GPT-5.5 vs Gemini 3.5 Flash
Two raw models, the expensive one and the cheap one, and the cheap one is better on a single page. Gemini 3.5 Flash led GPT-5.5 on short documents, scans, and Hebrew and Arabic, at a fraction of the price. GPT held up a little better on multi-page and long documents, though both fail badly there. Neither comes with a document product.
Both rows are a general model with a file and a field list and nothing else, and they came fifth and sixth, three and a half points apart. The gap isn't where you'd guess from the price.
Gemini was the better single-page reader: second only to DocuPipe on forms, receipts, and photographed or rotated scans, and ahead of GPT on handwriting and on Hebrew and Arabic layouts. GPT was ahead on multi-page statements and on long registers, where it returned nothing on nine documents but Gemini did worse, posting the lowest long-document average in the test with runs that failed outright. On non-English documents they were level.
On resilience both fail structurally on length and nothing about the call fixes it. GPT fails a little less completely. Neither has anything to tune and neither is safe on a corpus with long documents.
On price Gemini wins by a wide margin: $1.50 and $9 per million tokens against GPT's $5 and $30, plus a free tier for development. Both offer batch discounts. Per page, Gemini is a fraction of a cent and GPT is a few cents.
On product there's nothing to compare, because neither has one. Both have strong general APIs with native PDF input, Files APIs, and structured outputs. Gemini accepts up to 1,000 pages a request, and GPT accepts Office and spreadsheet files directly. Neither has a dashboard, webhooks, document jobs, review, or retention controls, and Gemini's data policy differs between its free and paid tiers.
For short documents, Gemini, and it isn't close on price. For a corpus with multi-page documents, neither is a good choice, but GPT degrades a little more gracefully. If you're choosing a raw model for extraction and aren't already committed to one of these, Claude outscored both.
Teams already standardized on OpenAI whose documents run to a few pages, who'll chunk anything longer themselves.
High-volume single-page work such as receipts and photographed forms, where cost per page decides.
How it scores, area by area
The accuracy score comes from the benchmark. Every other score is ours, with the reason written next to it. We use the same weights on every review and comparison.
| Area | GPT-5.5 | Gemini 3.5 Flash |
|---|---|---|
| Accuracy35% of total | 7.6Ranked 5th of 8 systems on the benchmark. | 7.3Ranked 6th of 8 systems on the benchmark. |
| Resilience15% of total | 4.0Nine documents came back empty and there is no setting that fixes it; you have to build the chunking yourself. | 3.0Several runs failed outright and the long-document average was the lowest in the test; excellent one day and empty the next depending on page count. |
| Price15% of total | 8.0$5 and $30 per million tokens, the priciest direct model, still only cents per page; batch at half price. | 10.0$1.50 and $9 per million tokens plus a free tier; well under a cent per page. |
| Getting started10% of total | 3.0No document product. You build the tooling yourself. | 4.0No document product, though the free tier makes trying it trivial. |
| API and integration10% of total | 5.0Strong general SDKs, PDF and Office input, a Files API, batch at half price and structured outputs; no document jobs or webhooks. | 5.0Strong general SDKs, the largest PDF page limit of the direct models, a Files API and structured output; no document jobs or webhooks. |
| Data control7.5% of total | 5.0Platform policy only. Nothing document-specific. | 4.0Platform policy only, and it differs between the free and paid tiers. |
| Compliance7.5% of total | 6.0Platform certifications apply to the API; your pipeline is your responsibility. | 6.0Platform certifications apply to the API; your pipeline is your responsibility. |
| Overallweighted | 6.1weighted score out of 10 | 6.2weighted score out of 10 |
How each area is scored
- Accuracy (35%). The benchmark result, on a 0 to 10 scale.
- Resilience (15%). Does it just work? Fewer modes to choose, no tuning to reach the ranked result, no runs that fail outright, and no documents that come back quietly empty. Informed by the benchmark, judged by us.
- Price (15%). Published cost per page for parse plus extraction, cheapest scoring highest. Unpublished pricing is marked down.
