GLM-5.2 vs MiniMax M3 评测对比

See key specs and per-benchmark scores for each model/mode. Scroll horizontally for all columns. 当前对比 2 个模型的评测数据与核心参数。

GLM-5.2

智谱AI

Release: 2026-06-13
Context length: 1M
Parameters: 7,533.3 (act 400)
最大输出: 128,000 tokens

Model profile·Playground

Best overall

GLM-5.2 · 76.24

Best single

GLM-5.2 · LiveBench 76.24

Modality coverage

MiniMax M3 · 3 modalities

Head to head

GLM-5.2

MiniMax M3

AheadTiedBehind

Benchmarks

Wins

Losses

+6.22

Average diff

Performance benchmarks

Compare benchmark results across thinking modes and tool usage.

Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology

Filter: Best Available·2 modes · 1 Benchmark

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Benchmark score table

Complete scores for each model/mode across selected benchmarks.

1 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.

Benchmark	GLM-5.2	MiniMax M3
LiveBench 综合评估	76.24Standard Mode	70.02Deep Thinking Mode

API price comparison

Side-by-side input/output token pricing

Detailed feature breakdown

Licensing, MoE architecture, and multi-modality support.

Features & specs	GLM-5.2智谱AI	MiniMax M3MiniMaxAI
Core specsRelease	2026-06-13	2026-06-01
Context length	1M	1M
Parameters	7533.3	4280
Active parameters	400	230
Max output	128000	524288
MoE	Yes	Yes
LicenseCode Open Source	Closed Source	Closed Source
Weights Open Source	Closed Source	Not provided
Commercial use	免费商用授权	不可以商用
Modality supportText Input/Output	/	/
Image Input/Output	Not provided	/
Video Input/Output	Not provided	/
ResourcesPaper / report	GLM-5: from Vibe Coding to Agentic Engineering	MiniMax M3: Coding & Agentic Frontier with MSA Architecture, 1M Context and Native Multimodality