Head to head
Kimi K3 vs GLM-5.2
Which is better, Kimi K3 or GLM-5.2?
GLM-5.2 finishes ahead of Kimi K3 on the index, 87.1 to 85.1. It is the stronger of the two on Speed, Control and Value, while Kimi K3 still leads on Craft. Both are scored on the same rubric, in the same week, and neither placing is sponsored.
Updated
Kimi K3
20 of 73
85.1
One of the strongest open-weights models, and it reads a million tokens without blinking.
GLM-5.2
13 of 73
87.1
Still the GLM to choose when the licence matters most: MIT, a million tokens, no conditions.
The four scores, side by side
| Score | Kimi K3 | GLM-5.2 | Difference |
|---|---|---|---|
| Craft | 88 | 84 | +4 |
| Speed | 74 | 82 | +8 |
| Control | 91 | 95 | +4 |
| Value | 78 | 92 | +14 |
| Index score | 85.1 | 87.1 | +2.0 |
Which to pick
Pick GLM-5.2 unless Craft is what decides it for you, which is exactly where Kimi K3 is the better answer.
Kimi K3
Moonshot published K3 at a very large scale, with native vision, an explicit thinking mode and a million-token window, and in October it still ranks among the top open models on both independent leaderboards. Two caveats matter: the licence restricts commercial use above a revenue threshold, so it is open weights rather than open source, and the hosted API costs three dollars in and fifteen out, priced like a Western frontier model rather than a Chinese one.
Where it shines
- Million-token context handled properly rather than nominally
- Native vision and an explicit thinking mode
- Among the highest human-preference ratings of any open model
GLM-5.2
GLM-5.2 made the open-versus-closed argument concrete in the summer: close to the closed flagships on coding and reasoning, a million tokens of context, and weights under MIT with no user threshold or field-of-use clause. GLM-5.3 has since improved on it, but under Zhipu's own licence rather than MIT, which keeps 5.2 relevant for anyone who needs the plainest possible terms on a model this capable. You can run it, tune it and ship it without sending a byte to anyone.
Where it shines
- MIT licence with no user-count or field-of-use restrictions
- Million-token context on weights you can hold yourself
- Shares its base model with the stronger GLM-5.3