Qwen3 235B-A22B vs GLM 5.2 — Alibaba’s open flagship versus Zhipu’s. Below is the full side-by-side: specifications, API pricing, context window, local hardware requirements, and a clear, data-driven recommendation on which to pick.
| Spec | Qwen3 235B-A22B | GLM 5.2 |
|---|---|---|
| Developer | Alibaba | Zhipu AI |
| Type | LLM (MoE) | LLM (coding/agentic, MoE) |
| Parameters | 235B total / 22B active (MoE) | 744B total / ~40B active (MoE) |
| Context window | 128K | 1M |
| Modality | Text → Text | Text → Text |
| License | Apache 2.0 (open) | MIT (open) |
| Open weights | ✅ Yes | ✅ Yes |
| Input price ($/1M) | $0.45 | $1.4 |
| Output price ($/1M) | $1.8 | $4.4 |
| VRAM (4-bit) | ~140 GB | ~370 GB |
| Min GPU (local) | Multi-GPU or Mac 192GB | Multi-GPU server (e.g. 5× H100 80GB) |
| Released | 2025 | 2026-06 |
Key differences
- Cost: Qwen3 235B-A22B is 173% cheaper than GLM 5.2 on a blended-token basis.
- Context: GLM 5.2 wins on context window (1M vs 128K) — better for long documents, large codebases and big RAG inputs.
- Openness: both are open-weight, so either can be self-hosted or fine-tuned. Compare their VRAM needs above to see what your GPU can run.
- Run Qwen3 235B-A22B locally: ~~140 GB at 4-bit (min Multi-GPU or Mac 192GB).
- Run GLM 5.2 locally: ~~370 GB at 4-bit (min Multi-GPU server (e.g. 5× H100 80GB)).
Which should you choose?
Choose Qwen3 235B-A22B if you want the lower per-token cost for high-volume workloads.
Choose GLM 5.2 if you need the larger context window.
→ Estimate real costs in the API cost calculator · check local hardware in the VRAM calculator · browse all 30+ models.
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