Noometry

Model comparison

DeepSeek-V3.1-Terminus vs GLM-4.7

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 42.0 on the Noometry Index.

Last verified . 13 shared benchmarks.

DeepSeek-V3.1-Terminus DeepSeek

43.1

Rank #97 Confirmed

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Summary

  • They share 13 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 3 categories and GLM-4.7 in 4 categories; 2 gaps are clear of the uncertainty.
  • DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
  • GLM-4.7 accepts more context: 205K tokens versus 164K.

Side by side

DeepSeek-V3.1-TerminusGLM-4.7
ProviderDeepSeekZ.ai (Zhipu)
Noometry Index43.142.0
Released2025-09-222025-12-22
WeightsOpenOpen
Context window164K205K
Max output147K131K
Input $ / M tokens$0.27$0.60
Output $ / M tokens$1$2.20
Results tracked1636

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Category by category

Coding GLM-4.7 leads

DeepSeek-V3.1-Terminus: 42.0 (#113), GLM-4.7: 44.0 (#79)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
SciCode40.6%45.1%
LMArena Coding14261454
ALE-Bench745.17399.48
LMArena WebDev—1435

Agentic & Tool Use Not comparable

DeepSeek-V3.1-Terminus: —, GLM-4.7: 26.5 (#103)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
Terminal-Bench—33.4%
Vending-Bench 2—2,377

Reasoning DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 26.4 (#133), GLM-4.7: 24.3 (#164)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
CritPt1.7%1.7%
LMArena Hard Prompts14261443
SimpleBench—47.7%
Kagi LLM Benchmark57.4%—
Chess Puzzles—6%
DTBench81.3%—
LMCA28.6%—
Epoch Capabilities Index—143.51

Math Too close to call

DeepSeek-V3.1-Terminus: 38.5 (#137), GLM-4.7: 38.6 (#135)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
LMArena Math14021423
OTIS Mock AIME 2024-2025—83.3%
ProofBench—6%
FrontierMath (Feb 2025 set)—2.4%
FrontierMath Tier 4 (v1)—0%

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, GLM-4.7: 47.0 (#80)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
GPQA Diamond—83.3%
SimpleQA Verified—32.2%
Vectara Hallucination Rate—11.7%
LMArena Expert—1424

Multilingual Too close to call

DeepSeek-V3.1-Terminus: 52.1 (#92), GLM-4.7: 52.8 (#79)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
LMArena Non-English14071417
LMArena Russian14361423
LMArena Chinese—1495
LMArena French—1432
LMArena German—1424
LMArena Japanese—1439
LMArena Korean—1399
LMArena Spanish—1434

Instruction Following Too close to call

DeepSeek-V3.1-Terminus: 74.0 (#106), GLM-4.7: 74.4 (#95)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
LMArena Instruction Following14041411

Long Context Too close to call

DeepSeek-V3.1-Terminus: 43.4 (#97), GLM-4.7: 42.8 (#116)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
LMArena Longer Query14211432
CL-bench—15.9%
CL-bench Life—10.9%

Writing & Preference Too close to call

DeepSeek-V3.1-Terminus: 61.0 (#92), GLM-4.7: 60.9 (#93)

BenchmarkDeepSeek-V3.1-TerminusGLM-4.7
LMArena Text14191435
LMArena Creative Writing14031401
LMArena Multi-Turn14111446
EQ-Bench Creative Writing—1413

Frequently asked questions

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

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 42.0 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1-Terminus or GLM-4.7?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

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

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 42.0 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 164K.

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

13 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GLM-4.7 has 36.

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