Model comparison

DeepSeek-V3.2-Speciale vs GLM-4.5

GLM-4.5 is the stronger model overall, scoring 41.4 to 39.1 on the Noometry Index.

Last verified October 10, 2026. 2 shared benchmarks.

DeepSeek-V3.2-Speciale

DeepSeek

39.1

Rank #159 Reported

GLM-4.5 Z.ai (Zhipu)

41.4

Rank #125 Confirmed

Summary

They share 2 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 1 category and GLM-4.5 in 2 categories; 3 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GLM-4.5 leads 57.7 to 46.1.
The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 40.6% for GLM-4.5.
DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
GLM-4.5 accepts more context: 131K tokens versus 128K.

Side by side

DeepSeek-V3.2-Speciale and GLM-4.5 specifications
	DeepSeek-V3.2-Speciale	GLM-4.5
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	39.1	41.4
Released	2025-12-01	2025-07-27
Weights	Open	Open
Context window	128K	131K
Max output	128K	98K
Input $ / M tokens	$0.58	$0.60
Output $ / M tokens	$1.68	$2.20
Results tracked	3	27

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Category by category

Coding GLM-4.5 leads

DeepSeek-V3.2-Speciale: 39.9 (#136), GLM-4.5: 41.1 (#121)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-4.5
WeirdML	46.7%	40.6%
SWE-bench Verified (bash only)	—	54.2%
LMArena Coding	—	1434
ALE-Bench	—	344.82
AlgoTune	—	1.52

Reasoning DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 32.1 (#65), GLM-4.5: 27.7 (#99)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-4.5
SimpleBench	52.6%	—
Kagi LLM Benchmark	—	57.9%
LMArena Hard Prompts	—	1430

Math Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.5: 36.3 (#119)

Math benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-4.5
LMArena Math	—	1427

Knowledge Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.5: 35.5 (#172)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-4.5
Humanity's Last Exam	—	8.3%
Confabulations	—	11.3%
LMArena Expert	—	1433

Multilingual Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.5: 53.0 (#74)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-4.5
LMArena Non-English	—	1417
LMArena Chinese	—	1465
LMArena French	—	1423
LMArena German	—	1406
LMArena Japanese	—	1416
LMArena Korean	—	1382
LMArena Russian	—	1414
LMArena Spanish	—	1454

Instruction Following Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.5: 74.3 (#100)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-4.5
LMArena Instruction Following	—	1405

Long Context Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.5: 38.2 (#196)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-4.5
Fiction.LiveBench	—	58.3%
LMArena Longer Query	—	1413

Writing & Preference GLM-4.5 leads

DeepSeek-V3.2-Speciale: 46.1 (#217), GLM-4.5: 57.7 (#121)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-4.5
EQ-Bench Creative Writing	1276	1343
LMArena Text	—	1430
LMArena Creative Writing	—	1395
Short-Story Creative Writing	—	73.4%
LMArena Multi-Turn	—	1415

Frequently asked questions

Is DeepSeek-V3.2-Speciale better than GLM-4.5?

GLM-4.5 is the stronger model overall, scoring 41.4 to 39.1 on the Noometry Index.

Which is cheaper, DeepSeek-V3.2-Speciale or GLM-4.5?

DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.

Is DeepSeek-V3.2-Speciale or GLM-4.5 better for coding?

GLM-4.5 scores higher on coding benchmarks: 41.1 versus 39.9 in the Noometry coding category.

Which has the bigger context window?

GLM-4.5 does, with 131K tokens against 128K.

How many benchmarks do DeepSeek-V3.2-Speciale and GLM-4.5 share?

2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GLM-4.5 has 27.