Noometry

Model comparison

GLM-4.6 vs Qwen3.5-9B

GLM-4.6 is the stronger model overall, scoring 41.4 to 33.8 on the Noometry Index. Qwen3.5-9B costs 8.9× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified . 3 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen3.5-9B Alibaba (Qwen)

33.8

Rank #236 Confirmed

Summary

  • They share 3 benchmarks with published results for both. GLM-4.6 scores higher in 4 categories and Qwen3.5-9B in 1 category; 4 gaps are clear of the uncertainty.
  • The widest gap is in agentic & tool use, where GLM-4.6 leads 32.3 to 14.5.
  • The biggest single-benchmark swing is Terminal-Bench: 24.5% for GLM-4.6 and 9.2% for Qwen3.5-9B.
  • Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • Qwen3.5-9B accepts more context: 262K tokens versus 205K.

Side by side

GLM-4.6Qwen3.5-9B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.433.8
Released2025-09-302026-02-23
WeightsOpenOpen
Context window205K262K
Max output131K66K
Input $ / M tokens$0.60$0.10
Output $ / M tokens$2.20$0.15
Results tracked2910

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Qwen3.5-9B: 35.9 (#217)

BenchmarkGLM-4.6Qwen3.5-9B
SciCode38.4%27.5%
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1340—
LMArena Coding1449—
ALE-Bench340.82—

Agentic & Tool Use GLM-4.6 leads

GLM-4.6: 32.3 (#66), Qwen3.5-9B: 14.5 (#151)

BenchmarkGLM-4.6Qwen3.5-9B
Terminal-Bench24.5%9.2%
Berkeley Function Calling Leaderboard72.4%—

Reasoning Too close to call

GLM-4.6: 23.7 (#172), Qwen3.5-9B: 23.1 (#182)

BenchmarkGLM-4.6Qwen3.5-9B
CritPt1.1%0.3%
Kagi LLM Benchmark47.4%—
Chess Puzzles—12%
LMArena Hard Prompts1440—
DTBench—71.2%
LMCA—24.5%
Epoch Capabilities Index—139.46

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), Qwen3.5-9B: 34.8 (#192)

BenchmarkGLM-4.6Qwen3.5-9B
MathArena Final-Answer Competitions—48.5%
OTIS Mock AIME 2024-2025—61.7%
LMArena Math1432—
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge Qwen3.5-9B leads

GLM-4.6: 40.2 (#124), Qwen3.5-9B: 46.0 (#84)

BenchmarkGLM-4.6Qwen3.5-9B
GPQA Diamond—79%
Vectara Hallucination Rate9.5%—
LMArena Expert1431—

Multilingual Not comparable

GLM-4.6: 53.5 (#66), Qwen3.5-9B: —

BenchmarkGLM-4.6Qwen3.5-9B
LMArena Non-English1426—
LMArena Chinese1499—
LMArena French1459—
LMArena German1447—
LMArena Japanese1393—
LMArena Korean1400—
LMArena Russian1419—
LMArena Spanish1436—

Instruction Following Not comparable

GLM-4.6: 74.3 (#98), Qwen3.5-9B: —

BenchmarkGLM-4.6Qwen3.5-9B
LMArena Instruction Following1410—

Long Context Not comparable

GLM-4.6: 43.4 (#94), Qwen3.5-9B: —

BenchmarkGLM-4.6Qwen3.5-9B
LMArena Longer Query1422—

Writing & Preference Not comparable

GLM-4.6: 61.1 (#90), Qwen3.5-9B: —

BenchmarkGLM-4.6Qwen3.5-9B
LMArena Text1440—
LMArena Creative Writing1411—
EQ-Bench Creative Writing1411—
LMArena Multi-Turn1427—

Frequently asked questions

Is GLM-4.6 better than Qwen3.5-9B?

GLM-4.6 is the stronger model overall, scoring 41.4 to 33.8 on the Noometry Index. Qwen3.5-9B costs 8.9× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.

Is GLM-4.6 or Qwen3.5-9B better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 35.9 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5-9B does, with 262K tokens against 205K.

How many benchmarks do GLM-4.6 and Qwen3.5-9B share?

3 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3.5-9B has 10.

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