GLM-5.3 · Media / benchmark · Independent measurement
A fixed prompt set supplies a bounded outside reference, not repeated retesting.
Unverified: the original source could not be rechecked. Historical figures below are not current verified results.
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.
The figures, task set, reasoning tier, and client conditions apply only to the listed source and collection snapshot. Different versions, harnesses, or providers must not be compared directly; undisclosed parameters remain unknown.
For a reproduction, fix the model version, provider or client, reasoning tier, tools, task-set version, sample count, and collection date, and record failures, retries, and human corrections. Full steps are in the source notes below.
MindStudio (official blog of the AI development platform) · Luis Chavez-Mattos (Product Director, Editor) · Original publication date 2026-08-14 · Site edit date 2026-09-20
Open original sourceGLM-5.3
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