GPT-5.5 vs Qwen3-Max
GPT-5.5 (2026) and Qwen3-Max (2026) are frontier reasoning models from OpenAI and Alibaba. GPT-5.5 ships a 1M-token context window, while Qwen3-Max ships a 128K-token context window. On pricing, Qwen3-Max costs $0.78/1M input tokens versus $5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3-Max is ~541% cheaper at $0.78/1M; pay for GPT-5.5 only for coding workflow support.
Specs
Pricing and availability
| GPT-5.5 | Qwen3-Max | |
|---|---|---|
| Input price | $5/1M tokens | $0.78/1M tokens |
| Output price | $30/1M tokens | $3.9/1M tokens |
| Providers |
Capabilities
| GPT-5.5 | Qwen3-Max | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: GPT-5.5 and code execution: GPT-5.5. Both models share vision, multimodal input, function calling, and tool use, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, GPT-5.5 lists $5/1M input and $30/1M output tokens, while Qwen3-Max lists $0.78/1M input and $3.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-Max lower by about $10.78 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.
Choose GPT-5.5 when coding workflow support and larger context windows are central to the workload. Choose Qwen3-Max when vision-heavy evaluation and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, GPT-5.5 or Qwen3-Max?
GPT-5.5 supports 1M tokens, while Qwen3-Max supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, GPT-5.5 or Qwen3-Max?
Qwen3-Max is cheaper on tracked token pricing. GPT-5.5 costs $5/1M input and $30/1M output tokens. Qwen3-Max costs $0.78/1M input and $3.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.5 or Qwen3-Max open source?
GPT-5.5 is listed under Proprietary. Qwen3-Max is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, GPT-5.5 or Qwen3-Max?
Both GPT-5.5 and Qwen3-Max expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-5.5 or Qwen3-Max?
Both GPT-5.5 and Qwen3-Max expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run GPT-5.5 and Qwen3-Max?
GPT-5.5 is available on OpenAI API. Qwen3-Max is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.