Model comparison

DeepSeek-V3.2-Speciale vs GLM-5.2

GLM-5.2 is the stronger model overall, scoring 51.1 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.5× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

Last verified October 10, 2026. 3 shared benchmarks.

DeepSeek-V3.2-Speciale

DeepSeek

39.7

Rank #162 Reported

GLM-5.2 Z.ai (Zhipu)

51.1

Rank #44 Confirmed

Summary

They share 3 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 0 categories and GLM-5.2 in 3 categories; 3 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GLM-5.2 leads 70.4 to 46.0.
The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 70.1% for GLM-5.2.
DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
GLM-5.2 accepts more context: 1M tokens versus 128K.

Side by side

DeepSeek-V3.2-Speciale and GLM-5.2 specifications
	DeepSeek-V3.2-Speciale	GLM-5.2
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	39.7	51.1
Released	2025-12-01	2026-06-13
Weights	Open	Open
Context window	128K	1M
Max output	128K	131K
Input $ / M tokens	$0.58	$1.40
Output $ / M tokens	$1.68	$4.40
Results tracked	3	51

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding GLM-5.2 leads

DeepSeek-V3.2-Speciale: 40.4 (#140), GLM-5.2: 51.3 (#41)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
WeirdML	46.7%	70.1%
SWE-bench Verified	—	78.7%
DeepSWE	—	43.8%
FrontierCode	—	24.5%
LMArena WebDev	—	1603
SciCode	—	50.5%
LMArena Coding	—	1485
ALE-Bench	—	1,047

Agentic & Tool Use Not comparable

DeepSeek-V3.2-Speciale: —, GLM-5.2: 32.4 (#63)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
APEX-Agents	—	45.2%
τ²-bench Banking	—	37.1%
PostTrainBench	—	31.7%
GBAEval	—	0%
Vending-Bench 2	—	8,314

Reasoning GLM-5.2 leads

DeepSeek-V3.2-Speciale: 32.9 (#73), GLM-5.2: 42.3 (#52)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
SimpleBench	52.6%	58.8%
ARC-AGI-2	—	22.8%
Kagi LLM Benchmark	—	62.6%
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

DeepSeek-V3.2-Speciale: —, GLM-5.2: 55.7 (#43)

Math benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
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 Not comparable

DeepSeek-V3.2-Speciale: —, GLM-5.2: 57.1 (#40)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
GPQA Diamond	—	91.9%
SimpleQA Verified	—	34.2%
LMArena Expert	—	1486

Multilingual Not comparable

DeepSeek-V3.2-Speciale: —, GLM-5.2: 55.8 (#26)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
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

DeepSeek-V3.2-Speciale: —, GLM-5.2: 76.9 (#34)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
LMArena Instruction Following	—	1465

Long Context Not comparable

DeepSeek-V3.2-Speciale: —, GLM-5.2: 45.3 (#43)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
LMArena Longer Query	—	1479

Writing & Preference GLM-5.2 leads

DeepSeek-V3.2-Speciale: 46.0 (#222), GLM-5.2: 70.4 (#21)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Speciale	GLM-5.2
EQ-Bench Creative Writing	1276	1757
LMArena Text	—	1470
LMArena Creative Writing	—	1462
EQ-Bench 4	—	1222
LMArena Multi-Turn	—	1469

Frequently asked questions

Is DeepSeek-V3.2-Speciale better than GLM-5.2?

Which is cheaper, DeepSeek-V3.2-Speciale or GLM-5.2?

DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

Is DeepSeek-V3.2-Speciale or GLM-5.2 better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 40.4 in the Noometry coding category.

Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 128K.

How many benchmarks do DeepSeek-V3.2-Speciale and GLM-5.2 share?

3 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GLM-5.2 has 51.