Model comparison

GLM-5.2 vs MiniMax-01

GLM-5.2 has enough public results to be ranked (#44); MiniMax-01 does not yet, so treat this comparison as directional.

Last verified October 11, 2026. 1 shared benchmarks.

GLM-5.2 Z.ai (Zhipu)

51.1

Rank #44 Confirmed

MiniMax-01

MiniMax

33.4

Unranked Sparse

Summary

They share 1 benchmark with published results for both. GLM-5.2 scores higher in 2 categories and MiniMax-01 in 0 categories; 2 gaps are clear of the uncertainty.
The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 33.6.
The biggest single-benchmark swing is Kagi LLM Benchmark: 62.6% for GLM-5.2 and 42.5% for MiniMax-01.
MiniMax-01 is cheaper at $0.20 / $1.10 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
MiniMax-01 accepts more context: 1M tokens versus 1M.

Side by side

GLM-5.2 and MiniMax-01 specifications
	GLM-5.2	MiniMax-01
Provider	Z.ai (Zhipu)	MiniMax
Noometry Index	51.1	33.4
Released	2026-06-13	—
Weights	Open	Open
Context window	1M	1M
Max output	131K	900K
Input $ / M tokens	$1.40	$0.20
Output $ / M tokens	$4.40	$1.10
Results tracked	51	2

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Category by category

Coding Not comparable

GLM-5.2: 51.3 (#41), MiniMax-01: —

Coding benchmarks
Benchmark	GLM-5.2	MiniMax-01
SWE-bench Verified	78.7%	—
DeepSWE	43.8%	—
FrontierCode	24.5%	—
LMArena WebDev	1603	—
SciCode	50.5%	—
WeirdML	70.1%	—
LMArena Coding	1485	—
ALE-Bench	1,047	—

Agentic & Tool Use Not comparable

GLM-5.2: 32.4 (#63), MiniMax-01: —

Agentic & Tool Use benchmarks
Benchmark	GLM-5.2	MiniMax-01
APEX-Agents	45.2%	—
τ²-bench Banking	37.1%	—
PostTrainBench	31.7%	—
GBAEval	0%	—
Vending-Bench 2	8,314	—

Reasoning GLM-5.2 leads

GLM-5.2: 42.3 (#52), MiniMax-01: 22.4

Reasoning benchmarks
Benchmark	GLM-5.2	MiniMax-01
Kagi LLM Benchmark	62.6%	42.5%
ARC-AGI-2	22.8%	—
SimpleBench	58.8%	—
NYT Connections (extended)	74.3%	—
ARC-AGI-1	77%	—
CritPt	20.9%	—
Chess Puzzles	21%	—
EBR-Bench	9.5%	—
LMArena Hard Prompts	1480	—
Mystery Game Puzzles	19%	—
DTBench	93.6%	—
LMCA	45.8%	—
Surface Evolver Bench	55.6%	—
Epoch Capabilities Index	151.78	—

Math Not comparable

GLM-5.2: 55.7 (#43), MiniMax-01: —

Math benchmarks
Benchmark	GLM-5.2	MiniMax-01
FrontierMath (Tiers 1-3)	59.2%	—
FrontierMath Tier 4	29.3%	—
MathArena Final-Answer Competitions	67.6%	—
OTIS Mock AIME 2024-2025	86.4%	—
ProofBench	35%	—
LMArena Math	1482	—

Knowledge GLM-5.2 leads

GLM-5.2: 57.1 (#40), MiniMax-01: 33.6

Knowledge benchmarks
Benchmark	GLM-5.2	MiniMax-01
GPQA Diamond	91.9%	—
SimpleQA Verified	34.2%	—
Confabulations	—	23.9%
LMArena Expert	1486	—

Multilingual Not comparable

GLM-5.2: 55.8 (#26), MiniMax-01: —

Multilingual benchmarks
Benchmark	GLM-5.2	MiniMax-01
LMArena Non-English	1459	—
LMArena Chinese	1519	—
LMArena French	1479	—
LMArena German	1468	—
LMArena Japanese	1451	—
LMArena Korean	1445	—
LMArena Russian	1466	—
LMArena Spanish	1477	—

Instruction Following Not comparable

GLM-5.2: 76.9 (#34), MiniMax-01: —

Instruction Following benchmarks
Benchmark	GLM-5.2	MiniMax-01
LMArena Instruction Following	1465	—

Long Context Not comparable

GLM-5.2: 45.3 (#43), MiniMax-01: —

Long Context benchmarks
Benchmark	GLM-5.2	MiniMax-01
LMArena Longer Query	1479	—

Writing & Preference Not comparable

GLM-5.2: 70.4 (#21), MiniMax-01: —

Writing & Preference benchmarks
Benchmark	GLM-5.2	MiniMax-01
LMArena Text	1470	—
LMArena Creative Writing	1462	—
EQ-Bench Creative Writing	1757	—
EQ-Bench 4	1222	—
LMArena Multi-Turn	1469	—

Frequently asked questions

Is GLM-5.2 better than MiniMax-01?