Model comparison

DeepSeek-V3 vs GLM-4.5V

DeepSeek-V3 and GLM-4.5V score almost the same on the Noometry Index (39.5 vs 39.8), so choose on price, context window or the category you care about most.

Last verified October 10, 2026. 14 shared benchmarks.

DeepSeek-V3

DeepSeek

39.5

Rank #166 Confirmed

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Summary

They share 14 benchmarks with published results for both. DeepSeek-V3 scores higher in 5 categories and GLM-4.5V in 3 categories; 7 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GLM-4.5V leads 27.4 to 20.5.
The biggest single-benchmark swing is Kagi LLM Benchmark: 52.3% for DeepSeek-V3 and 59.8% for GLM-4.5V.
DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
DeepSeek-V3 accepts more context: 164K tokens versus 64K.

Side by side

DeepSeek-V3 and GLM-4.5V specifications
	DeepSeek-V3	GLM-4.5V
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	39.5	39.8
Released	2024-12-26	2025-08-11
Weights	Open	Open
Context window	164K	64K
Max output	164K	16K
Input $ / M tokens	$0.24	$0.60
Output $ / M tokens	$0.90	$1.80
Results tracked	60	15

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

Category by category

Coding DeepSeek-V3 leads

DeepSeek-V3: 42.3 (#106), GLM-4.5V: 39.5 (#155)

Coding benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
LMArena Coding	1368	1347
Aider Polyglot	55.1%	—
SciCode	35.8%	—
WeirdML	36.1%	—
BigCodeBench Instruct	50%	—
LiveBench Coding	70.9%	—
BigCodeBench Complete	62.2%	—
HumanEval+	86.6%	—
MBPP+	73%	—

Agentic & Tool Use Not comparable

DeepSeek-V3: —, GLM-4.5V: —

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
METR Time Horizons	49.6%	—

Reasoning GLM-4.5V leads

DeepSeek-V3: 20.5 (#236), GLM-4.5V: 27.4 (#119)

Reasoning benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
Kagi LLM Benchmark	52.3%	59.8%
LMArena Hard Prompts	1365	1334
SimpleBench	27.2%	—
CritPt	0%	—
LiveBench Reasoning	65.8%	—
DTBench	64.8%	—
LiveBench Data Analysis	60.9%	—
LMCA	15.5%	—
BIG-Bench Hard	87.5%	—
Epoch Capabilities Index	135.94	—
ForecastBench	59.1	—
HellaSwag	88.9%	—
LiveBench	66.9%	—
PIQA	84.7%	—
WinoGrande	85.2%	—

Math GLM-4.5V leads

DeepSeek-V3: 32.1 (#219), GLM-4.5V: 37.4 (#159)

Math benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
LMArena Math	1373	1354
OTIS Mock AIME 2024-2025	37.8%	—
Omni-MATH	40.3%	—
LiveBench Math	73.5%	—
MATH Level 5	75.5%	—
FrontierMath (Feb 2025 set)	1.7%	—

Knowledge Too close to call

DeepSeek-V3: 37.5 (#155), GLM-4.5V: 37.5 (#156)

Knowledge benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
LMArena Expert	1351	1353
GPQA Diamond	67.6%	—
MMLU-Pro	72.3%	—
Confabulations	26.1%	—
Vectara Hallucination Rate	6.1%	—
GPQA (HELM)	53.8%	—
ARC (AI2) Challenge	95.3%	—
MMLU	87.2%	—
TriviaQA	82.9%	—

Multimodal Not comparable

DeepSeek-V3: —, GLM-4.5V: 34.3 (#92)

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

Multilingual DeepSeek-V3 leads

DeepSeek-V3: 48.5 (#143), GLM-4.5V: 44.6 (#177)

Multilingual benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
LMArena Non-English	1358	1303
LMArena Chinese	1391	1337
LMArena Russian	1373	1298
LMArena Spanish	1358	1336
LMArena French	1385	—
LMArena German	1374	—
LMArena Japanese	1333	—
LMArena Korean	1319	—

Instruction Following DeepSeek-V3 leads

DeepSeek-V3: 72.8 (#130), GLM-4.5V: 69.2 (#175)

Instruction Following benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
LMArena Instruction Following	1345	1311
LiveBench Instruction Following	81.5%	—
IFEval	83.2%	—

Long Context GLM-4.5V leads

DeepSeek-V3: 34.0 (#253), GLM-4.5V: 39.6 (#171)

Long Context benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
LMArena Longer Query	1352	1304
Fiction.LiveBench	50%	—

Writing & Preference DeepSeek-V3 leads

DeepSeek-V3: 57.4 (#130), GLM-4.5V: 52.5 (#170)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3	GLM-4.5V
LMArena Text	1375	1333
LMArena Creative Writing	1364	1295
LMArena Multi-Turn	1389	1332
Short-Story Creative Writing	77%	—
EQ-Bench Creative Writing	1472	—
WildBench	83%	—
LiveBench Language	49.1%	—

Frequently asked questions

Is DeepSeek-V3 better than GLM-4.5V?

DeepSeek-V3 and GLM-4.5V score almost the same on the Noometry Index (39.5 vs 39.8), so choose on price, context window or the category you care about most.

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

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

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

DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 39.5 in the Noometry coding category.

Which has the bigger context window?

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

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

14 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and GLM-4.5V has 15.