- Getting started (10%). How far you get without a sales call: a dashboard, a free tier with no card, help authoring schemas, a test environment.
- API and integration (10%). Webhooks and which tier they are on, SDKs, no-code connectors, whether the API is built around document jobs at all.
- Data control (7.5%). Deletion controls, scheduled retention, zero-retention availability and on which tier, choice of region.
- Compliance (7.5%). Certifications stated on public pages: SOC 2, ISO 27001, HIPAA with a BAA, GDPR, private deployment options.
By document type
Average accuracy on each group of benchmark documents, both systems on the same files. Every document and score is on the evidence page.
| Document type | GPT-5.5 | Gemini 3.5 Flash |
|---|---|---|
| Short forms, invoices, receiptsone page · 30 documents | 88.7% Good | 94.1% Good |
| Scans and photosimages, not PDFs · 7 documents | 88.7% Good | 95.5% Strong |
| Statements, filings, reports2 to 5 pages · 21 documents | 85.1% Good | 87.5% Good |
| Non-English documents12 languages · 25 documents | 69.4% Mixed | 68.9% Mixed |
| Long registers, catalogs, directories6 pages and up · 21 documents | 50.4% Weak | 28.3% Weak |
Price, product, and policies
Full reviews: GPT-5.5, Gemini 3.5 Flash.
| GPT-5.5 | Gemini 3.5 Flash | |
|---|---|---|
| Pricing model | Per token through the OpenAI API. No document-specific product or plan. | Per token through the Gemini API. No document-specific product or plan. |
| Entry price | $5 per million input tokens, $30 per million output tokens. | $1.50 per million input tokens, $9 per million output tokens on the paid tier. |
| Cost per page | Depends on page size and output length; typically a cent to several cents per page, higher than the other two direct models. | Depends on page size and output length; typically well under a cent to a couple of cents per page, the cheapest of the direct models. |
| Free tier | No ongoing free tier for API use. | Yes. A rate-limited free tier for development. |
| Pay as you go | Yes. Pure usage billing; batch processing at half price. | Yes. Pure usage billing. |
| Dashboard | No document product. A playground for prompts; upload, review and results tooling is yours to build. | No document product. AI Studio for trying prompts; upload, review and results tooling is yours to build. |
| Webhooks | None for document jobs. You run the pipeline. | None for document jobs. You run the pipeline. |
| API and SDKs | Official SDKs in Python, TypeScript and others. PDF, Office and spreadsheet input up to 50 MB per request, a Files API, a batch API at half price, structured outputs. | Official SDKs in Python, JavaScript, Go and others. PDF input up to 1,000 pages or 50 MB, a Files API with 48-hour storage, structured output. |
| Data retention | Governed by OpenAI's API data policies. Nothing document-specific. | Governed by Google's API data policies, which differ between the free and paid tiers. Nothing document-specific. |
| Compliance | OpenAI's platform certifications apply to the API; your pipeline around it is your responsibility. | Google's platform certifications apply to the API; your pipeline around it is your responsibility. |
We checked these against the vendors' own pricing and documentation pages on 2026-09-11. Sources: GPT-5.5: developers.openai.com; Gemini 3.5 Flash: ai.google.dev. Prices change, so confirm before you budget.
Frequently asked
Which is better for document extraction, GPT-5.5 or Gemini 3.5 Flash?
Close overall, 76.48% to 72.98% in our benchmark. Gemini was ahead on single-page documents, scans, and Hebrew and Arabic. GPT held up a little better on multi-page documents. Both collapse on long ones.
How much cheaper is Gemini than GPT-5.5?
Gemini is $1.50 per million input tokens and $9 per million output. GPT-5.5 is $5 and $30. Gemini also has a free tier for development.
Scores are macro-average field accuracy from the DocuBench 0.2.0, 72-document run. Every document, answer key, and raw extraction is open in the explorer; the scoring rules are on the methodology page, and the replication kit re-runs the scorer. A document a system could not process scores zero rather than being dropped.