaluation in the interface the team will deploy, since preprocessing and default settings can differ between a chat product and the API.
Measure exact row and column counts, whether blanks stayed blank, and header-to-column alignment. For commercial work, hide model names and use blind review.
Public evidence favors Sol on hard professional documents and favors Gemini on clean, direct table extraction, but no single benchmark covers both models on the same difficult tables. Here is what each source helps judge.
The variation across Gemini's own table scores is itself informative: extraction accuracy depends heavily on the specific document type, not just the model.
Both models extract a more reliable table when explicitly told to transcribe rather than interpret, since an unprompted model may normalize a value instead of copying it exactly.
GPT-5.6 Sol does best when asked to inspect the page visually before transcribing, reconstruct the table exactly, and verify the final row and column counts against the page before answering. Start at medium or high reasoning effort and raise it only if testing shows fewer alignment errors10.
Gemini 3.1 Pro does best when the prompt separates grid detection from cell transcription: identify the complete column grid first, then output every physical row, representing merged headings as spans rather than extra columns9.
A GPT-5.6 Sol prompt: verify counts before answering
Inspect the page visually before transcribing it.
Reconstruct the table exactly.
Return JSON with column_headers, rows as arrays of
equal length, merged_header_spans, footnotes, and
uncertain_cells with row, column and visible text.
Preserve blank cells as null. Never shift a value
left to fill a blank. Do not calculate, normalize,
deduplicate or repair the source.
Verify the final row and column counts against the
page before answering.A Gemini 3.1 Pro prompt: grid first, then rows
Extract the visible table into the supplied JSON
Schema.
First identify the complete column grid. Then
output every physical row. Use null for empty
cells. Keep repeated values and subtotal rows.
Represent merged headings in header_spans, do not
insert extra body columns. If a cell crosses a
boundary or is unreadable, preserve its position
and add an uncertainty record instead of guessing.Structured JSON can be syntactically perfect while a cell sits in the wrong place. The useful question is where each model tends to slip, and what to change in the prompt.
One question first. Would one shifted cell materially damage the spreadsheet? Then follow the branch that matches your document.
A starting point, not a rule. Test on your own documents before you commit.
If one shifted cell would materially damage the spreadsheet and the source is visually difficult, start with GPT-5.6 Sol and validate the result. Its lead on the closest exact-model document benchmark is the strongest evidence available1.
If errors of any kind are unacceptable, run both models and review disagreements. Neither model is safe as a sole extractor, since even Sol failed most complete tasks on the hardest professional-document benchmark1.
If the table is clean and regular, or the workload spans many languages, start with Gemini 3.1 Pro. It costs less than half Sol's rate and has published multilingual table-extraction evidence6, 3. If the page has merged, nested or multi-line headers, start with Sol and require explicit span metadata3.
One case neither model nor Playgram solves on its own: an automated pipeline that extracts tables from thousands of incoming PDFs and writes them straight into a database with no review step. That needs a developer's own validation logic and infrastructure, not a chat workspace, so a team building that kind of automated pipeline should evaluate the models directly through OpenAI's or Google's API rather than through Playgram.
GPT-5.6 Sol is the safer default for messy PDF pages where keeping rows aligned is the primary goal. Gemini 3.1 Pro is the better-value option for clean, repetitive tables and already has strong direct table-extraction results.
The conclusion is not definitive. There is no public, independent, same-harness benchmark measuring both exact models on full-table reconstruction with cell-level alignment scoring. The strongest direct table benchmarks include Gemini but not Sol, and the strongest exact head-to-head benchmark is broader than extraction and was reported by OpenAI itself.
The safest final step is to test the shape of your own documents, not a generic PDF from the internet. A fair test needs the same setup for both models: the same page, the same prompt and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first table comes back. The cleaner the setup, the more the difference you see is really GPT-5.6 Sol vs Gemini 3.1 Pro, and not just which one happened to be easier to reach that day.
Upgrade as needed, and only pay for what you actually use
Save ~17% with the annual plan
Pro
Perfect for small and medium teams
Unlimited users & infinite memory
Multi-LLM chats
Granular access control to models
EU data residency
Ultra
Best for large, growing teams
Unlimited users & infinite memory
Multi-LLM chats
Unlimited use of DeepSeek V4 Flash
Granular access control to models
Choose US or EU data residency
Enterprise
Get in touch
For organizations with advanced needs
Unlimited users & SSO
Priority Support
Unlimited use of DeepSeek V4 Flash
Granular access control to models
Choose US or EU data residency
30-days money back guarantee