Noometry

Model comparison

DeepSeek-V3.1 vs GLM-4.6

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 41.4 on the Noometry Index.

Last verified . 20 shared benchmarks.

DeepSeek-V3.1 DeepSeek

42.8

Rank #108 Confirmed

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Summary

  • They share 20 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 3 categories and GLM-4.6 in 5 categories; 4 gaps are clear of the uncertainty.
  • The widest gap is in long context, where GLM-4.6 leads 43.4 to 36.3.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 53.2% for DeepSeek-V3.1 and 47.4% for GLM-4.6.
  • DeepSeek-V3.1 is cheaper at $0.25 / $0.95 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-V3.1GLM-4.6
ProviderDeepSeekZ.ai (Zhipu)
Noometry Index42.841.4
Released2025-08-212025-09-30
WeightsOpenOpen
Context window164K205K
Max output8K131K
Input $ / M tokens$0.25$0.60
Output $ / M tokens$0.95$2.20
Results tracked2729

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Category by category

Coding Too close to call

DeepSeek-V3.1: 40.3 (#144), GLM-4.6: 40.1 (#148)

BenchmarkDeepSeek-V3.1GLM-4.6
LMArena Coding14171449
SWE-bench Verified (bash only)—55.4%
LMArena WebDev—1340
SciCode—38.4%
WeirdML38.4%—
ALE-Bench—340.82

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, GLM-4.6: 32.3 (#66)

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

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), GLM-4.6: 23.7 (#172)

BenchmarkDeepSeek-V3.1GLM-4.6
Kagi LLM Benchmark53.2%47.4%
LMArena Hard Prompts14171440
SimpleBench40%—
CritPt—1.1%
DTBench82.7%—
LMCA24.3%—
Epoch Capabilities Index139.92—
ForecastBench58—

Math Too close to call

DeepSeek-V3.1: 38.9 (#122), GLM-4.6: 39.1 (#111)

BenchmarkDeepSeek-V3.1GLM-4.6
LMArena Math14201432
FrontierMath (Feb 2025 set)—3.8%
FrontierMath Tier 4 (v1)—2.1%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), GLM-4.6: 40.2 (#124)

BenchmarkDeepSeek-V3.1GLM-4.6
Vectara Hallucination Rate5.5%9.5%
LMArena Expert14051431

Multilingual GLM-4.6 leads

DeepSeek-V3.1: 51.6 (#106), GLM-4.6: 53.5 (#66)

BenchmarkDeepSeek-V3.1GLM-4.6
LMArena Non-English14001426
LMArena Chinese14691499
LMArena French14471459
LMArena German14111447
LMArena Japanese13781393
LMArena Korean13371400
LMArena Russian14051419
LMArena Spanish14311436

Instruction Following Too close to call

DeepSeek-V3.1: 73.9 (#110), GLM-4.6: 74.3 (#98)

BenchmarkDeepSeek-V3.1GLM-4.6
LMArena Instruction Following14001410

Long Context GLM-4.6 leads

DeepSeek-V3.1: 36.3 (#232), GLM-4.6: 43.4 (#94)

BenchmarkDeepSeek-V3.1GLM-4.6
LMArena Longer Query14221422
Fiction.LiveBench52.8%—

Writing & Preference Too close to call

DeepSeek-V3.1: 60.3 (#98), GLM-4.6: 61.1 (#90)

BenchmarkDeepSeek-V3.1GLM-4.6
LMArena Text14201440
LMArena Creative Writing14011411
EQ-Bench Creative Writing14361411
LMArena Multi-Turn14081427

Frequently asked questions

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

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 41.4 on the Noometry Index.

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

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

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

They score almost the same on coding (40.3 vs 40.1); test both on your own repository before choosing.

Which has the bigger context window?

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

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

20 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GLM-4.6 has 29.

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