Noometry

Model comparison

GLM-4.7-Flash vs Qwen3 32B

GLM-4.7-Flash and Qwen3 32B score almost the same on the Noometry Index (38.8 vs 39.2), so choose on price, context window or the category you care about most.

Last verified . 17 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Qwen3 32B Alibaba (Qwen)

39.2

Rank #172 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.7-Flash scores higher in 4 categories and Qwen3 32B in 4 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where Qwen3 32B leads 52.9 to 47.4.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 66.9% for Qwen3 32B.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 131K.

Side by side

GLM-4.7-FlashQwen3 32B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index38.839.2
Released2026-01-192025-04
WeightsOpenOpen
Context window200K131K
Max output131K16K
Input $ / M tokens$0.06$0.70
Output $ / M tokens$0.40$2.80
Results tracked2126

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Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Qwen3 32B: 37.7 (#190)

BenchmarkGLM-4.7-FlashQwen3 32B
LMArena Coding13831358
Aider Polyglot—40%
SciCode—35.4%

Agentic & Tool Use Not comparable

GLM-4.7-Flash: —, Qwen3 32B: 32.6 (#62)

BenchmarkGLM-4.7-FlashQwen3 32B
Berkeley Function Calling Leaderboard—48.7%

Reasoning Too close to call

GLM-4.7-Flash: 20.9 (#229), Qwen3 32B: 20.2 (#241)

BenchmarkGLM-4.7-FlashQwen3 32B
Chess Puzzles0%5%
LMArena Hard Prompts13561334
Kagi LLM Benchmark—54.9%
CritPt—0.3%
DTBench—67.5%
LMCA—17.3%
Epoch Capabilities Index—138.51

Math Qwen3 32B leads

GLM-4.7-Flash: 36.1 (#173), Qwen3 32B: 39.7 (#99)

BenchmarkGLM-4.7-FlashQwen3 32B
OTIS Mock AIME 2024-202558.3%66.9%
LMArena Math13551399

Knowledge Qwen3 32B leads

GLM-4.7-Flash: 35.5 (#184), Qwen3 32B: 40.0 (#125)

BenchmarkGLM-4.7-FlashQwen3 32B
GPQA Diamond60.5%65.7%
Vectara Hallucination Rate9.3%5.9%
LMArena Expert13571362

Multilingual Too close to call

GLM-4.7-Flash: 46.5 (#158), Qwen3 32B: 45.6 (#167)

BenchmarkGLM-4.7-FlashQwen3 32B
LMArena Non-English13301317
LMArena Chinese14031357
LMArena German13371341
LMArena Russian13321311
LMArena French1332—
LMArena Korean1283—
LMArena Spanish1350—

Instruction Following GLM-4.7-Flash leads

GLM-4.7-Flash: 70.1 (#167), Qwen3 32B: 68.9 (#179)

BenchmarkGLM-4.7-FlashQwen3 32B
LMArena Instruction Following13271305

Long Context Qwen3 32B leads

GLM-4.7-Flash: 40.9 (#148), Qwen3 32B: 43.8 (#87)

BenchmarkGLM-4.7-FlashQwen3 32B
LMArena Longer Query13451327
Fiction.LiveBench—74.2%

Writing & Preference Qwen3 32B leads

GLM-4.7-Flash: 47.4 (#210), Qwen3 32B: 52.9 (#163)

BenchmarkGLM-4.7-FlashQwen3 32B
LMArena Text13511340
LMArena Creative Writing12971297
LMArena Multi-Turn13421331
EQ-Bench Creative Writing1125—

Frequently asked questions

Is GLM-4.7-Flash better than Qwen3 32B?

GLM-4.7-Flash and Qwen3 32B score almost the same on the Noometry Index (38.8 vs 39.2), so choose on price, context window or the category you care about most.

Which is cheaper, GLM-4.7-Flash or Qwen3 32B?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.

Is GLM-4.7-Flash or Qwen3 32B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 37.7 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 Qwen3 32B share?

17 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen3 32B has 26.

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