GPT-5.5 vs Qwen3.8-Max
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GPT-5.5 | Qwen3.8-Max |
|---|---|---|
| Provider | OpenAI | Alibaba |
| Input Price (1M) | $5.00 | $2.00 |
| Output Price (1M) | $30.00 | $6.00 |
| Context Window | 1,000,000 | 1,000,000 |
Verdict
GPT-5.5 costs $5.00 per 1M input tokens and $30.00 per 1M output tokens. Qwen3.8-Max costs $2.00 per 1M input tokens and $6.00 per 1M output tokens. Qwen3.8-Max is 60% cheaper on input tokens than GPT-5.5. For output tokens, Qwen3.8-Max is the more affordable option at $6.00/1M vs $30.00.
On context window, GPT-5.5 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GPT-5.5
- ✓ You are already integrated with OpenAI
When to choose Qwen3.8-Max
- ✓ You need the lowest input token cost ($ 2.00/1M)
- ✓ Your workload is output-heavy — Qwen3.8-Max generates text cheaper
- ✓ You are already integrated with Alibaba
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 GPT-5.5 cheaper than Qwen3.8-Max? ▼
Qwen3.8-Max is cheaper on input tokens at $2.00/1M vs $5.00/1M for GPT-5.5 — a 60% saving.
What is the context window of GPT-5.5 vs Qwen3.8-Max? ▼
GPT-5.5 has a 1,000,000-token context window. Qwen3.8-Max has a 1,000,000-token context window. GPT-5.5 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GPT-5.5 or Qwen3.8-Max? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Qwen3.8-Max is the cheaper option. For maximum context length, GPT-5.5 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.