North-Micro-Vision-Instruct is the stronger all-rounder.
Scores updated · 17 tests both models report · How we compare
Where each one wins
Tests won in each of the one area where both have results. Each piece is one test, so a longer bar means more evidence; grey means the two scored within a point of each other.
The biggest differences
The tests each model wins by the widest margin, up to three each. Scores are out of 100.
Every test, side by side
All 17 tests both models report. The winning score is in its model's colour; marks a score checked independently.
Images and chartsNorth-Micro-Vision-Instruct
Other results14 tests, not counted
Tests outside the eight areas. They are not counted above: several are summary scores built from other tests, or the same test under another name.
- GQA TestDev_BalancedNorth-Micro-Vision-Instruct by 17.939.557.4+17.9
- MMLU-Pro testNorth-Micro-Vision-Instruct by 10.819.930.7+10.8
- MMMU (val) (Pass@1)LFM2.5-VL-1.6B by 7.740.632.9+7.7
- ChartQA TestNorth-Micro-Vision-Instruct by 6.973.980.8+6.9
- MMMU DEV_VALLFM2.5-VL-1.6B by 5.13832.9+5.1
- OCRBench v2_enLFM2.5-VL-1.6B by 4.841.536.7+4.8
- DocVQA-valNorth-Micro-Vision-Instruct by 4.487.792.1+4.4
- MMLU testNorth-Micro-Vision-Instruct by 446.450.4+4
- RealWorldQALFM2.5-VL-1.6B by 2.664.862.2+2.6
- HallusionBenchNorth-Micro-Vision-Instruct by 1.460.161.5+1.4
- MTL MMBench_DEVNorth-Micro-Vision-Instruct by 1.362.363.6+1.3
- MMMBNorth-Micro-Vision-Instruct by 1.171.772.8+1.1
- MMBench DEV_EN_V11tie69.668.7tie
- MMBench_DEVtie0.60.6tie
Questions people ask
Which is better, LFM2.5-VL-1.6B or North-Micro-Vision-Instruct?
North-Micro-Vision-Instruct wins the one area where both have results: images and charts. LFM2.5-VL-1.6B wins none.
How do you compare the two?
We use the 17 benchmark tests both models have published scores on. The verdict counts the 3 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 14 are listed but not counted, because several are summary scores or repeat a test. Each score is the one shown on the model's own page.