Noometry

Model comparison

GLM-4.5 vs GLM-4.7-Flash

GLM-4.5 is the stronger model overall, scoring 42.0 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-4.5's lead doesn't matter for your workload.

Last verified . 17 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 Confirmed

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.5 scores higher in 7 categories and GLM-4.7-Flash in 1 category; 6 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.5 leads 57.5 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-4.5.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 131K.

Side by side

GLM-4.5GLM-4.7-Flash
ProviderZ.ai (Zhipu)Z.ai (Zhipu)
Noometry Index42.038.8
Released2025-07-272026-01-19
WeightsOpenOpen
Context window131K200K
Max output98K131K
Input $ / M tokens$0.60$0.06
Output $ / M tokens$2.20$0.40
Results tracked2721

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Category by category

Coding Too close to call

GLM-4.5: 41.4 (#125), GLM-4.7-Flash: 40.6 (#135)

BenchmarkGLM-4.5GLM-4.7-Flash
LMArena Coding14341383
SWE-bench Verified (bash only)54.2%—
WeirdML40.6%—
ALE-Bench344.82—
AlgoTune1.52—

Reasoning GLM-4.5 leads

GLM-4.5: 28.6 (#100), GLM-4.7-Flash: 20.9 (#229)

BenchmarkGLM-4.5GLM-4.7-Flash
LMArena Hard Prompts14291356
Kagi LLM Benchmark57.9%—
Chess Puzzles—0%

Math GLM-4.5 leads

GLM-4.5: 39.0 (#116), GLM-4.7-Flash: 36.1 (#173)

BenchmarkGLM-4.5GLM-4.7-Flash
LMArena Math14271355
OTIS Mock AIME 2024-2025—58.3%

Knowledge Too close to call

GLM-4.5: 35.9 (#179), GLM-4.7-Flash: 35.5 (#184)

BenchmarkGLM-4.5GLM-4.7-Flash
LMArena Expert14331357
GPQA Diamond—60.5%
Humanity's Last Exam8.3%—
Confabulations11.3%—
Vectara Hallucination Rate—9.3%

Multilingual GLM-4.5 leads

GLM-4.5: 52.8 (#77), GLM-4.7-Flash: 46.5 (#158)

BenchmarkGLM-4.5GLM-4.7-Flash
LMArena Non-English14171330
LMArena Chinese14651403
LMArena French14181332
LMArena German14071337
LMArena Korean13801283
LMArena Russian14141332
LMArena Spanish14541350
LMArena Japanese1415—

Instruction Following GLM-4.5 leads

GLM-4.5: 74.1 (#104), GLM-4.7-Flash: 70.1 (#167)

BenchmarkGLM-4.5GLM-4.7-Flash
LMArena Instruction Following14041327

Long Context GLM-4.7-Flash leads

GLM-4.5: 38.2 (#201), GLM-4.7-Flash: 40.9 (#148)

BenchmarkGLM-4.5GLM-4.7-Flash
LMArena Longer Query14121345
Fiction.LiveBench58.3%—

Writing & Preference GLM-4.5 leads

GLM-4.5: 57.5 (#127), GLM-4.7-Flash: 47.4 (#210)

BenchmarkGLM-4.5GLM-4.7-Flash
LMArena Text14301351
LMArena Creative Writing13951297
EQ-Bench Creative Writing13431125
LMArena Multi-Turn14151342
Short-Story Creative Writing73.4%—

Frequently asked questions

Is GLM-4.5 better than GLM-4.7-Flash?

GLM-4.5 is the stronger model overall, scoring 42.0 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-4.5's lead doesn't matter for your workload.

Which is cheaper, GLM-4.5 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-4.5 lists at $0.60 and $2.20.

Is GLM-4.5 or GLM-4.7-Flash better for coding?

They score almost the same on coding (41.4 vs 40.6); test both on your own repository before choosing.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 131K.

How many benchmarks do GLM-4.5 and GLM-4.7-Flash share?

17 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and GLM-4.7-Flash has 21.

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