Noometry

Model comparison

GLM-4.6 vs GLM-4.7-Flash

GLM-4.6 is the stronger model overall, scoring 41.4 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 6.9× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified . 18 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Summary

  • They share 18 benchmarks with published results for both. GLM-4.6 scores higher in 7 categories and GLM-4.7-Flash in 1 category; 7 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 47.4.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • GLM-4.6 accepts more context: 205K tokens versus 200K.

Side by side

GLM-4.6GLM-4.7-Flash
ProviderZ.ai (Zhipu)Z.ai (Zhipu)
Noometry Index41.438.8
Released2025-09-302026-01-19
WeightsOpenOpen
Context window205K200K
Max output131K131K
Input $ / M tokens$0.60$0.06
Output $ / M tokens$2.20$0.40
Results tracked2921

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Category by category

Coding Too close to call

GLM-4.6: 40.1 (#148), GLM-4.7-Flash: 40.6 (#135)

BenchmarkGLM-4.6GLM-4.7-Flash
LMArena Coding14491383
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1340—
SciCode38.4%—
ALE-Bench340.82—

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), GLM-4.7-Flash: —

BenchmarkGLM-4.6GLM-4.7-Flash
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), GLM-4.7-Flash: 20.9 (#229)

BenchmarkGLM-4.6GLM-4.7-Flash
LMArena Hard Prompts14401356
Kagi LLM Benchmark47.4%—
CritPt1.1%—
Chess Puzzles—0%

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), GLM-4.7-Flash: 36.1 (#173)

BenchmarkGLM-4.6GLM-4.7-Flash
LMArena Math14321355
OTIS Mock AIME 2024-2025—58.3%
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), GLM-4.7-Flash: 35.5 (#184)

BenchmarkGLM-4.6GLM-4.7-Flash
Vectara Hallucination Rate9.5%9.3%
LMArena Expert14311357
GPQA Diamond—60.5%

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), GLM-4.7-Flash: 46.5 (#158)

BenchmarkGLM-4.6GLM-4.7-Flash
LMArena Non-English14261330
LMArena Chinese14991403
LMArena French14591332
LMArena German14471337
LMArena Korean14001283
LMArena Russian14191332
LMArena Spanish14361350
LMArena Japanese1393—

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), GLM-4.7-Flash: 70.1 (#167)

BenchmarkGLM-4.6GLM-4.7-Flash
LMArena Instruction Following14101327

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), GLM-4.7-Flash: 40.9 (#148)

BenchmarkGLM-4.6GLM-4.7-Flash
LMArena Longer Query14221345

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), GLM-4.7-Flash: 47.4 (#210)

BenchmarkGLM-4.6GLM-4.7-Flash
LMArena Text14401351
LMArena Creative Writing14111297
EQ-Bench Creative Writing14111125
LMArena Multi-Turn14271342

Frequently asked questions

Is GLM-4.6 better than GLM-4.7-Flash?

GLM-4.6 is the stronger model overall, scoring 41.4 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 6.9× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Which is cheaper, GLM-4.6 or GLM-4.7-Flash?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Is GLM-4.6 or GLM-4.7-Flash better for coding?

They score almost the same on coding (40.1 vs 40.6); test both on your own repository before choosing.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 200K.

How many benchmarks do GLM-4.6 and GLM-4.7-Flash share?

18 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and GLM-4.7-Flash has 21.

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