Noometry

Model comparison

GLM-4.6V vs Kimi K2.7 Code

Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 41.3 on the Noometry Index. GLM-4.6V costs 3.8× 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-4.6V Z.ai (Zhipu)

41.3

Rank #137 Confirmed

Kimi K2.7 Code Moonshot AI

43.3

Rank #94 Confirmed

Summary

  • The widest gap is in knowledge, where Kimi K2.7 Code leads 53.5 to 38.0.
  • GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
  • Kimi K2.7 Code accepts more context: 262K tokens versus 128K.

Side by side

GLM-4.6VKimi K2.7 Code
ProviderZ.ai (Zhipu)Moonshot AI
Noometry Index41.343.3
Released2025-12-082026-06-12
WeightsOpenOpen
Context window128K262K
Max output33K262K
Input $ / M tokens$0.30$0.95
Output $ / M tokens$0.90$4
Results tracked1219

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Category by category

Coding Kimi K2.7 Code leads

GLM-4.6V: 40.9 (#128), Kimi K2.7 Code: 42.9 (#95)

BenchmarkGLM-4.6VKimi K2.7 Code
DeepSWE—30.5%
FrontierCode—30.1%
LMArena WebDev—1473
SciCode—47.5%
WeirdML—54.1%
LMArena Coding1390—
ALE-Bench—886.23

Agentic & Tool Use Not comparable

GLM-4.6V: —, Kimi K2.7 Code: 24.0 (#122)

BenchmarkGLM-4.6VKimi K2.7 Code
APEX-Agents—37.6%
GBAEval—0.9%
Vending-Bench 2—5,083

Reasoning Kimi K2.7 Code leads

GLM-4.6V: 27.6 (#115), Kimi K2.7 Code: 39.0 (#61)

BenchmarkGLM-4.6VKimi K2.7 Code
SimpleBench—57.9%
CritPt—10%
Chess Puzzles—21%
LMArena Hard Prompts1368—
Surface Evolver Bench—48.8%
Epoch Capabilities Index—149.97

Math Not comparable

GLM-4.6V: —, Kimi K2.7 Code: 52.9 (#48)

BenchmarkGLM-4.6VKimi K2.7 Code
FrontierMath (Tiers 1-3)—54%
FrontierMath Tier 4—12.2%
OTIS Mock AIME 2024-2025—95.6%

Knowledge Kimi K2.7 Code leads

GLM-4.6V: 38.0 (#149), Kimi K2.7 Code: 53.5 (#57)

BenchmarkGLM-4.6VKimi K2.7 Code
GPQA Diamond—87.9%
SimpleQA Verified—36.5%
LMArena Expert1371—

Multimodal Not comparable

GLM-4.6V: 34.8 (#90), Kimi K2.7 Code: —

BenchmarkGLM-4.6VKimi K2.7 Code
LMArena Vision1164—

Multilingual Not comparable

GLM-4.6V: 48.6 (#141), Kimi K2.7 Code: —

BenchmarkGLM-4.6VKimi K2.7 Code
LMArena Non-English1359—
LMArena Chinese1425—
LMArena Russian1340—

Instruction Following Not comparable

GLM-4.6V: 71.4 (#151), Kimi K2.7 Code: —

BenchmarkGLM-4.6VKimi K2.7 Code
LMArena Instruction Following1352—

Long Context Not comparable

GLM-4.6V: 41.3 (#143), Kimi K2.7 Code: —

BenchmarkGLM-4.6VKimi K2.7 Code
LMArena Longer Query1358—

Writing & Preference Not comparable

GLM-4.6V: 56.6 (#137), Kimi K2.7 Code: —

BenchmarkGLM-4.6VKimi K2.7 Code
LMArena Text1377—
LMArena Creative Writing1347—
LMArena Multi-Turn1360—

Frequently asked questions

Is GLM-4.6V better than Kimi K2.7 Code?

Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 41.3 on the Noometry Index. GLM-4.6V costs 3.8× 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-4.6V or Kimi K2.7 Code?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

Is GLM-4.6V or Kimi K2.7 Code better for coding?

Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 40.9 in the Noometry coding category.

Which has the bigger context window?

Kimi K2.7 Code does, with 262K tokens against 128K.

How many benchmarks do GLM-4.6V and Kimi K2.7 Code share?

0 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Kimi K2.7 Code has 19.

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