Noometry

Model comparison

GLM-4.6 vs Qwen3 8B

GLM-4.6 is the stronger model overall, scoring 41.4 to 33.7 on the Noometry Index. Qwen3 8B costs 3.2× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified . 4 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen3 8B Alibaba (Qwen)

33.7

Rank #238 Confirmed

Summary

  • They share 4 benchmarks with published results for both. GLM-4.6 scores higher in 6 categories and Qwen3 8B in 0 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-4.6 leads 23.7 to 16.6.
  • The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 42.6% for Qwen3 8B.
  • Qwen3 8B is cheaper at $0.18 / $0.70 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • GLM-4.6 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.6Qwen3 8B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.433.7
Released2025-09-302025-04
WeightsOpenOpen
Context window205K131K
Max output131K8K
Input $ / M tokens$0.60$0.18
Output $ / M tokens$2.20$0.70
Results tracked2911

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Qwen3 8B: 34.0 (#248)

BenchmarkGLM-4.6Qwen3 8B
SciCode38.4%22.6%
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 8B: 30.2 (#78)

BenchmarkGLM-4.6Qwen3 8B
Berkeley Function Calling Leaderboard72.4%42.6%
Terminal-Bench24.5%—

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), Qwen3 8B: 16.6 (#303)

BenchmarkGLM-4.6Qwen3 8B
CritPt1.1%0%
Kagi LLM Benchmark47.4%—
Chess Puzzles—5%
LMArena Hard Prompts1440—
DTBench—59.7%
LMCA—8.8%
Epoch Capabilities Index—136.17

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), Qwen3 8B: 34.9 (#191)

BenchmarkGLM-4.6Qwen3 8B
OTIS Mock AIME 2024-2025—56.1%
LMArena Math1432—
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), Qwen3 8B: 36.1 (#173)

BenchmarkGLM-4.6Qwen3 8B
Vectara Hallucination Rate9.5%4.8%
GPQA Diamond—56.8%
LMArena Expert1431—

Multilingual Not comparable

GLM-4.6: 53.5 (#66), Qwen3 8B: —

BenchmarkGLM-4.6Qwen3 8B
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 8B: —

BenchmarkGLM-4.6Qwen3 8B
LMArena Instruction Following1410—

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), Qwen3 8B: 37.9 (#210)

BenchmarkGLM-4.6Qwen3 8B
Fiction.LiveBench—62.1%
LMArena Longer Query1422—

Writing & Preference Not comparable

GLM-4.6: 61.1 (#90), Qwen3 8B: —

BenchmarkGLM-4.6Qwen3 8B
LMArena Text1440—
LMArena Creative Writing1411—
EQ-Bench Creative Writing1411—
LMArena Multi-Turn1427—

Frequently asked questions

Is GLM-4.6 better than Qwen3 8B?

GLM-4.6 is the stronger model overall, scoring 41.4 to 33.7 on the Noometry Index. Qwen3 8B costs 3.2× 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 8B?

Qwen3 8B is cheaper. It lists at $0.18 per million input tokens and $0.70 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Is GLM-4.6 or Qwen3 8B better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 34.0 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 131K.

How many benchmarks do GLM-4.6 and Qwen3 8B share?

4 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3 8B has 11.

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