Model comparison

GLM-4.5 vs Qwen3 14B

GLM-4.5 is the stronger model overall, scoring 42.0 to 35.5 on the Noometry Index. Qwen3 14B costs 1.6× less per token, which makes it the better buy when GLM-4.5's lead doesn't matter for your workload.

Last verified . 2 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 Confirmed

Qwen3 14B Alibaba (Qwen)

35.5

Rank #225 Confirmed

Summary

  • They share 2 benchmarks with published results for both. GLM-4.5 scores higher in 4 categories and Qwen3 14B in 1 category; 3 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-4.5 leads 28.6 to 18.5.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 57.9% for GLM-4.5 and 49.1% for Qwen3 14B.
  • Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.

Side by side

GLM-4.5 and Qwen3 14B specifications
GLM-4.5Qwen3 14B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index42.035.5
Released2025-07-272025-04
WeightsOpenOpen
Context window131K131K
Max output98K8K
Input $ / M tokens$0.60$0.35
Output $ / M tokens$2.20$1.40
Results tracked2712

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Category by category

Coding GLM-4.5 leads

GLM-4.5: 41.4 (#125), Qwen3 14B: 37.3 (#195)

Coding benchmarks
BenchmarkGLM-4.5Qwen3 14B
SWE-bench Verified (bash only)54.2%—
SciCode—31.6%
WeirdML40.6%—
LMArena Coding1434—
ALE-Bench344.82—
AlgoTune1.52—

Agentic & Tool Use Not comparable

GLM-4.5: —, Qwen3 14B: 29.6 (#83)

Agentic & Tool Use benchmarks
BenchmarkGLM-4.5Qwen3 14B
Berkeley Function Calling Leaderboard—41%

Reasoning GLM-4.5 leads

GLM-4.5: 28.6 (#100), Qwen3 14B: 18.5 (#280)

Reasoning benchmarks
BenchmarkGLM-4.5Qwen3 14B
Kagi LLM Benchmark57.9%49.1%
CritPt—0%
Chess Puzzles—4%
LMArena Hard Prompts1429—
DTBench—64%
LMCA—18.2%
Epoch Capabilities Index—138.23

Math Too close to call

GLM-4.5: 39.0 (#116), Qwen3 14B: 38.6 (#133)

Math benchmarks
BenchmarkGLM-4.5Qwen3 14B
OTIS Mock AIME 2024-2025—66.4%
LMArena Math1427—

Knowledge Qwen3 14B leads

GLM-4.5: 35.9 (#179), Qwen3 14B: 39.3 (#134)

Knowledge benchmarks
BenchmarkGLM-4.5Qwen3 14B
GPQA Diamond—63.8%
Humanity's Last Exam8.3%—
Confabulations11.3%—
Vectara Hallucination Rate—5.4%
LMArena Expert1433—

Multilingual Not comparable

GLM-4.5: 52.8 (#77), Qwen3 14B: —

Multilingual benchmarks
BenchmarkGLM-4.5Qwen3 14B
LMArena Non-English1417—
LMArena Chinese1465—
LMArena French1418—
LMArena German1407—
LMArena Japanese1415—
LMArena Korean1380—
LMArena Russian1414—
LMArena Spanish1454—

Instruction Following Not comparable

GLM-4.5: 74.1 (#104), Qwen3 14B: —

Instruction Following benchmarks
BenchmarkGLM-4.5Qwen3 14B
LMArena Instruction Following1404—

Long Context Too close to call

GLM-4.5: 38.2 (#201), Qwen3 14B: 38.1 (#204)

Long Context benchmarks
BenchmarkGLM-4.5Qwen3 14B
Fiction.LiveBench58.3%62.5%
LMArena Longer Query1412—

Writing & Preference Not comparable

GLM-4.5: 57.5 (#127), Qwen3 14B: —

Writing & Preference benchmarks
BenchmarkGLM-4.5Qwen3 14B
LMArena Text1430—
LMArena Creative Writing1395—
Short-Story Creative Writing73.4%—
EQ-Bench Creative Writing1343—
LMArena Multi-Turn1415—

Frequently asked questions

Is GLM-4.5 better than Qwen3 14B?

GLM-4.5 is the stronger model overall, scoring 42.0 to 35.5 on the Noometry Index. Qwen3 14B costs 1.6× less per token, which makes it the better buy when GLM-4.5's lead doesn't matter for your workload.

Which is cheaper, GLM-4.5 or Qwen3 14B?

Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.

Is GLM-4.5 or Qwen3 14B better for coding?

GLM-4.5 scores higher on coding benchmarks: 41.4 versus 37.3 in the Noometry coding category.

Which has the bigger context window?

Both accept 131K tokens.

How many benchmarks do GLM-4.5 and Qwen3 14B share?

2 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen3 14B has 12.

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