Best Models for Structured Data & Fact Extraction
Structured Data & Fact Extraction: Quality, Cost, and Completion Do Not Align
The quality bar and scope of the evidence
The supplied results do not specify a universal quality threshold, nor do they state how many tasks or models make up the full Structured Data & Fact Extraction category. This comparison therefore uses a practical, task-level bar: a model must meet the extraction quality required by the workflow, while also producing answers reliably and fitting its cost and serving configuration. The evidence covers structured fact and structured-output extraction, but it does not support a complete category census or a definitive ranking across every measured model.
Best quality versus lowest cost
On the highest reported extraction score, gpt-5.6-sol leads with 9.74 out of 10 for structured fact extraction [^1]. qwen3.7-plus is close at 9.67 out of 10 (±0.15), with a reported cost of $0.02 per task run in a separate comparison [^1]. Other high-scoring results include gpt-5.4-nano at 9.46 out of 10 (±0.25) and minimax/minimax-m3 at 9.36 out of 10 (±0.16) [^1]. grok-4.5 scores 9.33 out of 10, including a costed result at $0.03 per task run [^1].
The strongest costed choice is deepseek-v4-flash, which scores 9.54 out of 10 (±0.14) at $0.0004 per task run [^1]. It is not the highest-scoring model in the supplied results, but it combines a score above the other costed alternatives cited here with the lowest listed cost. The direct quality comparison with gpt-5.6-sol is 9.54 versus 9.74; however, no comparable cost is supplied for gpt-5.6-sol, so the evidence cannot quantify the cost gap between the quality leader and the cheapest model that meets a stated quality bar. Since no common bar is specified, deepseek-v4-flash can be called the cheapest listed option only when 9.54 is sufficient for the application—not as the cheapest model proven to clear a universal threshold.
These figures come from different reported configurations, so they should not be treated as a definitive cross-model leaderboard. In particular, the lower price attached to deepseek-v4-flash does not by itself establish that it is preferable when the application requires the maximum attainable extraction quality.
Reliability changes the practical ranking
A high score is useful only when the model returns an answer. gpt-5.4-nano scores 9.46 out of 10 when it answers, but answers on 76% of attempts [^1]. qwen3.7-plus scores 9.67 out of 10 but answers on only 40% of attempts [^1], while grok-4.5 scores 9.33 out of 10 with a 78% answer rate [^1]. minimax/minimax-m3 presents the sharpest warning: although it scores 9.36 out of 10 for structured extraction, a separate measurement reports a 0% answer rate [^1]. It should therefore not be used as a dependable sole model without a fallback or retry path.
The evidence does not provide a corresponding answer-rate figure for deepseek-v4-flash or gpt-5.6-sol, so it cannot establish whether either model is more reliable in completed production workflows. That limitation matters when choosing between the low-cost model and the nominal quality leader.
Which models are competitive—and where the verdict disagrees
For routine, cost-sensitive extraction, deepseek-v4-flash is the clearest starting point: its 9.54 out of 10 score and $0.0004 per task run offer a strong reported quality-cost combination [^1]. For the highest measured quality, gpt-5.6-sol is the leading result at 9.74 out of 10 [^1], provided its unspecified cost and completion behavior fit the application. qwen3.7-plus is a plausible alternative when 9.67 out of 10 justifies $0.02 per task run, but its 40% answer rate makes it a risky sole route for workflows that require dependable completion [^1].
gpt-5.4-nano and grok-4.5 are measured high-quality alternatives, but their reported answer rates and prices complicate the comparison: nano scores 9.46 out of 10 at $0.0069 per task run in batch mode and answers on 76% of attempts [^1], while grok-4.5 scores 9.33 out of 10, costs $0.03 per task run in its costed result, and answers on 78% of attempts [^1]. minimax/minimax-m3 is not competitive as a standalone production choice in the reported configuration because its 0% answer rate outweighs its 9.36 out of 10 score [^1].
The individual structured-extraction results therefore disagree with a simple category-level verdict. gpt-5.6-sol wins on the highest reported quality, deepseek-v4-flash wins the costed quality trade-off, qwen3.7-plus remains near the top on score but has weak answer completion, and minimax/minimax-m3 demonstrates that a strong conditional score can coexist with unusable observed answer availability. Because the reported configurations differ and some costs or reliability measures are missing, the evidence cannot determine one universally best model for every structured-data workload.
Routing rule
Route routine structured fact extraction to deepseek-v4-flash when 9.54 out of 10 is sufficient and the $0.0004 per task run cost is the priority; route quality-critical extraction to gpt-5.6-sol when its 9.74 out of 10 result matters and its cost and completion behavior are acceptable; consider qwen3.7-plus, gpt-5.4-nano, or grok-4.5 only when their task-specific quality, cost, and answer-rate trade-offs fit the workflow, and do not use minimax/minimax-m3 as the sole route without fallback coverage.
Check the live benchmark at https://llm-bench.kapualabs.com/ to set the quality bar your workflow actually needs. Use it to compare the reported batch and synchronous prices yourself before fixing the production route.