Noometry

Model comparison

GLM-4.5V vs Kimi K2.7 Code

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 39.2 on the Noometry Index. GLM-4.5V costs 1.9× 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-4.5V Z.ai (Zhipu)

39.2

Rank #158 Confirmed

Kimi K2.7 Code Moonshot AI

43.0

Rank #90 Confirmed

Summary

  • The widest gap is in math, where Kimi K2.7 Code leads 52.6 to 34.7.
  • GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
  • Kimi K2.7 Code accepts more context: 262K tokens versus 64K.

Side by side

GLM-4.5VKimi K2.7 Code
ProviderZ.ai (Zhipu)Moonshot AI
Noometry Index39.243.0
Released2025-08-112026-06-12
WeightsOpenOpen
Context window64K262K
Max output16K262K
Input $ / M tokens$0.60$0.95
Output $ / M tokens$1.80$4
Results tracked1519

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Category by category

Coding Kimi K2.7 Code leads

GLM-4.5V: 38.9 (#152), Kimi K2.7 Code: 42.4 (#92)

BenchmarkGLM-4.5VKimi K2.7 Code
DeepSWE—30.5%
FrontierCode—30.1%
LMArena WebDev—1473
SciCode—47.5%
WeirdML—54.1%
LMArena Coding1347—
ALE-Bench—886.23

Agentic & Tool Use Not comparable

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

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

Reasoning Kimi K2.7 Code leads

GLM-4.5V: 26.5 (#118), Kimi K2.7 Code: 38.2 (#56)

BenchmarkGLM-4.5VKimi K2.7 Code
SimpleBench—57.9%
Kagi LLM Benchmark59.8%—
CritPt—10%
Chess Puzzles—21%
LMArena Hard Prompts1335—
Surface Evolver Bench—48.8%
Epoch Capabilities Index—150.01

Math Kimi K2.7 Code leads

GLM-4.5V: 34.7 (#157), Kimi K2.7 Code: 52.6 (#39)

BenchmarkGLM-4.5VKimi K2.7 Code
FrontierMath (Tiers 1-3)—54%
FrontierMath Tier 4—12.2%
OTIS Mock AIME 2024-2025—95.6%
LMArena Math1353—

Knowledge Kimi K2.7 Code leads

GLM-4.5V: 36.8 (#155), Kimi K2.7 Code: 53.6 (#51)

BenchmarkGLM-4.5VKimi K2.7 Code
GPQA Diamond—87.9%
SimpleQA Verified—36.5%
LMArena Expert1353—

Multimodal Not comparable

GLM-4.5V: 33.6 (#86), Kimi K2.7 Code: —

BenchmarkGLM-4.5VKimi K2.7 Code
LMArena Vision1154—

Multilingual Not comparable

GLM-4.5V: 44.9 (#172), Kimi K2.7 Code: —

BenchmarkGLM-4.5VKimi K2.7 Code
LMArena Non-English1304—
LMArena Chinese1338—
LMArena Russian1299—
LMArena Spanish1335—

Instruction Following Not comparable

GLM-4.5V: 69.4 (#170), Kimi K2.7 Code: —

BenchmarkGLM-4.5VKimi K2.7 Code
LMArena Instruction Following1311—

Long Context Not comparable

GLM-4.5V: 39.6 (#166), Kimi K2.7 Code: —

BenchmarkGLM-4.5VKimi K2.7 Code
LMArena Longer Query1305—

Writing & Preference Not comparable

GLM-4.5V: 52.7 (#162), Kimi K2.7 Code: —

BenchmarkGLM-4.5VKimi K2.7 Code
LMArena Text1333—
LMArena Creative Writing1295—
LMArena Multi-Turn1333—

Frequently asked questions

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

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 39.2 on the Noometry Index. GLM-4.5V costs 1.9× 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-4.5V or Kimi K2.7 Code?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

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

Kimi K2.7 Code scores higher on coding benchmarks: 42.4 versus 38.9 in the Noometry coding category.

Which has the bigger context window?

Kimi K2.7 Code does, with 262K tokens against 64K.

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

0 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Kimi K2.7 Code has 19.

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