Noometry

Model comparison

GLM-5.3 vs Kimi K2.7 Code

GLM-5.3 is the stronger model overall, scoring 54.8 to 43.3 on the Noometry Index.

Last verified . 16 shared benchmarks.

GLM-5.3 Z.ai (Zhipu)

54.8

Rank #26 Confirmed

Kimi K2.7 Code Moonshot AI

43.3

Rank #94 Confirmed

Summary

  • They share 16 benchmarks with published results for both. GLM-5.3 scores higher in 5 categories and Kimi K2.7 Code in 0 categories; 5 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-5.3 leads 59.5 to 42.9.
  • The biggest single-benchmark swing is DeepSWE: 69% for GLM-5.3 and 30.5% for Kimi K2.7 Code.
  • Kimi K2.7 Code is cheaper at $0.95 / $4 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
  • GLM-5.3 accepts more context: 1M tokens versus 262K.

Side by side

GLM-5.3Kimi K2.7 Code
ProviderZ.ai (Zhipu)Moonshot AI
Noometry Index54.843.3
Released2026-08-142026-06-12
WeightsOpenOpen
Context window1M262K
Max output131K262K
Input $ / M tokens$1.40$0.95
Output $ / M tokens$4.40$4
Results tracked4219

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Category by category

Coding GLM-5.3 leads

GLM-5.3: 59.5 (#14), Kimi K2.7 Code: 42.9 (#95)

BenchmarkGLM-5.3Kimi K2.7 Code
DeepSWE69%30.5%
FrontierCode40.1%30.1%
LMArena WebDev16221473
SciCode59%47.5%
WeirdML75.4%54.1%
ALE-Bench1,317886.23
CursorBench42.6%—
FrontierSWE30.2%—
LMArena Coding1496—

Agentic & Tool Use GLM-5.3 leads

GLM-5.3: 36.4 (#38), Kimi K2.7 Code: 24.0 (#122)

BenchmarkGLM-5.3Kimi K2.7 Code
APEX-Agents56.6%37.6%
Vending-Bench 28,1645,083
GBAEval—0.9%

Reasoning GLM-5.3 leads

GLM-5.3: 46.1 (#46), Kimi K2.7 Code: 39.0 (#61)

BenchmarkGLM-5.3Kimi K2.7 Code
CritPt19.1%10%
Chess Puzzles21%21%
Epoch Capabilities Index155.61149.97
SimpleBench—57.9%
NYT Connections (extended)74.2%—
LMArena Hard Prompts1489—
Mystery Game Puzzles33%—
DTBench87.7%—
LMCA55.5%—
Surface Evolver Bench—48.8%
Bench to the Future 30.15—

Math GLM-5.3 leads

GLM-5.3: 62.3 (#33), Kimi K2.7 Code: 52.9 (#48)

BenchmarkGLM-5.3Kimi K2.7 Code
FrontierMath (Tiers 1-3)68.8%54%
FrontierMath Tier 429.3%12.2%
OTIS Mock AIME 2024-202591.1%95.6%
ProofBench49%—
LMArena Math1489—

Knowledge GLM-5.3 leads

GLM-5.3: 58.3 (#37), Kimi K2.7 Code: 53.5 (#57)

BenchmarkGLM-5.3Kimi K2.7 Code
GPQA Diamond90.9%87.9%
SimpleQA Verified41%36.5%
LMArena Expert1516—

Multilingual Not comparable

GLM-5.3: 55.7 (#28), Kimi K2.7 Code: —

BenchmarkGLM-5.3Kimi K2.7 Code
LMArena Non-English1457—
LMArena Chinese1528—
LMArena French1499—
LMArena German1499—
LMArena Japanese1453—
LMArena Korean1472—
LMArena Russian1463—
LMArena Spanish1460—

Instruction Following Not comparable

GLM-5.3: 77.5 (#23), Kimi K2.7 Code: —

BenchmarkGLM-5.3Kimi K2.7 Code
LMArena Instruction Following1477—

Long Context Not comparable

GLM-5.3: 45.4 (#41), Kimi K2.7 Code: —

BenchmarkGLM-5.3Kimi K2.7 Code
LMArena Longer Query1482—

Writing & Preference Not comparable

GLM-5.3: 75.7 (#6), Kimi K2.7 Code: —

BenchmarkGLM-5.3Kimi K2.7 Code
LMArena Text1471—
LMArena Creative Writing1457—
EQ-Bench Creative Writing2075—
LMArena Multi-Turn1472—

Frequently asked questions

Is GLM-5.3 better than Kimi K2.7 Code?

GLM-5.3 is the stronger model overall, scoring 54.8 to 43.3 on the Noometry Index.

Which is cheaper, GLM-5.3 or Kimi K2.7 Code?

Kimi K2.7 Code is cheaper. It lists at $0.95 per million input tokens and $4 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.

Is GLM-5.3 or Kimi K2.7 Code better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 42.9 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 262K.

How many benchmarks do GLM-5.3 and Kimi K2.7 Code share?

16 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Kimi K2.7 Code has 19.

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