Noometry

Model comparison

GLM-4.6 vs Qwen3 14B

GLM-4.6 is the stronger model overall, scoring 40.9 to 35.0 on the Noometry Index. Qwen3 14B costs 1.6× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified . 5 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

40.9

Rank #132 Confirmed

Qwen3 14B Alibaba (Qwen)

35.0

Rank #222 Confirmed

Summary

  • They share 5 benchmarks with published results for both. GLM-4.6 scores higher in 5 categories and Qwen3 14B in 1 category; 4 gaps are clear of the uncertainty.
  • The widest gap is in long context, where GLM-4.6 leads 43.4 to 38.0.
  • The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 41% for Qwen3 14B.
  • Qwen3 14B is cheaper at $0.35 / $1.40 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 14B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index40.935.0
Released2025-09-302025-04
WeightsOpenOpen
Context window205K131K
Max output131K8K
Input $ / M tokens$0.60$0.35
Output $ / M tokens$2.20$1.40
Results tracked2912

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 39.9 (#137), Qwen3 14B: 36.7 (#191)

BenchmarkGLM-4.6Qwen3 14B
SciCode38.4%31.6%
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1339—
LMArena Coding1449—
ALE-Bench340.82—

Agentic & Tool Use GLM-4.6 leads

GLM-4.6: 32.1 (#60), Qwen3 14B: 29.4 (#78)

BenchmarkGLM-4.6Qwen3 14B
Berkeley Function Calling Leaderboard72.4%41%
Terminal-Bench24.5%—

Reasoning GLM-4.6 leads

GLM-4.6: 23.1 (#168), Qwen3 14B: 18.1 (#272)

BenchmarkGLM-4.6Qwen3 14B
Kagi LLM Benchmark47.4%49.1%
CritPt1.1%0%
Chess Puzzles—4%
LMArena Hard Prompts1440—
DTBench—64%
LMCA—18.2%
Epoch Capabilities Index—138.23

Math Too close to call

GLM-4.6: 36.4 (#115), Qwen3 14B: 37.0 (#102)

BenchmarkGLM-4.6Qwen3 14B
OTIS Mock AIME 2024-2025—66.4%
LMArena Math1431—
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge Too close to call

GLM-4.6: 39.6 (#118), Qwen3 14B: 39.2 (#124)

BenchmarkGLM-4.6Qwen3 14B
Vectara Hallucination Rate9.5%5.4%
GPQA Diamond—63.8%
LMArena Expert1432—

Multilingual Not comparable

GLM-4.6: 53.6 (#62), Qwen3 14B: —

BenchmarkGLM-4.6Qwen3 14B
LMArena Non-English1426—
LMArena Chinese1500—
LMArena French1459—
LMArena German1447—
LMArena Japanese1394—
LMArena Korean1401—
LMArena Russian1418—
LMArena Spanish1436—

Instruction Following Not comparable

GLM-4.6: 74.5 (#93), Qwen3 14B: —

BenchmarkGLM-4.6Qwen3 14B
LMArena Instruction Following1410—

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#91), Qwen3 14B: 38.0 (#201)

BenchmarkGLM-4.6Qwen3 14B
Fiction.LiveBench—62.5%
LMArena Longer Query1423—

Writing & Preference Not comparable

GLM-4.6: 61.3 (#86), Qwen3 14B: —

BenchmarkGLM-4.6Qwen3 14B
LMArena Text1440—
LMArena Creative Writing1411—
EQ-Bench Creative Writing1411—
LMArena Multi-Turn1427—

Frequently asked questions

Is GLM-4.6 better than Qwen3 14B?

GLM-4.6 is the stronger model overall, scoring 40.9 to 35.0 on the Noometry Index. Qwen3 14B costs 1.6× 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 14B?

Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Is GLM-4.6 or Qwen3 14B better for coding?

GLM-4.6 scores higher on coding benchmarks: 39.9 versus 36.7 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 14B share?

5 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3 14B has 12.

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