Noometry

Model comparison

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

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 33.4 on the Noometry Index.

Last verified . 12 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Qwen2.5-Coder-32B Alibaba (Qwen)

33.4

Rank #245 Confirmed

Summary

  • They share 12 benchmarks with published results for both. GLM-4.7-Flash scores higher in 7 categories and Qwen2.5-Coder-32B in 1 category; 7 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GLM-4.7-Flash leads 40.6 to 22.6.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 33K.

Side by side

GLM-4.7-FlashQwen2.5-Coder-32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index38.833.4
Released2026-01-192024-09-18
WeightsOpenOpen
Context window200K33K
Max output131K29K
Input $ / M tokens$0.06$0.66
Output $ / M tokens$0.40$1
Results tracked2131

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Qwen2.5-Coder-32B: 22.6 (#333)

BenchmarkGLM-4.7-FlashQwen2.5-Coder-32B
LMArena Coding13831276
SWE-bench Verified (bash only)—9%
Aider Polyglot—16.4%
BigCodeBench Instruct—49%
LiveBench Coding—56.9%
BigCodeBench Complete—58%
HumanEval+—87.2%
MBPP+—77%

Reasoning Too close to call

GLM-4.7-Flash: 20.9 (#229), Qwen2.5-Coder-32B: 21.2 (#225)

BenchmarkGLM-4.7-FlashQwen2.5-Coder-32B
LMArena Hard Prompts13561251
Chess Puzzles0%—
LiveBench Reasoning—42.1%
LiveBench Data Analysis—49.9%
Epoch Capabilities Index—119.49
HellaSwag—83%
LiveBench—46.2%
WinoGrande—80.8%

Math GLM-4.7-Flash leads

GLM-4.7-Flash: 36.1 (#173), Qwen2.5-Coder-32B: 33.3 (#204)

BenchmarkGLM-4.7-FlashQwen2.5-Coder-32B
LMArena Math13551251
OTIS Mock AIME 2024-202558.3%—
LiveBench Math—46.6%
GSM8K—93%

Knowledge GLM-4.7-Flash leads

GLM-4.7-Flash: 35.5 (#184), Qwen2.5-Coder-32B: 33.4 (#203)

BenchmarkGLM-4.7-FlashQwen2.5-Coder-32B
LMArena Expert13571221
GPQA Diamond60.5%—
Vectara Hallucination Rate9.3%—
ARC (AI2) Challenge—70.5%
MMLU—79.1%

Multilingual GLM-4.7-Flash leads

GLM-4.7-Flash: 46.5 (#158), Qwen2.5-Coder-32B: 37.8 (#235)

BenchmarkGLM-4.7-FlashQwen2.5-Coder-32B
LMArena Non-English13301205
LMArena Chinese14031222
LMArena Russian13321228
LMArena French1332—
LMArena German1337—
LMArena Korean1283—
LMArena Spanish1350—

Instruction Following GLM-4.7-Flash leads

GLM-4.7-Flash: 70.1 (#167), Qwen2.5-Coder-32B: 61.4 (#245)

BenchmarkGLM-4.7-FlashQwen2.5-Coder-32B
LMArena Instruction Following13271223
LiveBench Instruction Following—58.7%

Long Context GLM-4.7-Flash leads

GLM-4.7-Flash: 40.9 (#148), Qwen2.5-Coder-32B: 38.0 (#208)

BenchmarkGLM-4.7-FlashQwen2.5-Coder-32B
LMArena Longer Query13451251

Writing & Preference GLM-4.7-Flash leads

GLM-4.7-Flash: 47.4 (#210), Qwen2.5-Coder-32B: 41.6 (#240)

BenchmarkGLM-4.7-FlashQwen2.5-Coder-32B
LMArena Text13511230
LMArena Creative Writing12971174
LMArena Multi-Turn13421222
EQ-Bench Creative Writing1125—
LiveBench Language—23.3%

Frequently asked questions

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

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 33.4 on the Noometry Index.

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

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.

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

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 22.6 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 33K.

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

12 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen2.5-Coder-32B has 31.

Related comparisons

Go deeper