# Qwen 3.5 Flash

Qwen 3.5 Flash is the production-hosted counterpart of the Qwen3.5-35B-A3B open-source model, purpose-built for developers who need context of 1M tokens, native vision-language understanding, and fast latency without managing infrastructure.

- **Model ID:** `alibaba/qwen3.5-flash`
- **Type:** chat
- **Providers:** alibaba
- **Context window:** 1,000,000
- **Maximum output tokens:** 64,000
- **Pricing:** $0.1/1M input tokens, $0.4/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/qwen3.5-flash

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

const result = streamText({
  model: 'alibaba/qwen3.5-flash',
  prompt: 'Why is the sky blue?'
})
```

## About

Qwen 3.5 Flash is built on Alibaba Cloud's fifth-generation Qwen3.5 architecture, which combines Gated DeltaNet linear attention with sparse mixture-of-experts layers in a 3:1 linear-to-full attention ratio. This design allows the model to process very long documents and codebases efficiently while keeping inference costs low, the hosted Flash tier makes contexts of 1M tokens the default rather than an opt-in premium.

The model handles text, images, and video natively in a single forward pass, without requiring separate vision adapters. That native multimodality makes it well-suited for workflows that mix screenshot analysis, document review, and code generation in the same conversation. Structured outputs, tool calling, and seed-based reproducibility are all supported out of the box.

Qwen 3.5 Flash ships with configurable reasoning depth, letting callers dial up or down the amount of internal chain-of-thought the model performs before responding. At lower reasoning settings the model behaves like a fast instruction-follower; at higher settings it performs multi-step decomposition suitable for mathematical problem solving or complex agentic tasks.

## What to consider

For latency-sensitive pipelines, compare time-to-first-token across available providers using the AI Gateway playground before committing to a routing configuration.

## When to use

### Best For

- Processing entire codebases or long PDF reports in a single request, exploiting the default context of 1M tokens
- Agentic loops that require fast tool calls with structured JSON responses and low per-token cost
- Multimodal pipelines where text, screenshots, and short video clips arrive in the same conversation thread
- Latency-sensitive applications that need reasoning capability but can't tolerate the cost of the Plus tier

### Consider Alternatives When

- If your task demands maximum reasoning depth and cost is secondary, consider Qwen3.5 Plus for heavier analytical workloads
- Your application is text-only with no need for vision inputs and you want the absolute lowest token price
- You need image or video generation rather than understanding

## Best for

- **Whole-codebase and long-PDF processing:** Handling entire repositories or long reports in a single request using the default context of 1M tokens
- **Fast agentic tool loops:** Low-cost structured JSON responses for agents that chain many tool calls
- **Multimodal conversation threads:** Pipelines where text, screenshots, and short video clips arrive in the same thread
- **Latency-sensitive reasoning:** Applications that need reasoning capability but can't tolerate the cost of the Plus tier

## Consider alternatives

- **Maximum reasoning depth:** Consider Qwen3.5 Plus for heavier analytical workloads when cost is secondary
- **Lowest text-only pricing:** A dedicated text model is cheaper for pipelines that never need vision
- **Image or video generation:** This model understands multimodal inputs but doesn't generate images or video

## Frequently asked questions

### What architecture powers Qwen 3.5 Flash?

It uses a Gated DeltaNet plus sparse mixture-of-experts design with a 3:1 linear-to-full attention ratio, enabling efficient processing of very long sequences at lower compute cost than dense transformer models.

### Can Qwen 3.5 Flash analyze video clips?

Yes. The model natively accepts video inputs alongside text and images, allowing you to include short video segments in the same prompt as text instructions without preprocessing.

### How does the context of 1M tokens affect RAG architecture decisions?

For many document retrieval tasks the full context window eliminates the need for a separate vector search layer, since entire documents or codebases can be passed directly. However, chunking and retrieval still benefit latency and cost for very large corpora.

### Does Qwen 3.5 Flash support tool calling?

Yes. Tool calling, structured JSON outputs, and function-calling patterns are fully supported across all AI Gateway interfaces.

### What does the configurable reasoning parameter do?

Callers can adjust how much internal chain-of-thought computation the model performs before responding. Lower settings optimize for speed; higher settings improve accuracy on multi-step reasoning tasks at the cost of added latency.

### What is the difference between Qwen 3.5 Flash and Qwen3.5-Plus?

Flash is the cost-optimized, lower-latency variant built on the 35B-A3B architecture, while Plus is the higher-capability tier suited for more demanding reasoning and visual analysis tasks. Both share the context window of 1M tokens.

### Is Qwen 3.5 Flash suitable for production agentic workflows?

Yes. The model was specifically designed for agentic use: it supports adaptive tool use, structured outputs, and the long context required to maintain agent state across many tool-call turns.

## Links

- [Model page](https://vercel.com/ai-gateway/models/qwen3.5-flash)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://modelstudio.console.alibabacloud.com/ap-southeast-1/?tab=doc#/doc/?type=model&url=2840914_2&modelId=group-qwen3.5-flash)
- [Provider pricing](https://modelstudio.console.alibabacloud.com/ap-southeast-1/?tab=doc#/doc/?type=model&url=prices)