Model comparison

GLM-4.5V vs Qwen3.5-9B

GLM-4.5V is the stronger model overall, scoring 39.8 to 33.8 on the Noometry Index. Qwen3.5-9B costs 8.0× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

Last verified . 0 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Qwen3.5-9B Alibaba (Qwen)

33.8

Rank #236 Confirmed

Summary

  • The widest gap is in knowledge, where Qwen3.5-9B leads 46.0 to 37.5.
  • Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • Qwen3.5-9B accepts more context: 262K tokens versus 64K.

Side by side

GLM-4.5V and Qwen3.5-9B specifications
GLM-4.5VQwen3.5-9B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index39.833.8
Released2025-08-112026-02-23
WeightsOpenOpen
Context window64K262K
Max output16K66K
Input $ / M tokens$0.60$0.10
Output $ / M tokens$1.80$0.15
Results tracked1510

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 39.5 (#155), Qwen3.5-9B: 35.9 (#217)

Coding benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
SciCode—27.5%
LMArena Coding1347—

Agentic & Tool Use Not comparable

GLM-4.5V: —, Qwen3.5-9B: 14.5 (#151)

Agentic & Tool Use benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
Terminal-Bench—9.2%

Reasoning GLM-4.5V leads

GLM-4.5V: 27.4 (#119), Qwen3.5-9B: 23.1 (#182)

Reasoning benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
Kagi LLM Benchmark59.8%—
CritPt—0.3%
Chess Puzzles—12%
LMArena Hard Prompts1334—
DTBench—71.2%
LMCA—24.5%
Epoch Capabilities Index—139.46

Math GLM-4.5V leads

GLM-4.5V: 37.4 (#159), Qwen3.5-9B: 34.8 (#192)

Math benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
MathArena Final-Answer Competitions—48.5%
OTIS Mock AIME 2024-2025—61.7%
LMArena Math1354—

Knowledge Qwen3.5-9B leads

GLM-4.5V: 37.5 (#156), Qwen3.5-9B: 46.0 (#84)

Knowledge benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
GPQA Diamond—79%
LMArena Expert1353—

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), Qwen3.5-9B: —

Multimodal benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
LMArena Vision1154—

Multilingual Not comparable

GLM-4.5V: 44.6 (#177), Qwen3.5-9B: —

Multilingual benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
LMArena Non-English1303—
LMArena Chinese1337—
LMArena Russian1298—
LMArena Spanish1336—

Instruction Following Not comparable

GLM-4.5V: 69.2 (#175), Qwen3.5-9B: —

Instruction Following benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
LMArena Instruction Following1311—

Long Context Not comparable

GLM-4.5V: 39.6 (#171), Qwen3.5-9B: —

Long Context benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
LMArena Longer Query1304—

Writing & Preference Not comparable

GLM-4.5V: 52.5 (#170), Qwen3.5-9B: —

Writing & Preference benchmarks
BenchmarkGLM-4.5VQwen3.5-9B
LMArena Text1333—
LMArena Creative Writing1295—
LMArena Multi-Turn1332—

Frequently asked questions

Is GLM-4.5V better than Qwen3.5-9B?

GLM-4.5V is the stronger model overall, scoring 39.8 to 33.8 on the Noometry Index. Qwen3.5-9B costs 8.0× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

Which is cheaper, GLM-4.5V or Qwen3.5-9B?

Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or Qwen3.5-9B better for coding?

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 35.9 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5-9B does, with 262K tokens against 64K.

How many benchmarks do GLM-4.5V and Qwen3.5-9B share?

0 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3.5-9B has 10.

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