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GLM 4.7 Flash 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 4.7 Flash and Qwen2.5 Coder 32B Instruct.

Zhipu AI

GLM 4.7 Flash

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning,...

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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 4.7 FlashQwen2.5 Coder 32B Instruct
ProviderZhipu AIAlibaba
Context Window202,752 tokens128,000 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-01-192024-11-11

API Pricing Comparison

Input Price per Million Tokens

GLM 4.7 Flash

$0.06

Qwen2.5 Coder 32B Instruct

$0.66

Output Price per Million Tokens

GLM 4.7 Flash

$0.40

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
7720.0%vs8120.0%
GLM 4.7 Flash
Qwen2.5 Coder 32B Instruct
HumanEvalPython coding & logic synthesis
7850.0%vs9150.0%
GLM 4.7 Flash
Qwen2.5 Coder 32B Instruct
MATHComplex mathematical problem solving
4000.0%vs6800.0%
GLM 4.7 Flash
Qwen2.5 Coder 32B Instruct
GPQAGraduate-level expert reasoning
3100.0%vs4050.0%
GLM 4.7 Flash
Qwen2.5 Coder 32B Instruct
HellaSwagCommonsense reasoning and inference
8000.0%vs8400.0%
GLM 4.7 Flash
Qwen2.5 Coder 32B Instruct
MT-BenchMulti-turn conversation flow quality
810.0%vs885.0%
GLM 4.7 Flash
Qwen2.5 Coder 32B Instruct

GLM 4.7 Flash Quirks & Gotchas

No developer gotchas reported.

Qwen2.5 Coder 32B Instruct Quirks & Gotchas

No developer gotchas reported.