Model comparison

DeepSeek-V3.1 vs GLM-4.7-Flash

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 2.9× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

Last verified October 10, 2026. 18 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Summary

They share 18 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 6 categories and GLM-4.7-Flash in 2 categories; 7 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 47.4.
GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
GLM-4.7-Flash accepts more context: 200K tokens versus 164K.

Side by side

DeepSeek-V3.1 and GLM-4.7-Flash specifications
	DeepSeek-V3.1	GLM-4.7-Flash
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	42.8	38.8
Released	2025-08-21	2026-01-19
Weights	Open	Open
Context window	164K	200K
Max output	8K	131K
Input $ / M tokens	$0.25	$0.06
Output $ / M tokens	$0.95	$0.40
Results tracked	27	21

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

Category by category

Coding Too close to call

DeepSeek-V3.1: 40.3 (#144), GLM-4.7-Flash: 40.6 (#135)

Coding benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.7-Flash
LMArena Coding	1417	1383
WeirdML	38.4%	—

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), GLM-4.7-Flash: 20.9 (#229)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.7-Flash
LMArena Hard Prompts	1417	1356
SimpleBench	40%	—
Kagi LLM Benchmark	53.2%	—
Chess Puzzles	—	0%
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-4.7-Flash: 36.1 (#173)

Math benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.7-Flash
LMArena Math	1420	1355
OTIS Mock AIME 2024-2025	—	58.3%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), GLM-4.7-Flash: 35.5 (#184)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.7-Flash
Vectara Hallucination Rate	5.5%	9.3%
LMArena Expert	1405	1357
GPQA Diamond	—	60.5%

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), GLM-4.7-Flash: 46.5 (#158)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.7-Flash
LMArena Non-English	1400	1330
LMArena Chinese	1469	1403
LMArena French	1447	1332
LMArena German	1411	1337
LMArena Korean	1337	1283
LMArena Russian	1405	1332
LMArena Spanish	1431	1350
LMArena Japanese	1378	—

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), GLM-4.7-Flash: 70.1 (#167)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.7-Flash
LMArena Instruction Following	1400	1327

Long Context GLM-4.7-Flash leads

DeepSeek-V3.1: 36.3 (#232), GLM-4.7-Flash: 40.9 (#148)

Long Context benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.7-Flash
LMArena Longer Query	1422	1345
Fiction.LiveBench	52.8%	—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), GLM-4.7-Flash: 47.4 (#210)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	GLM-4.7-Flash
LMArena Text	1420	1351
LMArena Creative Writing	1401	1297
EQ-Bench Creative Writing	1436	1125
LMArena Multi-Turn	1408	1342

Frequently asked questions

Is DeepSeek-V3.1 better than GLM-4.7-Flash?

Which is cheaper, DeepSeek-V3.1 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-V3.1 lists at $0.25 and $0.95.

Is DeepSeek-V3.1 or GLM-4.7-Flash better for coding?

They score almost the same on coding (40.3 vs 40.6); test both on your own repository before choosing.

Which has the bigger context window?

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

How many benchmarks do DeepSeek-V3.1 and GLM-4.7-Flash share?

18 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GLM-4.7-Flash has 21.