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Qwen 3.5 Flash

Qwen 3.5 Flash is Alibaba Cloud's production-hosted multimodal model built on a hybrid linear-attention MoE architecture, offering a context window of 1M tokens and sub-second responsiveness for high-throughput agentic workloads.

Vision (Image)File InputReasoningTool Use
index.ts
import { streamText } from 'ai'
const result = streamText({
model: 'alibaba/qwen3.5-flash',
prompt: 'Why is the sky blue?'
})

Playground

Try out Qwen 3.5 Flash by Alibaba Cloud. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

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Qwen 3.5 Flash

Providers

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider
Context
Max Output
Latency
Throughput
Input
Output
Cache
Web Search
Capabilities
ZDR
No Training
Release Date
Alibaba Cloud
Legal:Terms
Privacy
1M64K
1.1s
147tps
$0.10/M
$0.40/M
Read:$0.0/M
Write:$0.13/M
+1
02/24/2026
Throughput

P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.

Latency

P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.

Uptime

Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.

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About Qwen 3.5 Flash

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 When Choosing a Provider

  • Configuration: For latency-sensitive pipelines, compare time-to-first-token across available providers using the AI Gateway playground before committing to a routing configuration.
  • Zero Data Retention: AI Gateway supports Zero Data Retention for this model via direct gateway requests (BYOK is not included). To configure this, check the documentation.
  • Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

When to Use Qwen 3.5 Flash

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 when

  • 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

Conclusion

Qwen 3.5 Flash delivers Alibaba Cloud's fifth-generation multimodal reasoning at a cost point suited for production scale, with a context of 1M tokens that eliminates most RAG pipeline overhead. For teams building document-heavy or agentic applications on Vercel, it occupies the efficiency end of the Qwen3.5 lineup without sacrificing vision support.