Qwen3.5-Flash vs GLM-4.7-FlashX
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Qwen3.5-Flash | GLM-4.7-FlashX |
|---|---|---|
| Provider | Alibaba | Zhipu AI |
| Input Price (1M) | $0.10 | $0.07 |
| Output Price (1M) | $0.40 | $0.40 |
| Context Window | 1,000,000 | 202,752 |
Verdict
Qwen3.5-Flash costs $0.10 per 1M input tokens and $0.40 per 1M output tokens. GLM-4.7-FlashX costs $0.07 per 1M input tokens and $0.40 per 1M output tokens. GLM-4.7-FlashX is 30% cheaper on input tokens than Qwen3.5-Flash. For output tokens, Qwen3.5-Flash is the more affordable option at $0.40/1M vs $0.40.
On context window, Qwen3.5-Flash supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Qwen3.5-Flash
- ✓ You need a larger context window (1,000,000 tokens)
- ✓ You are already integrated with Alibaba
When to choose GLM-4.7-FlashX
- ✓ You need the lowest input token cost ($ 0.07/1M)
- ✓ You are already integrated with Zhipu AI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is Qwen3.5-Flash cheaper than GLM-4.7-FlashX? ▼
GLM-4.7-FlashX is cheaper on input tokens at $0.07/1M vs $0.10/1M for Qwen3.5-Flash — a 30% saving.
What is the context window of Qwen3.5-Flash vs GLM-4.7-FlashX? ▼
Qwen3.5-Flash has a 1,000,000-token context window. GLM-4.7-FlashX has a 202,752-token context window. Qwen3.5-Flash supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Qwen3.5-Flash or GLM-4.7-FlashX? ▼
The best choice depends on your use case. For cost efficiency on input tokens, GLM-4.7-FlashX is the cheaper option. For maximum context length, Qwen3.5-Flash supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.