Model comparison

DeepSeek-V3 vs GLM-4.6V

GLM-4.6V is the stronger model overall, scoring 40.9 to 39.2 on the Noometry Index.

Last verified October 10, 2026. 11 shared benchmarks.

DeepSeek-V3

DeepSeek

39.2

Rank #157 Confirmed

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Summary

They share 11 benchmarks with published results for both. DeepSeek-V3 scores higher in 4 categories and GLM-4.6V in 3 categories; 4 gaps are clear of the uncertainty.
The widest gap is in long context, where GLM-4.6V leads 41.3 to 34.0.
DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
DeepSeek-V3 accepts more context: 164K tokens versus 128K.

Side by side

DeepSeek-V3 and GLM-4.6V specifications
	DeepSeek-V3	GLM-4.6V
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	39.2	40.9
Released	2024-12-26	2025-12-08
Weights	Open	Open
Context window	164K	128K
Max output	164K	33K
Input $ / M tokens	$0.24	$0.30
Output $ / M tokens	$0.90	$0.90
Results tracked	60	12

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Category by category

Coding DeepSeek-V3 leads

DeepSeek-V3: 41.9 (#99), GLM-4.6V: 40.2 (#128)

Coding benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
LMArena Coding	1368	1390
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-4.6V: —

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
METR Time Horizons	49.6%	—

Reasoning GLM-4.6V leads

DeepSeek-V3: 20.0 (#226), GLM-4.6V: 26.5 (#117)

Reasoning benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
LMArena Hard Prompts	1365	1368
SimpleBench	27.2%	—
Kagi LLM Benchmark	52.3%	—
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	135.95	—
ForecastBench	59.1	—
HellaSwag	88.9%	—
LiveBench	66.9%	—
PIQA	84.7%	—
WinoGrande	85.2%	—

Math Not comparable

DeepSeek-V3: 30.4 (#208), GLM-4.6V: —

Math benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
OTIS Mock AIME 2024-2025	37.8%	—
Omni-MATH	40.3%	—
LiveBench Math	73.5%	—
LMArena Math	1373	—
MATH Level 5	75.5%	—
FrontierMath (Feb 2025 set)	1.7%	—

Knowledge Too close to call

DeepSeek-V3: 37.5 (#147), GLM-4.6V: 37.4 (#148)

Knowledge benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
LMArena Expert	1352	1372
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-4.6V: 34.2 (#83)

Multimodal benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
LMArena Vision	—	1164

Multilingual Too close to call

DeepSeek-V3: 48.8 (#138), GLM-4.6V: 48.8 (#135)

Multilingual benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
LMArena Non-English	1358	1359
LMArena Chinese	1390	1426
LMArena Russian	1372	1341
LMArena French	1386	—
LMArena German	1374	—
LMArena Japanese	1333	—
LMArena Korean	1321	—
LMArena Spanish	1358	—

Instruction Following DeepSeek-V3 leads

DeepSeek-V3: 73.0 (#125), GLM-4.6V: 71.6 (#145)

Instruction Following benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
LMArena Instruction Following	1345	1351
LiveBench Instruction Following	81.5%	—
IFEval	83.2%	—

Long Context GLM-4.6V leads

DeepSeek-V3: 34.0 (#248), GLM-4.6V: 41.3 (#138)

Long Context benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
LMArena Longer Query	1353	1358
Fiction.LiveBench	50%	—

Writing & Preference Too close to call

DeepSeek-V3: 57.6 (#124), GLM-4.6V: 56.8 (#132)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3	GLM-4.6V
LMArena Text	1375	1377
LMArena Creative Writing	1364	1347
LMArena Multi-Turn	1389	1359
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-4.6V?

GLM-4.6V is the stronger model overall, scoring 40.9 to 39.2 on the Noometry Index.

Which is cheaper, DeepSeek-V3 or GLM-4.6V?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

Is DeepSeek-V3 or GLM-4.6V better for coding?

DeepSeek-V3 scores higher on coding benchmarks: 41.9 versus 40.2 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3 does, with 164K tokens against 128K.

How many benchmarks do DeepSeek-V3 and GLM-4.6V share?

11 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and GLM-4.6V has 12.