Model comparison

DeepSeek-R1 vs GLM-4.6V

DeepSeek-R1 is the stronger model overall, scoring 42.3 to 41.3 on the Noometry Index. GLM-4.6V costs 2.0× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.

Last verified October 10, 2026. 11 shared benchmarks.

DeepSeek-R1

DeepSeek

42.3

Rank #115 Confirmed

GLM-4.6V Z.ai (Zhipu)

41.3

Rank #137 Confirmed

Summary

They share 11 benchmarks with published results for both. DeepSeek-R1 scores higher in 6 categories and GLM-4.6V in 1 category; 6 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GLM-4.6V leads 27.6 to 18.6.
GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
DeepSeek-R1 accepts more context: 164K tokens versus 128K.
GLM-4.6V has downloadable open weights; the other is API-only.

Side by side

DeepSeek-R1 and GLM-4.6V specifications
	DeepSeek-R1	GLM-4.6V
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	42.3	41.3
Released	2025-01-20	2025-12-08
Weights	Proprietary	Open
Context window	164K	128K
Max output	64K	33K
Input $ / M tokens	$0.50	$0.30
Output $ / M tokens	$2.15	$0.90
Results tracked	52	12

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Category by category

Coding DeepSeek-R1 leads

DeepSeek-R1: 46.3 (#68), GLM-4.6V: 40.9 (#128)

Coding benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
LMArena Coding	1427	1390
Aider Polyglot	71.4%	—
SciCode	35.7%	—
WeirdML	41.6%	—
LiveBench Coding	66.7%	—
ALE-Bench	804.12	—
AlgoTune	1.7	—

Agentic & Tool Use Not comparable

DeepSeek-R1: 30.7 (#75), GLM-4.6V: —

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
DeepResearch Bench	35.1%	—
BALROG	34.9%	—
METR Time Horizons	53.8%	—

Reasoning GLM-4.6V leads

DeepSeek-R1: 18.6 (#278), GLM-4.6V: 27.6 (#115)

Reasoning benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
LMArena Hard Prompts	1416	1368
ARC-AGI-2	1.3%	—
SimpleBench	40.8%	—
Kagi LLM Benchmark	69.4%	—
ARC-AGI-1	21.2%	—
CritPt	1.1%	—
LiveBench Reasoning	83.2%	—
LiveBench Data Analysis	69.8%	—
Epoch Capabilities Index	141.29	—
ForecastBench	60	—
LiveBench	71.6%	—

Math Not comparable

DeepSeek-R1: 43.8 (#79), GLM-4.6V: —

Math benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
OTIS Mock AIME 2024-2025	66.4%	—
Omni-MATH	42.4%	—
LiveBench Math	80.7%	—
LMArena Math	1400	—
MATH Level 5	96.6%	—

Knowledge DeepSeek-R1 leads

DeepSeek-R1: 44.5 (#87), GLM-4.6V: 38.0 (#149)

Knowledge benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
LMArena Expert	1394	1371
GPQA Diamond	76.3%	—
MMLU-Pro	79.3%	—
Confabulations	12.7%	—
Vectara Hallucination Rate	11.3%	—
GPQA (HELM)	66.6%	—

Multimodal Not comparable

DeepSeek-R1: —, GLM-4.6V: 34.8 (#90)

Multimodal benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
LMArena Vision	—	1164

Multilingual DeepSeek-R1 leads

DeepSeek-R1: 52.4 (#85), GLM-4.6V: 48.6 (#141)

Multilingual benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
LMArena Non-English	1412	1359
LMArena Chinese	1442	1425
LMArena Russian	1423	1340
LMArena French	1417	—
LMArena German	1404	—
LMArena Japanese	1391	—
LMArena Korean	1360	—
LMArena Spanish	1411	—

Instruction Following Too close to call

DeepSeek-R1: 72.0 (#143), GLM-4.6V: 71.4 (#151)

Instruction Following benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
LMArena Instruction Following	1382	1352
LiveBench Instruction Following	80.5%	—
IFEval	78.4%	—

Long Context DeepSeek-R1 leads

DeepSeek-R1: 45.4 (#36), GLM-4.6V: 41.3 (#143)

Long Context benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
LMArena Longer Query	1391	1358
Fiction.LiveBench	75%	—

Writing & Preference DeepSeek-R1 leads

DeepSeek-R1: 61.4 (#88), GLM-4.6V: 56.6 (#137)

Writing & Preference benchmarks
Benchmark	DeepSeek-R1	GLM-4.6V
LMArena Text	1428	1377
LMArena Creative Writing	1405	1347
LMArena Multi-Turn	1405	1360
Short-Story Creative Writing	83%	—
EQ-Bench Creative Writing	1500	—
WildBench	82.8%	—
LiveBench Language	48.5%	—

Frequently asked questions

Is DeepSeek-R1 better than GLM-4.6V?

Which is cheaper, DeepSeek-R1 or GLM-4.6V?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.

Is DeepSeek-R1 or GLM-4.6V better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 40.9 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-R1 does, with 164K tokens against 128K.

How many benchmarks do DeepSeek-R1 and GLM-4.6V share?

11 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GLM-4.6V has 12.