Best LLM for structured data extraction
Unstructured text in, validated JSON out.
Why this ranking is opinionated
Reliability of JSON output is the dominant concern — a model with native JSON mode produces fewer parse errors and lower retry rates. Quality of free-text reasoning matters less.
Top 5 recommendations
ranked by monthly cost at this workload- · Cheapest qualifying option at this workload (~$0.00/mo).
- · Missing preferred: tools — may need a workaround.
- · ~$0.00/mo (+0% over the cheapest option).
- · 262,144 tokens of context — far above this use case's 16,000-token minimum.
- · Supports preferred capabilities: tools.
- · ~$0.00/mo (+0% over the cheapest option).
- · 262,144 tokens of context — far above this use case's 16,000-token minimum.
- · Supports preferred capabilities: tools.
- · ~$0.00/mo (+0% over the cheapest option).
- · 1,048,576 tokens of context — far above this use case's 16,000-token minimum.
- · Missing preferred: tools — may need a workaround.
- · ~$0.00/mo (+0% over the cheapest option).
- · 1,048,576 tokens of context — far above this use case's 16,000-token minimum.
- · Missing preferred: tools — may need a workaround.
Frequently asked questions
What makes a good LLM for structured data extraction?
Reliability of JSON output is the dominant concern — a model with native JSON mode produces fewer parse errors and lower retry rates. Quality of free-text reasoning matters less.
What capabilities matter most for structured data extraction?
For structured data extraction the typical filters are: json_mode, and a context window of at least 16k tokens. The ranking on this page weights monthly cost (at the workload defaults shown above) most heavily, then capability fit.
What is currently the cheapest LLM for structured data extraction?
At the typical workload defaults, Uncensored (free) from Venice ranks cheapest right now (~$0 / month). Plug your own monthly token volumes into the calculator on this page for a workload-specific number.
Is the cheapest LLM always the right choice for structured data extraction?
Not always. Cheap models often trade off reasoning quality, tool reliability, or context size. Use the cheapest as a baseline and benchmark against a tier-up model on your own evaluation set before committing to a contract — quality differences compound over millions of tokens.