GLM-4.7-Flash vs Qwen2.5-Coder-32B-Instruct
GLM-4.7-Flash (31.2B parameters) and Qwen2.5-Coder-32B-Instruct (32.8B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.
Side by side
| Fact | GLM-4.7-Flash | Qwen2.5-Coder-32B-Instruct |
|---|---|---|
| Parameters | 31.2B (Mixture-of-experts: 4 of 64 experts active per token (exact active-parameter count not stated on the model card)) | 32.8B |
| Architecture | Multi-head latent attention; mixture of 64 experts, 4 active per token | Grouped-query attention |
| Context length | 198K tokens (202,752) | 32K tokens (32,768) |
| License | MIT | Apache 2.0 |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 69.8 GB | 73.2 GB |
| VRAM needed, FP8 | 34.9 GB | 36.6 GB |
| VRAM needed, INT4 | 17.4 GB | 18.3 GB |
| Cheapest live fit, As published | A100 · $1.21/hr | A100 · $1.21/hr |
| Cheapest live fit, FP8 | L40 · $0.742/hr | L40 · $0.742/hr |
| Cheapest live fit, INT4 | RTX A5000 · $0.176/hr | RTX A5000 · $0.176/hr |
| KV cache per token (16-bit) | 53 KB | 256 KB |
| KV cache at 32k tokens | 1.65 GB | 8.00 GB |
| KV cache at 128k tokens | 6.61 GB | 32.0 GB |
VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.
Which to pick
- GLM-4.7-Flash needs less VRAM at its published precision (69.8 GB against 73.2 GB), so it fits on a smaller GPU.
- GLM-4.7-Flash lists the longer context window (202,752 tokens against 32,768).
- GLM-4.7-Flash caches less per sequence at 32k tokens (1.7 GB against 8.0 GB), leaving more memory for batching.
- Licenses differ: GLM-4.7-Flash is under MIT and Qwen2.5-Coder-32B-Instruct under Apache 2.0. Read both before commercial use.
These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.
Keep reading
- GLM-4.7-Flash: full VRAM table and live GPU fit
- Qwen2.5-Coder-32B-Instruct: full VRAM table and live GPU fit
- The GLM model series
- The Qwen model series
- All models that fit in 80 GB
Other comparisons
- GLM-4.7-Flash vs Qwen3-32B
- GLM-4.7-Flash vs Qwen3-30B-A3B
- GLM-4.7-Flash vs Qwen2.5-32B-Instruct
- GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507
- Qwen2.5-Coder-32B-Instruct vs NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
- Qwen2.5-Coder-32B-Instruct vs NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
- Qwen2.5-Coder-32B-Instruct vs granite-4.1-30b
