GPT-5 Nano vs Qwen-Max
Compare GPT-5 Nano and Qwen-Max. Find out which one is better for your use case.
Model Comparison
| Feature | GPT-5 Nano | Qwen-Max |
|---|---|---|
| Provider | OpenAI | Alibaba Cloud |
| Model Type | text | text |
| Context Window | 400,000 tokens | 32,768 tokens |
| Input Cost | $0.05 / 1M tokens | $1.60 / 1M tokens |
| Output Cost | $0.40 / 1M tokens | $6.40 / 1M tokens |
Strengths & Best Use Cases
GPT-5 Nano
1. Extremely fast performance
- Fastest model in the GPT-5 family.
- Great for real-time workflows, rapid responses, and high-throughput systems.
2. Most cost-efficient GPT-5 model
- Lowest input and output token costs.
- Suitable for large-scale or budget-sensitive applications.
3. Ideal for lightweight, well-scoped tasks
- Excels at summarization, classification, text extraction, and simple logic tasks.
- Best used when tasks are narrow and well-defined.
4. Multimodal input
- Accepts text + image as input.
- Outputs text only.
5. Broad tool support
- Supports Web Search, File Search, Image Generation (as a tool), Code Interpreter, and MCP.
- (Does not support Computer Use.)
Qwen-Max
1. Strong general-purpose reasoning
- Great for coding, analysis, creation, and multi-step tasks.
2. Stable commercial-grade model
- Predictable output quality and long-term stability.
3. Supports batch operations
- Batch inference is 50% cheaper.
4. Good for production agents
- Reliable instruction following and structured output.
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