Noometry

Model comparison

DeepSeek-V3.1-Terminus vs GLM-4.6

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 41.4 on the Noometry Index.

Last verified . 14 shared benchmarks.

DeepSeek-V3.1-Terminus DeepSeek

43.1

Rank #97 Confirmed

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Summary

  • They share 14 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 2 categories and GLM-4.6 in 5 categories; 3 gaps are clear of the uncertainty.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 57.4% for DeepSeek-V3.1-Terminus and 47.4% for GLM-4.6.
  • DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • GLM-4.6 accepts more context: 205K tokens versus 164K.

Side by side

DeepSeek-V3.1-TerminusGLM-4.6
ProviderDeepSeekZ.ai (Zhipu)
Noometry Index43.141.4
Released2025-09-222025-09-30
WeightsOpenOpen
Context window164K205K
Max output147K131K
Input $ / M tokens$0.27$0.60
Output $ / M tokens$1$2.20
Results tracked1629

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Category by category

Coding DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 42.0 (#113), GLM-4.6: 40.1 (#148)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
SciCode40.6%38.4%
LMArena Coding14261449
ALE-Bench745.17340.82
SWE-bench Verified (bash only)—55.4%
LMArena WebDev—1340

Agentic & Tool Use Not comparable

DeepSeek-V3.1-Terminus: —, GLM-4.6: 32.3 (#66)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
Terminal-Bench—24.5%
Berkeley Function Calling Leaderboard—72.4%

Reasoning DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 26.4 (#133), GLM-4.6: 23.7 (#172)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
Kagi LLM Benchmark57.4%47.4%
CritPt1.7%1.1%
LMArena Hard Prompts14261440
DTBench81.3%—
LMCA28.6%—

Math Too close to call

DeepSeek-V3.1-Terminus: 38.5 (#137), GLM-4.6: 39.1 (#111)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
LMArena Math14021432
FrontierMath (Feb 2025 set)—3.8%
FrontierMath Tier 4 (v1)—2.1%

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, GLM-4.6: 40.2 (#124)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
Vectara Hallucination Rate—9.5%
LMArena Expert—1431

Multilingual GLM-4.6 leads

DeepSeek-V3.1-Terminus: 52.1 (#92), GLM-4.6: 53.5 (#66)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
LMArena Non-English14071426
LMArena Russian14361419
LMArena Chinese—1499
LMArena French—1459
LMArena German—1447
LMArena Japanese—1393
LMArena Korean—1400
LMArena Spanish—1436

Instruction Following Too close to call

DeepSeek-V3.1-Terminus: 74.0 (#106), GLM-4.6: 74.3 (#98)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
LMArena Instruction Following14041410

Long Context Too close to call

DeepSeek-V3.1-Terminus: 43.4 (#97), GLM-4.6: 43.4 (#94)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
LMArena Longer Query14211422

Writing & Preference Too close to call

DeepSeek-V3.1-Terminus: 61.0 (#92), GLM-4.6: 61.1 (#90)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.6
LMArena Text14191440
LMArena Creative Writing14031411
LMArena Multi-Turn14111427
EQ-Bench Creative Writing—1411

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than GLM-4.6?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 41.4 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1-Terminus or GLM-4.6?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Is DeepSeek-V3.1-Terminus or GLM-4.6 better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 40.1 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 164K.

How many benchmarks do DeepSeek-V3.1-Terminus and GLM-4.6 share?

14 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GLM-4.6 has 29.

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