GLM-4.6V (106B-A12B) is the stronger all-rounder.
Scores updated · 9 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.
What it costs
Prices per million tokens, roughly 750,000 words. The bars show the cost of a million tokens read plus a million written.
The biggest differences
The tests each model wins by the widest margin, up to three each. Scores are out of 100.
Where GLM-4.6V (106B-A12B) pulls ahead
Where Qwen3 VL Thinking (8B) pulls ahead
No clear win on a test scored out of 100.
Every test, side by side
All 9 tests both models report. The winning score is in its model's colour; marks a score checked independently.
Images and chartsGLM-4.6V (106B-A12B)
Other results5 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.
- LiveCodeBenchQwen3 VL Thinking (8B) by 9.348.758+9.3
- AIME 2025Qwen3 VL Thinking (8B) by 8.471.980.3+8.4
- HMMT 2025Qwen3 VL Thinking (8B) by 3.357.360.6+3.3
- MMBench (EN)GLM-4.6V (106B-A12B) by 2.292.890.5+2.2
- MATH-Visiontie63.562.7tie
Questions people ask
Which is better, GLM-4.6V (106B-A12B) or Qwen3 VL Thinking (8B)?
GLM-4.6V (106B-A12B) wins the one area where both have results: images and charts. Qwen3 VL Thinking (8B) wins none.
How do you compare the two?
We use the 9 benchmark tests both models have published scores on. The verdict counts the 4 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 5 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.