Best LLM for Data Extraction (2026)

Bottom line up front: For data extraction and structured output, GPT-5.6 leads on JSON reliability and schema adherence. Claude Sonnet 5 is the stronger choice when extraction requires reasoning about ambiguous or inconsistent source documents. GPT-5.6 Luna is the best cost-efficient option for high-volume extraction pipelines where documents are clean and well-structured.

What data extraction demands from an LLM

Data extraction is unforgiving. Unlike content generation where approximate output is acceptable, extraction pipelines have hard requirements:

• 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 data extraction. When you specify a JSON schema, it adheres to it with near-100% consistency — no extra keys, no missing required fields, correct data types throughout.

OpenAI's structured output implementation uses constrained decoding — the model is literally constrained to produce valid JSON matching your schema. This is a significant reliability advantage over models that produce JSON through instruction following alone.

For production extraction pipelines where downstream systems depend on consistent output structure, this reliability difference is worth the higher cost.

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.

Claude's strength in following complex instructions also helps with multi-stage extraction: first extract all dates, then normalise them to ISO 8601, then identify which is the execution date vs the effective date. This kind of conditional extraction logic works more reliably with Claude than with 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