GLM-5.2vsMiniMax M3
Across 3 shared benchmarks, GLM-5.2 leads overall: GLM-5.2 wins 3, MiniMax M3 wins 0, with 0 ties and an average score difference of +8.11.
GLM-5.2
智谱AI · 2026-06-13 · Reasoning model
MiniMax M3
MiniMaxAI · 2026-06-01 · Multimodal model
GLM-5.23 wins(100%)(0%)0 winsMiniMax M3
Benchmark scores
Grouped by capability, sorted by largest gap within each. 3 shared benchmarks.
AI Agent - Tool Usage
GLM-5.2 1/1| Benchmark | GLM-5.2 | MiniMax M3 | Diff |
|---|---|---|---|
| TerminalBench 2.1 | 814 / 14Thinking High (With Tools) | 6611 / 14Thinking (With Tools) | +15 |
Coding and Software Engineer
GLM-5.2 1/1| Benchmark | GLM-5.2 | MiniMax M3 | Diff |
|---|---|---|---|
| SWE-Bench Pro - Public | 62.105 / 44Thinking (With Tools) | 597 / 44Thinking (With Tools) | +3.10 |
General Knowledge
GLM-5.2 1/1| Benchmark | GLM-5.2 | MiniMax M3 | Diff |
|---|---|---|---|
| LiveBench | 76.249 / 115Normal (No Tools) | 70.0240 / 115Deep Thinking (No Tools) | +6.22 |
Specs
| Field | GLM-5.2 | MiniMax M3 |
|---|---|---|
| Publisher | 智谱AI | MiniMaxAI |
| Release date | 2026-06-13 | 2026-06-01 |
| Model type | Reasoning model | Multimodal model |
| Architecture | MoE | MoE |
| Parameters | 753.33B | 428B |
| Context length | 1M | 1M |
| Max output | 128K | 512K |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | GLM-5.2 | MiniMax M3 |
|---|---|---|
| Text input | $1.4 / 1M tokens | ¥2.1 / 1M tokens |
| Text output | $4.4 / 1M tokens | ¥8.4 / 1M tokens |
| Cache read | $0.26 / 1M tokens | ¥0.42 / 1M tokens |
Summary
- GLM-5.2leads in:AI Agent - Tool Usage (1/1), Coding and Software Engineer (1/1), General Knowledge (1/1)
On average across the 3 shared benchmarks, GLM-5.2 scores 8.11 higher.
Largest single-benchmark gap: TerminalBench 2.1 — GLM-5.2 81 vs MiniMax M3 66 (+15).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.