Multimodal · Benchmark profile
A display-only Liquid AI extraction metric measuring field-name agreement between requested schema fields and extracted JSON fields.
Data verified 27 Jul 2026 · Methodology 1.8.0
Benchmark score on Liquid image-to-JSON extraction schema consistency F1
LFM2.5-VL-1.6B-Extract leads at 99.6%, followed by LFM2.5-VL-450M-Extract (98.8%).
2 ranked models · higher is better · labels show rank and score
| Model | Rank | Provider | Score |
|---|---|---|---|
| LFM2.5-VL-1.6B-Extract | #1 | LiquidAI | 99.6% |
| LFM2.5-VL-450M-Extract | #2 | LiquidAI | 98.8% |
One best score per model · higher is better
| Rank | Model | Provider | License | Evidence use | Score |
|---|---|---|---|---|---|
| #1 | LFM2.5-VL-1.6B-Extract lfm2-5-vl-1-6b-extract | LiquidAI | open | Estimated reference | 99.6% |
| #2 | LFM2.5-VL-450M-Extract lfm2-5-vl-450m-extract | LiquidAI | open | Estimated reference | 98.8% |
About Liquid image-to-JSON extraction schema consistency F1
A display-only Liquid AI extraction metric measuring field-name agreement between requested schema fields and extracted JSON fields. Results stay tied to the exact model variant and evaluation system. Multiple systems for the same model use the best published score on this page; overall Lumina scoring uses the median of ranking-eligible rows.
Open benchmark source ↗FAQ
A display-only Liquid AI extraction metric measuring field-name agreement between requested schema fields and extracted JSON fields.
LFM2.5-VL-1.6B-Extract by LiquidAI currently leads with 99.6%.
2 models in the LuminaBench cohort have a qualifying score on this benchmark.
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