MindStudio used the fixed, reproducible third-party KingBench 3 benchmark (an 80-point scale, 10 points per task) to compare GLM-5.3 with Fable 5, Opus 4.8, Opus 5, Kimi K3, and Qwen3.8 Max using the same prompt set.
| Model | KingBench 3 score |
|---|---|
| GLM-5.3 | 73/80 (91.25%) — the highest score ever recorded on this benchmark |
| Fable 5 | 82.5% |
| Qwen3.8 Max | 81.25% |
| Opus 4.8 | 80% |
| Opus 5 | 77.5% |
| Kimi K3 | 77.5% |
| GLM-5.2 (about two months earlier) | 75% |
Key observation: Within roughly two months, with parameters and architecture unchanged, GLM-5.2 → 5.3 jumped from 75% to 91.25%, attributable entirely to post-training—a remarkably large improvement.
Elevator simulation (multiple groups of people, random floors, and queue-based dispatching for three elevators): 8 points (Fable 5 scored 9).
Invisible contact lens case Three.js 3D model (clickable L/R lids): 3 → 8 points (5.2 scored only 3 points on the same task).
Folding table in Three.js (slider-controlled 3D folding animation): a perfect 10 (Fable 5 scored 9).
Panda eating a hamburger in SVG: a perfect 10 (details: rosy cheeks, a bamboo background, and hamburger crumbs).
Archery game (moving targets + leaderboard): a perfect 10; the model even self-verified the game logic first (Fable 5 scored 8).
Permutation-counting math problem (correct answer: 2460): a perfect 10.
End-to-end local pipeline (generate a dataset → fine-tune Gemma 2B → serve a web UI): a perfect 10, with no human intervention required throughout.
The hardest task—a 3D dual-time-zone wristwatch: 7 points (most other models scored 0–3 points; Fable 5 scored 4 and Opus 5 scored 3), matching the historical best for this task. The finished product included a GMT-style dial, a sweeping seconds hand, date/day-of-week windows, and a second-time-zone bezel.
On this benchmark, GLM-5.3 narrowly beat Fable 5 and clearly outperformed Opus 5 and Kimi K3.
More noteworthy is the post-training leap from 5.2 → 5.3: "That kind of gain from post-training alone suggests there's still more [headroom]"—there is still substantial room for improvement from post-training alone.
ZAI positions GLM-5.3 as being dedicated to security analysis (code auditing and vulnerability discovery), alongside an "open source shield initiative": defensive security capabilities remain open source, while high-risk abuse capabilities are provided through restricted access.
The test confirms that GLM-5.3 closes the gap for open-source models between "backend logic vs. frontend polish"—on the same task, it can produce both a clean UI and correct simulation logic.
"GLM-5.3 scored 91.25%, the highest result recorded on that test, ahead of Opus 5, Kimi K3, and Qwen3.8 Max, and roughly… This is a necessary excerpt; read the original source for full context.
"The score matters because it comes from a fixed, repeatable set of coding and simulation challenges run against every m… This is a necessary excerpt; read the original source for full context.
GLM-5.3
