Model comparison

DeepSeek-V3.1-Terminus vs GLM-4.5

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 42.0 on the Noometry Index.

Last verified October 10, 2026. 12 shared benchmarks.

DeepSeek-V3.1-Terminus

DeepSeek

43.1

Rank #97 Confirmed

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 Confirmed

Summary

They share 12 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 3 categories and GLM-4.5 in 4 categories; 3 gaps are clear of the uncertainty.
The widest gap is in long context, where DeepSeek-V3.1-Terminus leads 43.4 to 38.2.
DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 131K.

Side by side

DeepSeek-V3.1-Terminus and GLM-4.5 specifications
	DeepSeek-V3.1-Terminus	GLM-4.5
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	43.1	42.0
Released	2025-09-22	2025-07-27
Weights	Open	Open
Context window	164K	131K
Max output	147K	98K
Input $ / M tokens	$0.27	$0.60
Output $ / M tokens	$1	$2.20
Results tracked	16	27

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Category by category

Coding Too close to call

DeepSeek-V3.1-Terminus: 42.0 (#113), GLM-4.5: 41.4 (#125)

Coding benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-4.5
LMArena Coding	1426	1434
ALE-Bench	745.17	344.82
SWE-bench Verified (bash only)	—	54.2%
SciCode	40.6%	—
WeirdML	—	40.6%
AlgoTune	—	1.52

Reasoning GLM-4.5 leads

DeepSeek-V3.1-Terminus: 26.4 (#133), GLM-4.5: 28.6 (#100)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-4.5
Kagi LLM Benchmark	57.4%	57.9%
LMArena Hard Prompts	1426	1429
CritPt	1.7%	—
DTBench	81.3%	—
LMCA	28.6%	—

Math Too close to call

DeepSeek-V3.1-Terminus: 38.5 (#137), GLM-4.5: 39.0 (#116)

Math benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-4.5
LMArena Math	1402	1427

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, GLM-4.5: 35.9 (#179)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-4.5
Humanity's Last Exam	—	8.3%
Confabulations	—	11.3%
LMArena Expert	—	1433

Multilingual Too close to call

DeepSeek-V3.1-Terminus: 52.1 (#92), GLM-4.5: 52.8 (#77)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-4.5
LMArena Non-English	1407	1417
LMArena Russian	1436	1414
LMArena Chinese	—	1465
LMArena French	—	1418
LMArena German	—	1407
LMArena Japanese	—	1415
LMArena Korean	—	1380
LMArena Spanish	—	1454

Instruction Following Too close to call

DeepSeek-V3.1-Terminus: 74.0 (#106), GLM-4.5: 74.1 (#104)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-4.5
LMArena Instruction Following	1404	1404

Long Context DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 43.4 (#97), GLM-4.5: 38.2 (#201)

Long Context benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-4.5
LMArena Longer Query	1421	1412
Fiction.LiveBench	—	58.3%

Writing & Preference DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 61.0 (#92), GLM-4.5: 57.5 (#127)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1-Terminus	GLM-4.5
LMArena Text	1419	1430
LMArena Creative Writing	1403	1395
LMArena Multi-Turn	1411	1415
Short-Story Creative Writing	—	73.4%
EQ-Bench Creative Writing	—	1343

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than GLM-4.5?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 42.0 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1-Terminus or GLM-4.5?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.

Is DeepSeek-V3.1-Terminus or GLM-4.5 better for coding?

They score almost the same on coding (42.0 vs 41.4); test both on your own repository before choosing.

Which has the bigger context window?

DeepSeek-V3.1-Terminus does, with 164K tokens against 131K.

How many benchmarks do DeepSeek-V3.1-Terminus and GLM-4.5 share?

12 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GLM-4.5 has 27.