Qwen3.5-Flash
VerifiedFast open-weight multimodal model for million-token text, image, and video tasks.
About Qwen3.5-Flash
Qwen3.5-Flash combines transformer-based architecture with specialized encoders for visual and video data. Its design prioritizes speed while maintaining support for extremely long input sequences across multiple modalities. The open-weight release allows full local deployment and fine-tuning.
Key strengths include native handling of mixed text, image, and video content without external preprocessing pipelines. The large context window enables analysis of extended documents, full-length videos, or complex multi-image conversations in a single pass. This makes it suitable for tasks requiring broad contextual understanding.
Typical usage covers video summarization, long-form document understanding with embedded visuals, and interactive multimodal chat systems. Developers integrate it into applications needing both high throughput and extensive context retention.
Capabilities
How Qwen3.5-Flash compares
Qwen3.5-Flash (striped bar) vs other multimodal on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · Qwen3.5-Flash ranks #6 of 102
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Long Document Analysis
Handles reasoning across full-length reports or research papers thanks to its 1M token context window.
Video Understanding Tasks
Processes and interprets video content for summarization or event detection using multimodal vision capabilities.
Multilingual Code Projects
Generates and reviews code while supporting multiple languages in a single workflow.
Strengths & limitations
Strengths
- +Handles 1M token contexts
- +Native image and video support
- +Fast inference as Flash variant
- +Strong reasoning and coding performance
Limitations
- –Speed may trade off peak accuracy
- –Video handling constrained by compute
- –Less depth than larger non-Flash models
Cost calculator
Estimate what Qwen3.5-Flash would cost for your usage.
Based on Qwen3.5-Flash's $0.07/1M input · $0.26/1M output. Estimate only — actual cost varies by provider and caching.
Quick start
OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. Only the model slug changes between models.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
});
const completion = await client.chat.completions.create({
model: "qwen/qwen3.5-flash-02-23",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: qwen/qwen3.5-flash-02-23
Editor's verdict
Qwen3.5-Flash is Alibaba Qwen's open-weight multimodal with a 1000K-token context window.
At $0.26 per 1M output tokens, it is very cost-efficient for its class.
As an open-weight model you can self-host it or call it through a hosted API.
Best suited to handles 1m token contexts and native image and video support.
Frequently asked questions
The model supports a context length of 1,000,000 tokens.
User reviews
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Other Qwen models
Sibling versions in the Qwen family from Alibaba Qwen.
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