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 reas…
Model details →Qwen2.5 Coder 32B Instruct vs Z.ai: GLM 5
Z.ai: GLM 5 wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
GLM-5 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling l…
Model details →Side-by-side comparison
| Capability | Qwen2.5 Coder 32B Instruct | Z.ai: GLM 5 | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 33K | 80K | 🏆 Z.ai: GLM 5 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.66 | $0.72 | 🏆 Qwen2.5 Coder 32B Instruct |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $1.00 | $2.30 | 🏆 Qwen2.5 Coder 32B Instruct |
| Tool / function calling First-class support for emitting structured tool calls. | — | Yes | 🏆 Z.ai: GLM 5 |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | Yes | 🏆 Z.ai: GLM 5 |
Frequently asked questions
Is Qwen2.5 Coder 32B Instruct better than Z.ai: GLM 5?
Z.ai: GLM 5 wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between Qwen2.5 Coder 32B Instruct and Z.ai: GLM 5?
Input: $0.66 vs $0.72 per 1M tokens. Output: $1.00 vs $2.30 per 1M tokens.
What context windows do Qwen2.5 Coder 32B Instruct and Z.ai: GLM 5 support?
Qwen2.5 Coder 32B Instruct supports up to 33K tokens. Z.ai: GLM 5 supports up to 80K tokens.
Do both Qwen2.5 Coder 32B Instruct and Z.ai: GLM 5 support tool calling?
Qwen2.5 Coder 32B Instruct: not advertised. Z.ai: GLM 5: yes.