Model comparison

DeepSeek-V3.2-Exp vs GLM-4.7-Flash

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 2.0× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

Last verified October 10, 2026. 21 shared benchmarks.

DeepSeek-V3.2-Exp

DeepSeek

44.3

Rank #78 Confirmed

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Summary

They share 21 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and GLM-4.7-Flash in 0 categories; 8 gaps are clear of the uncertainty.
The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 35.5.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 58.3% for GLM-4.7-Flash.
GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
GLM-4.7-Flash accepts more context: 200K tokens versus 164K.

Side by side

DeepSeek-V3.2-Exp and GLM-4.7-Flash specifications
	DeepSeek-V3.2-Exp	GLM-4.7-Flash
Provider	DeepSeek	Z.ai (Zhipu)
Noometry Index	44.3	38.8
Released	2025-09-29	2026-01-19
Weights	Open	Open
Context window	164K	200K
Max output	66K	131K
Input $ / M tokens	$0.26	$0.06
Output $ / M tokens	$0.38	$0.40
Results tracked	49	21

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Category by category

Coding DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 46.5 (#65), GLM-4.7-Flash: 40.6 (#135)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
LMArena Coding	1454	1383
SWE-bench Verified (bash only)	70%	—
Aider Polyglot	74.2%	—
LMArena WebDev	1362	—
SWE-bench Multilingual	59%	—
SciCode	38.9%	—
WeirdML	39.5%	—

Agentic & Tool Use Not comparable

DeepSeek-V3.2-Exp: 32.7 (#59), GLM-4.7-Flash: —

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
Terminal-Bench	39.6%	—
APEX-Agents	21.3%	—
Berkeley Function Calling Leaderboard	56.7%	—
TheAgentCompany	42.9%	—
Vending-Bench 2	1,034	—

Reasoning DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 22.1 (#208), GLM-4.7-Flash: 20.9 (#229)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
Chess Puzzles	14%	0%
LMArena Hard Prompts	1434	1356
ARC-AGI-2	4%	—
Kagi LLM Benchmark	52.2%	—
NYT Connections (extended)	36.7%	—
ARC-AGI-1	57%	—
CritPt	2.9%	—
Thematic Generalization	65%	—
DTBench	87.7%	—
LMCA	29.1%	—
Epoch Capabilities Index	146.27	—

Math DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 41.7 (#87), GLM-4.7-Flash: 36.1 (#173)

Math benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
OTIS Mock AIME 2024-2025	87.8%	58.3%
LMArena Math	1435	1355
MathArena Final-Answer Competitions	57.7%	—
ProofBench	8%	—
FrontierMath (Feb 2025 set)	22.1%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 51.7 (#66), GLM-4.7-Flash: 35.5 (#184)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
GPQA Diamond	83.4%	60.5%
Vectara Hallucination Rate	5.3%	9.3%
LMArena Expert	1436	1357

Multilingual DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 52.2 (#90), GLM-4.7-Flash: 46.5 (#158)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
LMArena Non-English	1409	1330
LMArena Chinese	1461	1403
LMArena French	1433	1332
LMArena German	1440	1337
LMArena Korean	1371	1283
LMArena Russian	1424	1332
LMArena Spanish	1440	1350
LMArena Japanese	1374	—

Instruction Following DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 74.5 (#93), GLM-4.7-Flash: 70.1 (#167)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
LMArena Instruction Following	1413	1327

Long Context DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 47.6 (#16), GLM-4.7-Flash: 40.9 (#148)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
LMArena Longer Query	1428	1345
Fiction.LiveBench	83.3%	—
CL-bench	13.2%	—
CL-bench Life	9.5%	—

Writing & Preference DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 62.4 (#77), GLM-4.7-Flash: 47.4 (#210)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Exp	GLM-4.7-Flash
LMArena Text	1425	1351
LMArena Creative Writing	1403	1297
EQ-Bench Creative Writing	1515	1125
LMArena Multi-Turn	1427	1342

Frequently asked questions

Is DeepSeek-V3.2-Exp better than GLM-4.7-Flash?

Which is cheaper, DeepSeek-V3.2-Exp 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-V3.2-Exp lists at $0.26 and $0.38.

Is DeepSeek-V3.2-Exp or GLM-4.7-Flash better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 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-V3.2-Exp and GLM-4.7-Flash share?

21 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GLM-4.7-Flash has 21.