GPT-5.5 vs Unstructured
A raw model that fills in fields against a parsing layer that doesn't. GPT-5.5 called directly came fifth. Unstructured, with the same field-filling step applied to its parse, came last. Both are cheap and both fail on long documents. GPT is the better choice for extraction. Unstructured is the better choice for feeding a search index with clean data terms, which is a different job.
GPT takes a file and a field list and returns the fields. Unstructured splits documents into elements for retrieval systems. To rank it we ran the same field-filling step over its output that every system got. The nine-point gap is smaller than most on this site because the two share a weakness.
That weakness is length. GPT returned nothing on nine long documents because a single call runs out of room. Unstructured lost the row structure of the same documents in its parse. On clean English text, spreadsheets, and photographed pages the two were close, and Unstructured was ahead on Hebrew and Arabic text direction. GPT led on single-page documents, on multi-page statements, and on Chinese and Japanese scans, where Unstructured came back nearly empty. Neither came close to DocuPipe on the long documents.
On resilience GPT's failure is structural and predictable. Unstructured's is a matter of category. It can't finish the job alone, and on hard layouts the structure the next step needs is gone before it runs.
On price both are cheap. GPT is cents per page on token billing with batch at half price. Unstructured is a cent and a half a page after 10,000 free pages, no card, for parsing only. A model step, possibly GPT itself, sits on top and costs extra.
On product Unstructured is the more complete for its own purpose: a hosted platform with connectors, partitioning, chunking, and enrichment, a Python SDK, REST API, MCP server, and the open-source library, zero data retention on every plan, and SOC 2 Type 2, HIPAA, and GDPR on the pricing page. GPT offers a strong general API with PDF and Office input, a Files API, batch, and structured outputs, and no document product, retention controls, or pipeline.
For structured extraction from short documents, GPT. For ingesting ordinary English documents into a search index, Unstructured, with a model behind it for any fields you need. For long documents, neither.
Structured extraction from short, printed documents, with your own pipeline.
Search and retrieval pipelines over ordinary English documents where you want cheap parsing, connectors, and zero-retention terms on every plan.
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 | Unstructured |
|---|---|---|
| Accuracy35% of total | 7.6Ranked 5th of 8 systems on the benchmark. | 6.8Ranked 8th 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. | 4.0Needs your own extraction step to produce fields at all, and on long or tabular documents the structure is lost before that step runs. |
| Price15% of total | 8.0$5 and $30 per million tokens, the priciest direct model, still only cents per page; batch at half price. | 8.0$0.015 per page for parsing, but you add your own model cost to get structured fields. |
| Getting started10% of total | 3.0No document product. You build the tooling yourself. | 6.0Hosted platform and open-source library, 10,000 free pages, no card; not an extraction product. |
| 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. | 6.0Python SDK, MCP server and a full workflow API with connectors, but built around partitioning rather than schema extraction, and webhooks are not marketed. |
| Data control7.5% of total | 5.0Platform policy only. Nothing document-specific. | 8.0Zero data retention stated on every plan. |
| Compliance7.5% of total | 6.0Platform certifications apply to the API; your pipeline is your responsibility. | 8.0SOC 2 Type 2, HIPAA and GDPR; dedicated instance and VPC on Business. |
| Overallweighted | 6.1weighted score out of 10 | 6.6weighted 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 | Unstructured |
|---|---|---|
| Short forms, invoices, receiptsone page · 30 documents | 88.7% Good | 80.2% Good |
| Scans and photosimages, not PDFs · 7 documents | 88.7% Good | 86.5% Good |
| Statements, filings, reports2 to 5 pages · 21 documents | 85.1% Good | 84.9% Good |
| Non-English documents12 languages · 25 documents | 69.4% Mixed | 62.2% Mixed |
| Long registers, catalogs, directories6 pages and up · 21 documents | 50.4% Weak | 32.5% Weak |
Price, product, and policies
Full reviews: GPT-5.5, Unstructured.
| GPT-5.5 | Unstructured | |
|---|---|---|
| Pricing model | Per token through the OpenAI API. No document-specific product or plan. | Per page, parse only. You supply the extraction step yourself. |
| Entry price | $5 per million input tokens, $30 per million output tokens. | 10,000 free pages, no card. Then $0.015 per page pay-as-you-go. Business plan on request. |
| 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. | $0.015 per page for parsing. Add the cost of whatever model you run on the output to get structured fields. |
| Free tier | No ongoing free tier for API use. | Yes. 10,000 pages, no credit card. |
| Pay as you go | Yes. Pure usage billing; batch processing at half price. | Yes. |
| Dashboard | No document product. A playground for prompts; upload, review and results tooling is yours to build. | Hosted platform for building workflows from source connectors (files, storage, databases) through partitioning and chunking to destination connectors, plus a widely used open-source library. |
| Webhooks | None for document jobs. You run the pipeline. | Workflow and job notifications through the API; not marketed as webhooks on the pricing page. |
| 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. | Python SDK, REST endpoints, an MCP server, and workflow and job management APIs. |
| Data retention | Governed by OpenAI's API data policies. Nothing document-specific. | Zero data retention stated across all plans. |
| Compliance | OpenAI's platform certifications apply to the API; your pipeline around it is your responsibility. | SOC 2 Type 2, HIPAA, GDPR. Dedicated instance and VPC options on Business. |
We checked these against the vendors' own pricing and documentation pages on 2026-09-11. Sources: GPT-5.5: developers.openai.com; Unstructured: unstructured.io, docs.unstructured.io. Prices change, so confirm before you budget.
Frequently asked
Is GPT-5.5 better than Unstructured for extraction?
Yes, 76.48% to 67.67% in our benchmark. Unstructured is a parsing layer rather than an extraction product. With the same field-filling step applied it held up on clean English text and little else.
Can I use Unstructured with GPT?
That's a common pattern. Unstructured parses and chunks, and a model fills the fields. Our benchmark scored Unstructured's parse with a fixed extraction step applied, which is what that pattern measures.
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.