Model comparison

GLM-4.5 vs Qwen3.5-9B

GLM-4.5 is the stronger model overall, scoring 42.0 to 33.8 on the Noometry Index. Qwen3.5-9B costs 8.9× less per token, which makes it the better buy when GLM-4.5's lead doesn't matter for your workload.

Last verified October 12, 2026. 0 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 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 35.9.
Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
Qwen3.5-9B accepts more context: 262K tokens versus 131K.

Side by side

GLM-4.5 and Qwen3.5-9B specifications
	GLM-4.5	Qwen3.5-9B
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	42.0	33.8
Released	2025-07-27	2026-02-23
Weights	Open	Open
Context window	131K	262K
Max output	98K	66K
Input $ / M tokens	$0.60	$0.10
Output $ / M tokens	$2.20	$0.15
Results tracked	27	10

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Category by category

Coding GLM-4.5 leads

GLM-4.5: 41.4 (#125), Qwen3.5-9B: 35.9 (#217)

Coding benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
SWE-bench Verified (bash only)	54.2%	—
SciCode	—	27.5%
WeirdML	40.6%	—
LMArena Coding	1434	—
ALE-Bench	344.82	—
AlgoTune	1.52	—

Agentic & Tool Use Not comparable

GLM-4.5: —, Qwen3.5-9B: 14.5 (#151)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
Terminal-Bench	—	9.2%

Reasoning GLM-4.5 leads

GLM-4.5: 28.6 (#100), Qwen3.5-9B: 23.1 (#182)

Reasoning benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
Kagi LLM Benchmark	57.9%	—
CritPt	—	0.3%
Chess Puzzles	—	12%
LMArena Hard Prompts	1429	—
DTBench	—	71.2%
LMCA	—	24.5%
Epoch Capabilities Index	—	139.46

Math GLM-4.5 leads

GLM-4.5: 39.0 (#116), Qwen3.5-9B: 34.8 (#192)

Math benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
MathArena Final-Answer Competitions	—	48.5%
OTIS Mock AIME 2024-2025	—	61.7%
LMArena Math	1427	—

Knowledge Qwen3.5-9B leads

GLM-4.5: 35.9 (#179), Qwen3.5-9B: 46.0 (#84)

Knowledge benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
GPQA Diamond	—	79%
Humanity's Last Exam	8.3%	—
Confabulations	11.3%	—
LMArena Expert	1433	—

Multilingual Not comparable

GLM-4.5: 52.8 (#77), Qwen3.5-9B: —

Multilingual benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
LMArena Non-English	1417	—
LMArena Chinese	1465	—
LMArena French	1418	—
LMArena German	1407	—
LMArena Japanese	1415	—
LMArena Korean	1380	—
LMArena Russian	1414	—
LMArena Spanish	1454	—

Instruction Following Not comparable

GLM-4.5: 74.1 (#104), Qwen3.5-9B: —

Instruction Following benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
LMArena Instruction Following	1404	—

Long Context Not comparable

GLM-4.5: 38.2 (#201), Qwen3.5-9B: —

Long Context benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
Fiction.LiveBench	58.3%	—
LMArena Longer Query	1412	—

Writing & Preference Not comparable

GLM-4.5: 57.5 (#127), Qwen3.5-9B: —

Writing & Preference benchmarks
Benchmark	GLM-4.5	Qwen3.5-9B
LMArena Text	1430	—
LMArena Creative Writing	1395	—
Short-Story Creative Writing	73.4%	—
EQ-Bench Creative Writing	1343	—
LMArena Multi-Turn	1415	—

Frequently asked questions

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

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

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

GLM-4.5 scores higher on coding benchmarks: 41.4 versus 35.9 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5-9B does, with 262K tokens against 131K.

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

0 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen3.5-9B has 10.