Model comparison

DeepSeek-R1 vs GLM-4.7-Flash

DeepSeek-R1 is the stronger model overall, scoring 42.3 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 6.3× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.

Last verified October 11, 2026. 20 shared benchmarks.

DeepSeek-R1

DeepSeek

42.3

Rank #115 Confirmed

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Summary

They share 20 benchmarks with published results for both. DeepSeek-R1 scores higher in 7 categories and GLM-4.7-Flash in 1 category; 8 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where DeepSeek-R1 leads 61.4 to 47.4.
The biggest single-benchmark swing is GPQA Diamond: 76.3% for DeepSeek-R1 and 60.5% for GLM-4.7-Flash.
GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
GLM-4.7-Flash accepts more context: 200K tokens versus 164K.
GLM-4.7-Flash has downloadable open weights; the other is API-only.

Side by side

DeepSeek-R1 and GLM-4.7-Flash specifications
	DeepSeek-R1	GLM-4.7-Flash
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	42.3	38.8
Released	2025-01-20	2026-01-19
Weights	Proprietary	Open
Context window	164K	200K
Max output	64K	131K
Input $ / M tokens	$0.50	$0.06
Output $ / M tokens	$2.15	$0.40
Results tracked	52	21

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Category by category

Coding DeepSeek-R1 leads

DeepSeek-R1: 46.3 (#68), GLM-4.7-Flash: 40.6 (#135)

Coding benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
LMArena Coding	1427	1383
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-4.7-Flash: —

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
DeepResearch Bench	35.1%	—
BALROG	34.9%	—
METR Time Horizons	53.8%	—

Reasoning GLM-4.7-Flash leads

DeepSeek-R1: 18.6 (#278), GLM-4.7-Flash: 20.9 (#229)

Reasoning benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
LMArena Hard Prompts	1416	1356
ARC-AGI-2	1.3%	—
SimpleBench	40.8%	—
Kagi LLM Benchmark	69.4%	—
ARC-AGI-1	21.2%	—
CritPt	1.1%	—
Chess Puzzles	—	0%
LiveBench Reasoning	83.2%	—
LiveBench Data Analysis	69.8%	—
Epoch Capabilities Index	141.29	—
ForecastBench	60	—
LiveBench	71.6%	—

Math DeepSeek-R1 leads

DeepSeek-R1: 43.8 (#79), GLM-4.7-Flash: 36.1 (#173)

Math benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
OTIS Mock AIME 2024-2025	66.4%	58.3%
LMArena Math	1400	1355
Omni-MATH	42.4%	—
LiveBench Math	80.7%	—
MATH Level 5	96.6%	—

Knowledge DeepSeek-R1 leads

DeepSeek-R1: 44.5 (#87), GLM-4.7-Flash: 35.5 (#184)

Knowledge benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
GPQA Diamond	76.3%	60.5%
Vectara Hallucination Rate	11.3%	9.3%
LMArena Expert	1394	1357
MMLU-Pro	79.3%	—
Confabulations	12.7%	—
GPQA (HELM)	66.6%	—

Multilingual DeepSeek-R1 leads

DeepSeek-R1: 52.4 (#85), GLM-4.7-Flash: 46.5 (#158)

Multilingual benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
LMArena Non-English	1412	1330
LMArena Chinese	1442	1403
LMArena French	1417	1332
LMArena German	1404	1337
LMArena Korean	1360	1283
LMArena Russian	1423	1332
LMArena Spanish	1411	1350
LMArena Japanese	1391	—

Instruction Following DeepSeek-R1 leads

DeepSeek-R1: 72.0 (#143), GLM-4.7-Flash: 70.1 (#167)

Instruction Following benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
LMArena Instruction Following	1382	1327
LiveBench Instruction Following	80.5%	—
IFEval	78.4%	—

Long Context DeepSeek-R1 leads

DeepSeek-R1: 45.4 (#36), GLM-4.7-Flash: 40.9 (#148)

Long Context benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
LMArena Longer Query	1391	1345
Fiction.LiveBench	75%	—

Writing & Preference DeepSeek-R1 leads

DeepSeek-R1: 61.4 (#88), GLM-4.7-Flash: 47.4 (#210)

Writing & Preference benchmarks
Benchmark	DeepSeek-R1	GLM-4.7-Flash
LMArena Text	1428	1351
LMArena Creative Writing	1405	1297
EQ-Bench Creative Writing	1500	1125
LMArena Multi-Turn	1405	1342
Short-Story Creative Writing	83%	—
WildBench	82.8%	—
LiveBench Language	48.5%	—

Frequently asked questions

Is DeepSeek-R1 better than GLM-4.7-Flash?

Which is cheaper, DeepSeek-R1 or GLM-4.7-Flash?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.

Is DeepSeek-R1 or GLM-4.7-Flash better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 40.6 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 164K.

How many benchmarks do DeepSeek-R1 and GLM-4.7-Flash share?

20 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GLM-4.7-Flash has 21.