Model comparison

GLM-5.3 vs Qwen2.5-Coder (1.5B)

GLM-5.3 has enough public results to be ranked (#25); Qwen2.5-Coder (1.5B) does not yet, so treat this comparison as directional.

Last verified . 1 shared benchmarks.

GLM-5.3 Z.ai (Zhipu)

54.6

Rank #25 Confirmed

Summary

  • They share 1 benchmark with published results for both.

Side by side

GLM-5.3 and Qwen2.5-Coder (1.5B) specifications
GLM-5.3Qwen2.5-Coder (1.5B)
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index54.6—
Released2026-08-142024-09-18
WeightsOpenOpen
Context window1M—
Max output131K—
Input $ / M tokens$1.40—
Output $ / M tokens$4.40—
Results tracked416

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Category by category

Coding Not comparable

GLM-5.3: 59.0 (#12), Qwen2.5-Coder (1.5B): —

Coding benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
DeepSWE69%—
FrontierCode40.1%—
LMArena WebDev1623—
FrontierSWE30.2%—
SciCode59%—
WeirdML75.4%—
LMArena Coding1499—
ALE-Bench1,317—

Agentic & Tool Use Not comparable

GLM-5.3: 36.3 (#34), Qwen2.5-Coder (1.5B): —

Agentic & Tool Use benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
APEX-Agents56.6%—
Vending-Bench 28,164—

Reasoning Not comparable

GLM-5.3: 45.2 (#42), Qwen2.5-Coder (1.5B): —

Reasoning benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
Epoch Capabilities Index155.56112.93
NYT Connections (extended)74.2%—
CritPt19.1%—
Chess Puzzles21%—
LMArena Hard Prompts1491—
Mystery Game Puzzles33%—
DTBench87.7%—
LMCA55.5%—
Bench to the Future 30.15—
HellaSwag—76.8%
WinoGrande—72.9%

Math Not comparable

GLM-5.3: 62.1 (#27), Qwen2.5-Coder (1.5B): —

Math benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
FrontierMath (Tiers 1-3)68.8%—
FrontierMath Tier 429.3%—
OTIS Mock AIME 2024-202591.1%—
ProofBench49%—
LMArena Math1493—
GSM8K—86.7%

Knowledge Not comparable

GLM-5.3: 58.3 (#34), Qwen2.5-Coder (1.5B): —

Knowledge benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
GPQA Diamond90.9%—
SimpleQA Verified41%—
LMArena Expert1512—
ARC (AI2) Challenge—60.9%
MMLU—68%

Multilingual Not comparable

GLM-5.3: 55.9 (#27), Qwen2.5-Coder (1.5B): —

Multilingual benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
LMArena Non-English1457—
LMArena Chinese1523—
LMArena French1499—
LMArena German1505—
LMArena Japanese1438—
LMArena Korean1478—
LMArena Russian1460—
LMArena Spanish1444—

Instruction Following Not comparable

GLM-5.3: 77.8 (#15), Qwen2.5-Coder (1.5B): —

Instruction Following benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
LMArena Instruction Following1481—

Long Context Not comparable

GLM-5.3: 45.5 (#32), Qwen2.5-Coder (1.5B): —

Long Context benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
LMArena Longer Query1485—

Writing & Preference Not comparable

GLM-5.3: 75.9 (#6), Qwen2.5-Coder (1.5B): —

Writing & Preference benchmarks
BenchmarkGLM-5.3Qwen2.5-Coder (1.5B)
LMArena Text1473—
LMArena Creative Writing1459—
EQ-Bench Creative Writing2075—
LMArena Multi-Turn1473—

Frequently asked questions

Is GLM-5.3 better than Qwen2.5-Coder (1.5B)?

GLM-5.3 has enough public results to be ranked (#25); Qwen2.5-Coder (1.5B) does not yet, so treat this comparison as directional.

How many benchmarks do GLM-5.3 and Qwen2.5-Coder (1.5B) share?

1 benchmark has published results for both models. GLM-5.3 has 41 scored results on Noometry and Qwen2.5-Coder (1.5B) has 6.

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