Noometry

Model comparison

GLM-4.7-Flash vs Qwen2.5 32B Instruct

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 30.1 on the Noometry Index.

Last verified . 3 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Qwen2.5 32B Instruct Alibaba (Qwen)

30.1

Rank #297 Confirmed

Summary

  • They share 3 benchmarks with published results for both. GLM-4.7-Flash scores higher in 4 categories and Qwen2.5 32B Instruct in 0 categories; 4 gaps are clear of the uncertainty.
  • The widest gap is in math, where GLM-4.7-Flash leads 36.1 to 16.2.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 7.4% for Qwen2.5 32B Instruct.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 131K.

Side by side

GLM-4.7-FlashQwen2.5 32B Instruct
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index38.830.1
Released2026-01-192024-09
WeightsOpenOpen
Context window200K131K
Max output131K8K
Input $ / M tokens$0.06$0.70
Output $ / M tokens$0.40$2.80
Results tracked217

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Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Qwen2.5 32B Instruct: 38.7 (#169)

BenchmarkGLM-4.7-FlashQwen2.5 32B Instruct
BigCodeBench Instruct—45%
LMArena Coding1383—
BigCodeBench Complete—52.3%

Reasoning GLM-4.7-Flash leads

GLM-4.7-Flash: 20.9 (#229), Qwen2.5 32B Instruct: 19.2 (#266)

BenchmarkGLM-4.7-FlashQwen2.5 32B Instruct
Chess Puzzles0%0%
LMArena Hard Prompts1356—
Epoch Capabilities Index—128.52

Math GLM-4.7-Flash leads

GLM-4.7-Flash: 36.1 (#173), Qwen2.5 32B Instruct: 16.2 (#296)

BenchmarkGLM-4.7-FlashQwen2.5 32B Instruct
OTIS Mock AIME 2024-202558.3%7.4%
LMArena Math1355—
MATH Level 5—56.1%

Knowledge GLM-4.7-Flash leads

GLM-4.7-Flash: 35.5 (#184), Qwen2.5 32B Instruct: 24.9 (#266)

BenchmarkGLM-4.7-FlashQwen2.5 32B Instruct
GPQA Diamond60.5%46.1%
Vectara Hallucination Rate9.3%—
LMArena Expert1357—

Multilingual Not comparable

GLM-4.7-Flash: 46.5 (#158), Qwen2.5 32B Instruct: —

BenchmarkGLM-4.7-FlashQwen2.5 32B Instruct
LMArena Non-English1330—
LMArena Chinese1403—
LMArena French1332—
LMArena German1337—
LMArena Korean1283—
LMArena Russian1332—
LMArena Spanish1350—

Instruction Following Not comparable

GLM-4.7-Flash: 70.1 (#167), Qwen2.5 32B Instruct: —

BenchmarkGLM-4.7-FlashQwen2.5 32B Instruct
LMArena Instruction Following1327—

Long Context Not comparable

GLM-4.7-Flash: 40.9 (#148), Qwen2.5 32B Instruct: —

BenchmarkGLM-4.7-FlashQwen2.5 32B Instruct
LMArena Longer Query1345—

Writing & Preference Not comparable

GLM-4.7-Flash: 47.4 (#210), Qwen2.5 32B Instruct: —

BenchmarkGLM-4.7-FlashQwen2.5 32B Instruct
LMArena Text1351—
LMArena Creative Writing1297—
EQ-Bench Creative Writing1125—
LMArena Multi-Turn1342—

Frequently asked questions

Is GLM-4.7-Flash better than Qwen2.5 32B Instruct?

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 30.1 on the Noometry Index.

Which is cheaper, GLM-4.7-Flash or Qwen2.5 32B Instruct?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.

Is GLM-4.7-Flash or Qwen2.5 32B Instruct better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 38.7 in the Noometry coding category.

Which has the bigger context window?

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

How many benchmarks do GLM-4.7-Flash and Qwen2.5 32B Instruct share?

3 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen2.5 32B Instruct has 7.

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