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GLM 5.2 vs Qwen2.5 Coder 32B Instruct

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GLM 5.2 and Qwen2.5 Coder 32B Instruct.

Zhipu AI

GLM 5.2

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...

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Alibaba

Qwen2.5 Coder 32B Instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...

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Technical Specifications

SpecificationGLM 5.2Qwen2.5 Coder 32B Instruct
ProviderZhipu AIAlibaba
Context Window1,048,576 tokens128,000 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apiself hostable
Production Stabilitybetastable
API AvailableYesYes
Released Date2026-06-162024-11-11

API Pricing Comparison

Input Price per Million Tokens

GLM 5.2

$0.93

Qwen2.5 Coder 32B Instruct

$0.66

Output Price per Million Tokens

GLM 5.2

$3.00

Qwen2.5 Coder 32B Instruct

$1.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8950.0%vs8120.0%
GLM 5.2
Qwen2.5 Coder 32B Instruct
HumanEvalPython coding & logic synthesis
9120.0%vs9150.0%
GLM 5.2
Qwen2.5 Coder 32B Instruct
MATHComplex mathematical problem solving
8050.0%vs6800.0%
GLM 5.2
Qwen2.5 Coder 32B Instruct
GPQAGraduate-level expert reasoning
5350.0%vs4050.0%
GLM 5.2
Qwen2.5 Coder 32B Instruct
HellaSwagCommonsense reasoning and inference
8980.0%vs8400.0%
GLM 5.2
Qwen2.5 Coder 32B Instruct
MT-BenchMulti-turn conversation flow quality
930.0%vs885.0%
GLM 5.2
Qwen2.5 Coder 32B Instruct

GLM 5.2 Quirks & Gotchas

No developer gotchas reported.

Qwen2.5 Coder 32B Instruct Quirks & Gotchas

No developer gotchas reported.