Noometry

Model comparison

GLM-4.5-Air vs Kimi K2.7 Code

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 38.7 on the Noometry Index. GLM-4.5-Air costs 4.0× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.

Last verified . 0 shared benchmarks.

GLM-4.5-Air Z.ai (Zhipu)

38.7

Rank #169 Confirmed

Kimi K2.7 Code Moonshot AI

43.0

Rank #90 Confirmed

Summary

  • The widest gap is in knowledge, where Kimi K2.7 Code leads 53.6 to 34.6.
  • GLM-4.5-Air is cheaper at $0.20 / $1.10 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
  • Kimi K2.7 Code accepts more context: 262K tokens versus 131K.

Side by side

GLM-4.5-AirKimi K2.7 Code
ProviderZ.ai (Zhipu)Moonshot AI
Noometry Index38.743.0
Released2025-07-202026-06-12
WeightsOpenOpen
Context window131K262K
Max output98K262K
Input $ / M tokens$0.20$0.95
Output $ / M tokens$1.10$4
Results tracked2719

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Category by category

Coding Kimi K2.7 Code leads

GLM-4.5-Air: 34.5 (#225), Kimi K2.7 Code: 42.4 (#92)

BenchmarkGLM-4.5-AirKimi K2.7 Code
DeepSWE—30.5%
FrontierCode—30.1%
LMArena WebDev—1473
SciCode—47.5%
GSO2.9%—
WeirdML—54.1%
LMArena Coding1397—
ALE-Bench—886.23

Agentic & Tool Use Not comparable

GLM-4.5-Air: —, Kimi K2.7 Code: 23.9 (#115)

BenchmarkGLM-4.5-AirKimi K2.7 Code
APEX-Agents—37.6%
GBAEval—0.9%
Vending-Bench 2—5,083

Reasoning Kimi K2.7 Code leads

GLM-4.5-Air: 23.3 (#166), Kimi K2.7 Code: 38.2 (#56)

BenchmarkGLM-4.5-AirKimi K2.7 Code
SimpleBench—57.9%
Kagi LLM Benchmark43%—
CritPt—10%
Chess Puzzles—21%
LMArena Hard Prompts1379—
Surface Evolver Bench—48.8%
Epoch Capabilities Index—150.01
ForecastBench59.2—

Math Kimi K2.7 Code leads

GLM-4.5-Air: 33.8 (#166), Kimi K2.7 Code: 52.6 (#39)

BenchmarkGLM-4.5-AirKimi K2.7 Code
FrontierMath (Tiers 1-3)—54%
FrontierMath Tier 4—12.2%
OTIS Mock AIME 2024-2025—95.6%
Omni-MATH39.1%—
LMArena Math1396—

Knowledge Kimi K2.7 Code leads

GLM-4.5-Air: 34.6 (#184), Kimi K2.7 Code: 53.6 (#51)

BenchmarkGLM-4.5-AirKimi K2.7 Code
GPQA Diamond—87.9%
Humanity's Last Exam8.1%—
SimpleQA Verified—36.5%
MMLU-Pro76.2%—
Vectara Hallucination Rate9.3%—
GPQA (HELM)59.4%—
LMArena Expert1370—

Multilingual Not comparable

GLM-4.5-Air: 49.4 (#130), Kimi K2.7 Code: —

BenchmarkGLM-4.5-AirKimi K2.7 Code
LMArena Non-English1366—
LMArena Chinese1427—
LMArena French1399—
LMArena German1376—
LMArena Japanese1348—
LMArena Korean1306—
LMArena Russian1373—
LMArena Spanish1385—

Instruction Following Not comparable

GLM-4.5-Air: 69.8 (#165), Kimi K2.7 Code: —

BenchmarkGLM-4.5-AirKimi K2.7 Code
IFEval81.2%—
LMArena Instruction Following1353—

Long Context Not comparable

GLM-4.5-Air: 41.6 (#130), Kimi K2.7 Code: —

BenchmarkGLM-4.5-AirKimi K2.7 Code
LMArena Longer Query1367—

Writing & Preference Not comparable

GLM-4.5-Air: 56.1 (#134), Kimi K2.7 Code: —

BenchmarkGLM-4.5-AirKimi K2.7 Code
LMArena Text1384—
LMArena Creative Writing1343—
WildBench78.9%—
LMArena Multi-Turn1371—

Frequently asked questions

Is GLM-4.5-Air better than Kimi K2.7 Code?

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 38.7 on the Noometry Index. GLM-4.5-Air costs 4.0× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.

Which is cheaper, GLM-4.5-Air or Kimi K2.7 Code?

GLM-4.5-Air is cheaper. It lists at $0.20 per million input tokens and $1.10 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

Is GLM-4.5-Air or Kimi K2.7 Code better for coding?

Kimi K2.7 Code scores higher on coding benchmarks: 42.4 versus 34.5 in the Noometry coding category.

Which has the bigger context window?

Kimi K2.7 Code does, with 262K tokens against 131K.

How many benchmarks do GLM-4.5-Air and Kimi K2.7 Code share?

0 benchmarks have published results for both models. GLM-4.5-Air has 27 scored results on Noometry and Kimi K2.7 Code has 19.

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