Noometry

Model comparison

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

DeepSeek-V3.2-Speciale and GLM-4.7-Flash score almost the same on the Noometry Index (39.1 vs 38.4), so choose on price, context window or the category you care about most.

Last verified . 1 shared benchmarks.

DeepSeek-V3.2-Speciale DeepSeek

39.1

Rank #159 Reported

GLM-4.7-Flash Z.ai (Zhipu)

38.4

Rank #175 Confirmed

Summary

  • They share 1 benchmark with published results for both. DeepSeek-V3.2-Speciale scores higher in 1 category and GLM-4.7-Flash in 2 categories; 2 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.1 to 20.2.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 128K.

Side by side

DeepSeek-V3.2-SpecialeGLM-4.7-Flash
ProviderDeepSeekZ.ai (Zhipu)
Noometry Index39.138.4
Released2025-12-012026-01-19
WeightsOpenOpen
Context window128K200K
Max output128K131K
Input $ / M tokens$0.58$0.06
Output $ / M tokens$1.68$0.40
Results tracked321

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Category by category

Coding Too close to call

DeepSeek-V3.2-Speciale: 39.9 (#136), GLM-4.7-Flash: 40.0 (#133)

BenchmarkDeepSeek-V3.2-SpecialeGLM-4.7-Flash
WeirdML46.7%—
LMArena Coding—1383

Reasoning DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 32.1 (#65), GLM-4.7-Flash: 20.2 (#224)

BenchmarkDeepSeek-V3.2-SpecialeGLM-4.7-Flash
SimpleBench52.6%—
Chess Puzzles—0%
LMArena Hard Prompts—1356

Math Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.7-Flash: 34.4 (#159)

BenchmarkDeepSeek-V3.2-SpecialeGLM-4.7-Flash
OTIS Mock AIME 2024-2025—58.3%
LMArena Math—1354

Knowledge Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.7-Flash: 35.4 (#177)

BenchmarkDeepSeek-V3.2-SpecialeGLM-4.7-Flash
GPQA Diamond—60.5%
Vectara Hallucination Rate—9.3%
LMArena Expert—1357

Multilingual Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.7-Flash: 46.8 (#152)

BenchmarkDeepSeek-V3.2-SpecialeGLM-4.7-Flash
LMArena Non-English—1331
LMArena Chinese—1403
LMArena French—1332
LMArena German—1336
LMArena Korean—1284
LMArena Russian—1333
LMArena Spanish—1351

Instruction Following Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.7-Flash: 70.3 (#162)

BenchmarkDeepSeek-V3.2-SpecialeGLM-4.7-Flash
LMArena Instruction Following—1327

Long Context Not comparable

DeepSeek-V3.2-Speciale: —, GLM-4.7-Flash: 40.9 (#143)

BenchmarkDeepSeek-V3.2-SpecialeGLM-4.7-Flash
LMArena Longer Query—1345

Writing & Preference GLM-4.7-Flash leads

DeepSeek-V3.2-Speciale: 46.1 (#217), GLM-4.7-Flash: 47.7 (#206)

BenchmarkDeepSeek-V3.2-SpecialeGLM-4.7-Flash
EQ-Bench Creative Writing12761125
LMArena Text—1351
LMArena Creative Writing—1297
LMArena Multi-Turn—1342

Frequently asked questions

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

DeepSeek-V3.2-Speciale and GLM-4.7-Flash score almost the same on the Noometry Index (39.1 vs 38.4), so choose on price, context window or the category you care about most.

Which is cheaper, DeepSeek-V3.2-Speciale 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-Speciale lists at $0.58 and $1.68.

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

They score almost the same on coding (39.9 vs 40.0); test both on your own repository before choosing.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 128K.

How many benchmarks do DeepSeek-V3.2-Speciale and GLM-4.7-Flash share?

1 benchmark has published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GLM-4.7-Flash has 21.

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