Best LLM for Data Extraction (2026)

Short version: for data extraction and structured output, GPT-5.6 leads on JSON reliability and sticking to a schema. Reach for Claude Sonnet 5 when the extraction needs actual reasoning about messy or inconsistent source documents. GPT-5.6 Luna is the cost-efficient pick for high-volume pipelines where the documents are clean and predictable.

What data extraction demands from an LLM

Data extraction is unforgiving. Content generation can be approximately right; an extraction pipeline can't. The requirements are hard:

• Schema adherence — the model must return exactly the requested JSON structure, every time, without missing fields or inventing values
• Null handling — when a field is not present in the source document, the model must return null rather than hallucinating a plausible value
• Consistency — identical documents should produce identical extractions across runs
• Reasoning under ambiguity — real documents are messy. Dates in multiple formats, names with variations, prices in different currencies. The model must handle these consistently
• Volume cost — extraction pipelines often process thousands of documents per day. Input token cost at scale is a primary consideration

Top recommendations

1. GPT-5.6 — Best for structured output reliability

GPT-5.6 with OpenAI's native structured output mode is the most reliable model for extraction. Give it a JSON schema and it matches it almost every time — no stray keys, no missing required fields, right data types throughout.

That's because the mode uses constrained decoding: the model is physically held to valid JSON that fits your schema, rather than producing it by following an instruction and hoping. It's a real reliability gap versus models doing the latter.

If downstream systems break when the output shape changes, that gap is worth the higher price.

2. Claude Sonnet 5 — Best for complex, ambiguous documents

Claude Sonnet 5 is the better choice when source documents are irregular. Contracts with non-standard clause structures, invoices from multiple countries with different formatting conventions, research papers with inconsistent citation styles — these require reasoning about document structure, not just pattern matching.

Its knack for complex instructions also pays off in multi-stage extraction — pull all the dates, normalise them to ISO 8601, then work out which is the execution date and which is the effective date. That kind of conditional logic runs more reliably on Claude than on GPT-5.6.

3. GPT-5.6 Luna — Best for high-volume clean document extraction

For pipelines processing standardised documents — consistent invoice formats, fixed-structure form submissions, templated reports — GPT-5.6 Luna delivers extraction accuracy close to Sol at 25× lower cost.

The key qualifier is document consistency. Luna performs well when source documents follow a predictable pattern. It degrades more than Sol when document structure varies significantly.

Side-by-side comparison

Cost per document — extraction pipeline at scale

Assuming extraction from a typical business document: 1,500 input tokens (document content + system prompt with schema) and 200 output tokens (extracted JSON).

FAQ

Last verified: August 2026 · Back to LLM Selector