Model comparison

DeepSeek-V3.1 vs GLM-4.5V

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.8 on the Noometry Index.

Last verified October 10, 2026. 14 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Summary

They share 14 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and GLM-4.5V in 1 category; 6 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 52.5.
The biggest single-benchmark swing is Kagi LLM Benchmark: 53.2% for DeepSeek-V3.1 and 59.8% for GLM-4.5V.
DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
DeepSeek-V3.1 accepts more context: 164K tokens versus 64K.

Side by side

DeepSeek-V3.1 and GLM-4.5V specifications
	DeepSeek-V3.1	GLM-4.5V
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	42.8	39.8
Released	2025-08-21	2025-08-11
Weights	Open	Open
Context window	164K	64K
Max output	8K	16K
Input $ / M tokens	$0.25	$0.60
Output $ / M tokens	$0.95	$1.80
Results tracked	27	15

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Category by category

Coding Too close to call

DeepSeek-V3.1: 40.3 (#144), GLM-4.5V: 39.5 (#155)

Coding benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
LMArena Coding	1417	1347
WeirdML	38.4%	—

Reasoning Too close to call

DeepSeek-V3.1: 27.9 (#110), GLM-4.5V: 27.4 (#119)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
Kagi LLM Benchmark	53.2%	59.8%
LMArena Hard Prompts	1417	1334
SimpleBench	40%	—
DTBench	82.7%	—
LMCA	24.3%	—
Epoch Capabilities Index	139.92	—
ForecastBench	58	—

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), GLM-4.5V: 37.4 (#159)

Math benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
LMArena Math	1420	1354

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), GLM-4.5V: 37.5 (#156)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
LMArena Expert	1405	1353
Vectara Hallucination Rate	5.5%	—

Multimodal Not comparable

DeepSeek-V3.1: —, GLM-4.5V: 34.3 (#92)

Multimodal benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
LMArena Vision	—	1154

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), GLM-4.5V: 44.6 (#177)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
LMArena Non-English	1400	1303
LMArena Chinese	1469	1337
LMArena Russian	1405	1298
LMArena Spanish	1431	1336
LMArena French	1447	—
LMArena German	1411	—
LMArena Japanese	1378	—
LMArena Korean	1337	—

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), GLM-4.5V: 69.2 (#175)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
LMArena Instruction Following	1400	1311

Long Context GLM-4.5V leads

DeepSeek-V3.1: 36.3 (#232), GLM-4.5V: 39.6 (#171)

Long Context benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
LMArena Longer Query	1422	1304
Fiction.LiveBench	52.8%	—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), GLM-4.5V: 52.5 (#170)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.5V
LMArena Text	1420	1333
LMArena Creative Writing	1401	1295
LMArena Multi-Turn	1408	1332
EQ-Bench Creative Writing	1436	—

Frequently asked questions

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

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.8 on the Noometry Index.

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

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

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

They score almost the same on coding (40.3 vs 39.5); test both on your own repository before choosing.

Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 64K.

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

14 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GLM-4.5V has 15.