Model comparison

DeepSeek-V3.1-Terminus vs GLM-5.2

GLM-5.2 is the stronger model overall, scoring 51.1 to 43.1 on the Noometry Index. DeepSeek-V3.1-Terminus costs 4.8× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

Last verified October 11, 2026. 16 shared benchmarks.

DeepSeek-V3.1-Terminus

DeepSeek

43.1

Rank #97 Confirmed

GLM-5.2 Z.ai (Zhipu)

51.1

Rank #44 Confirmed

Summary

They share 16 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 0 categories and GLM-5.2 in 7 categories; 7 gaps are clear of the uncertainty.
The widest gap is in math, where GLM-5.2 leads 55.7 to 38.5.
The biggest single-benchmark swing is CritPt: 1.7% for DeepSeek-V3.1-Terminus and 20.9% for GLM-5.2.
DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
GLM-5.2 accepts more context: 1M tokens versus 164K.

Side by side

DeepSeek-V3.1-Terminus and GLM-5.2 specifications
	DeepSeek-V3.1-Terminus	GLM-5.2
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	43.1	51.1
Released	2025-09-22	2026-06-13
Weights	Open	Open
Context window	164K	1M
Max output	147K	131K
Input $ / M tokens	$0.27	$1.40
Output $ / M tokens	$1	$4.40
Results tracked	16	51

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Category by category

Coding GLM-5.2 leads

DeepSeek-V3.1-Terminus: 42.0 (#113), GLM-5.2: 51.3 (#41)

Coding benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-5.2
SciCode	40.6%	50.5%
LMArena Coding	1426	1485
ALE-Bench	745.17	1,047
SWE-bench Verified	—	78.7%
DeepSWE	—	43.8%
FrontierCode	—	24.5%
LMArena WebDev	—	1603
WeirdML	—	70.1%

Agentic & Tool Use Not comparable

DeepSeek-V3.1-Terminus: —, GLM-5.2: 32.4 (#63)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.1-Terminus	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-V3.1-Terminus: 26.4 (#133), GLM-5.2: 42.3 (#52)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-5.2
Kagi LLM Benchmark	57.4%	62.6%
CritPt	1.7%	20.9%
LMArena Hard Prompts	1426	1480
DTBench	81.3%	93.6%
LMCA	28.6%	45.8%
ARC-AGI-2	—	22.8%
SimpleBench	—	58.8%
NYT Connections (extended)	—	74.3%
ARC-AGI-1	—	77%
Chess Puzzles	—	21%
EBR-Bench	—	9.5%
Mystery Game Puzzles	—	19%
Surface Evolver Bench	—	55.6%
Epoch Capabilities Index	—	151.78

Math GLM-5.2 leads

DeepSeek-V3.1-Terminus: 38.5 (#137), GLM-5.2: 55.7 (#43)

Math benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-5.2
LMArena Math	1402	1482
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%

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, GLM-5.2: 57.1 (#40)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-5.2
GPQA Diamond	—	91.9%
SimpleQA Verified	—	34.2%
LMArena Expert	—	1486

Multilingual GLM-5.2 leads

DeepSeek-V3.1-Terminus: 52.1 (#92), GLM-5.2: 55.8 (#26)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-5.2
LMArena Non-English	1407	1459
LMArena Russian	1436	1466
LMArena Chinese	—	1519
LMArena French	—	1479
LMArena German	—	1468
LMArena Japanese	—	1451
LMArena Korean	—	1445
LMArena Spanish	—	1477

Instruction Following GLM-5.2 leads

DeepSeek-V3.1-Terminus: 74.0 (#106), GLM-5.2: 76.9 (#34)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-5.2
LMArena Instruction Following	1404	1465

Long Context GLM-5.2 leads

DeepSeek-V3.1-Terminus: 43.4 (#97), GLM-5.2: 45.3 (#43)

Long Context benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-5.2
LMArena Longer Query	1421	1479

Writing & Preference GLM-5.2 leads

DeepSeek-V3.1-Terminus: 61.0 (#92), GLM-5.2: 70.4 (#21)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-5.2
LMArena Text	1419	1470
LMArena Creative Writing	1403	1462
LMArena Multi-Turn	1411	1469
EQ-Bench Creative Writing	—	1757
EQ-Bench 4	—	1222

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than GLM-5.2?

Which is cheaper, DeepSeek-V3.1-Terminus or GLM-5.2?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

Is DeepSeek-V3.1-Terminus or GLM-5.2 better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 42.0 in the Noometry coding category.

Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 164K.

How many benchmarks do DeepSeek-V3.1-Terminus and GLM-5.2 share?

16 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GLM-5.2 has 51.