Model comparison

GLM-5.3 vs Qwen2.5 14B Instruct

GLM-5.3 has enough public results to be ranked (#26); Qwen2.5 14B Instruct does not yet, so treat this comparison as directional.

Last verified . 0 shared benchmarks.

GLM-5.3 Z.ai (Zhipu)

54.8

Rank #26 Confirmed

Summary

  • The widest gap is in coding, where GLM-5.3 leads 59.5 to 37.7.
  • Qwen2.5 14B Instruct is cheaper at $0.35 / $1.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
  • GLM-5.3 accepts more context: 1M tokens versus 131K.

Side by side

GLM-5.3 and Qwen2.5 14B Instruct specifications
GLM-5.3Qwen2.5 14B Instruct
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index54.838.7
Released2026-08-142024-09
WeightsOpenOpen
Context window1M131K
Max output131K8K
Input $ / M tokens$1.40$0.35
Output $ / M tokens$4.40$1.40
Results tracked423

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Category by category

Coding GLM-5.3 leads

GLM-5.3: 59.5 (#14), Qwen2.5 14B Instruct: 37.7 (#191)

Coding benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
DeepSWE69%—
FrontierCode40.1%—
CursorBench42.6%—
LMArena WebDev1622—
FrontierSWE30.2%—
SciCode59%—
WeirdML75.4%—
BigCodeBench Instruct—39.8%
LMArena Coding1496—
BigCodeBench Complete—52.2%
ALE-Bench1,317—

Agentic & Tool Use Not comparable

GLM-5.3: 36.4 (#38), Qwen2.5 14B Instruct: —

Agentic & Tool Use benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
APEX-Agents56.6%—
Vending-Bench 28,164—

Reasoning Not comparable

GLM-5.3: 46.1 (#46), Qwen2.5 14B Instruct: —

Reasoning benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
NYT Connections (extended)74.2%—
CritPt19.1%—
Chess Puzzles21%—
LMArena Hard Prompts1489—
Mystery Game Puzzles33%—
DTBench87.7%—
LMCA55.5%—
Bench to the Future 30.15—
Epoch Capabilities Index155.61—

Math Not comparable

GLM-5.3: 62.3 (#33), Qwen2.5 14B Instruct: —

Math benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
FrontierMath (Tiers 1-3)68.8%—
FrontierMath Tier 429.3%—
OTIS Mock AIME 2024-202591.1%—
ProofBench49%—
LMArena Math1489—

Knowledge Not comparable

GLM-5.3: 58.3 (#37), Qwen2.5 14B Instruct: —

Knowledge benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
GPQA Diamond90.9%—
SimpleQA Verified41%—
LMArena Expert1516—
MMLU—79.9%

Multilingual Not comparable

GLM-5.3: 55.7 (#28), Qwen2.5 14B Instruct: —

Multilingual benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
LMArena Non-English1457—
LMArena Chinese1528—
LMArena French1499—
LMArena German1499—
LMArena Japanese1453—
LMArena Korean1472—
LMArena Russian1463—
LMArena Spanish1460—

Instruction Following Not comparable

GLM-5.3: 77.5 (#23), Qwen2.5 14B Instruct: —

Instruction Following benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
LMArena Instruction Following1477—

Long Context Not comparable

GLM-5.3: 45.4 (#41), Qwen2.5 14B Instruct: —

Long Context benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
LMArena Longer Query1482—

Writing & Preference Not comparable

GLM-5.3: 75.7 (#6), Qwen2.5 14B Instruct: —

Writing & Preference benchmarks
BenchmarkGLM-5.3Qwen2.5 14B Instruct
LMArena Text1471—
LMArena Creative Writing1457—
EQ-Bench Creative Writing2075—
LMArena Multi-Turn1472—

Frequently asked questions

Is GLM-5.3 better than Qwen2.5 14B Instruct?

GLM-5.3 has enough public results to be ranked (#26); Qwen2.5 14B Instruct does not yet, so treat this comparison as directional.

Which is cheaper, GLM-5.3 or Qwen2.5 14B Instruct?

Qwen2.5 14B Instruct is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.

Is GLM-5.3 or Qwen2.5 14B Instruct better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 37.7 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 131K.

How many benchmarks do GLM-5.3 and Qwen2.5 14B Instruct share?

0 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Qwen2.5 14B Instruct has 3.

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