GLM-5.2vsGLM-4.7
Across 4 shared benchmarks, GLM-5.2 leads overall: GLM-5.2 wins 4, GLM-4.7 wins 0, with 0 ties and an average score difference of +11.30.
GLM-5.24 wins(100%)(0%)0 winsGLM-4.7
Benchmark scores
Grouped by capability, sorted by largest gap within each. 4 shared benchmarks.
General Knowledge
GLM-5.2 2/2| Benchmark | GLM-5.2 | GLM-4.7 | Diff |
|---|---|---|---|
| HLE | 54.708 / 159Thinking (With Tools) | 42.8042 / 159 | +11.90 |
| GPQA Diamond | 91.2015 / 179Thinking (No Tools) | 85.7045 / 179 | +5.50 |
Coding and Software Engineer
GLM-5.2 1/1| Benchmark | GLM-5.2 | GLM-4.7 | Diff |
|---|---|---|---|
| SWE-Bench Pro - Public | 62.105 / 44Thinking (With Tools) | 40.6040 / 44 | +21.50 |
Math and Reasoning
GLM-5.2 1/1| Benchmark | GLM-5.2 | GLM-4.7 | Diff |
|---|---|---|---|
| AIME 2026 | 99.201 / 15Thinking (No Tools) | 92.907 / 15 | +6.30 |
Specs
| Field | GLM-5.2 | GLM-4.7 |
|---|---|---|
| Publisher | 智谱AI | 智谱AI |
| Release date | 2026-06-13 | 2025-12-22 |
| Model type | Reasoning model | Chat model |
| Architecture | MoE | MoE |
| Parameters | 753.33B | 358B |
| Context length | 1M | 200K |
| Max output | 128K | 132072 |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | GLM-5.2 | GLM-4.7 |
|---|---|---|
| Text input | $1.4 / 1M tokens | Not public |
| Text output | $4.4 / 1M tokens | Not public |
| Cache read | $0.26 / 1M tokens | Not public |
One or both models have incomplete public pricing.
Summary
- GLM-5.2leads in:General Knowledge (2/2), Coding and Software Engineer (1/1), Math and Reasoning (1/1)
On average across the 4 shared benchmarks, GLM-5.2 scores 11.30 higher.
Largest single-benchmark gap: SWE-Bench Pro - Public — GLM-5.2 62.10 vs GLM-4.7 40.60 (+21.50).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.