GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency use cases such as classification, d…
Model details →OpenAI: GPT-5.4 Nano vs Qwen: Qwen3.5-Flash
Qwen: Qwen3.5-Flash wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the 3 series, these m…
Model details →Side-by-side comparison
| Capability | OpenAI: GPT-5.4 Nano | Qwen: Qwen3.5-Flash | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 400K | 1M | 🏆 Qwen: Qwen3.5-Flash |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.20 | $0.07 | 🏆 Qwen: Qwen3.5-Flash |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $1.25 | $0.26 | 🏆 Qwen: Qwen3.5-Flash |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | Yes | Yes | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | Yes | Yes | Tie |
Frequently asked questions
Is OpenAI: GPT-5.4 Nano better than Qwen: Qwen3.5-Flash?
Qwen: Qwen3.5-Flash wins on 3 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between OpenAI: GPT-5.4 Nano and Qwen: Qwen3.5-Flash?
Input: $0.20 vs $0.07 per 1M tokens. Output: $1.25 vs $0.26 per 1M tokens.
What context windows do OpenAI: GPT-5.4 Nano and Qwen: Qwen3.5-Flash support?
OpenAI: GPT-5.4 Nano supports up to 400K tokens. Qwen: Qwen3.5-Flash supports up to 1M tokens.
Do both OpenAI: GPT-5.4 Nano and Qwen: Qwen3.5-Flash support tool calling?
OpenAI: GPT-5.4 Nano: yes. Qwen: Qwen3.5-Flash: yes.