Qwen3.5-9B
VerifiedQwen3.5-9B processes text, image, and video with a 262k-token context.
About Qwen3.5-9B
Qwen3.5-9B was developed by Alibaba Qwen as an open-weight multimodal system. The model integrates text, image, and video modalities in a single architecture. Its 262144-token context window enables coherent handling of extended multimodal sequences.
Strengths include broad modality support and public weight availability for customization. Researchers and developers can inspect, fine-tune, or deploy the model without proprietary restrictions.
Typical usage covers video understanding, image-text reasoning, and long-form multimodal analysis. The open-weight release facilitates experimentation across academic and commercial projects requiring combined visual and textual processing.
Capabilities
How Qwen3.5-9B compares
Qwen3.5-9B (striped bar) vs other multimodal on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · Qwen3.5-9B ranks #5 of 122
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Extended Document and Media Analysis
The model processes up to 262144 tokens while performing multimodal understanding of images and video embedded in long inputs.
Multilingual Code Development
It generates and reasons about code across languages while following detailed instructions in extended contexts.
Long-Context Instruction Tasks
The model handles complex instruction following over very long sequences involving both text and visual data.
Strengths & limitations
Strengths
- +Very large 262k token context window
- +Native support for text, image, and video inputs
- +Efficient 9B parameter size for deployment
- +Strong coding and reasoning from Qwen lineage
Limitations
- –Smaller model size limits depth on complex tasks
- –Video processing can be resource-heavy
- –May lag behind larger models on nuanced reasoning
Pricing by provider
Live per-provider pricing & uptime, routed via OpenRouter. Prices are USD per 1M tokens.
| Provider | Input /1M | Output /1M | Context | Uptime |
|---|---|---|---|---|
| SiliconFlow(fp8) | $0.10 | $0.15 | 262K | 100.0% |
| DeepInfra(bf16) | $0.10 | $0.15 | 262K | 99.9% |
| Venice(fp8) | $0.10 | $0.15 | 256K | 100.0% |
| Together | $0.17 | $0.25 | 262K | 100.0% |
Cost calculator
Estimate what Qwen3.5-9B would cost for your usage.
Based on Qwen3.5-9B's $0.10/1M input · $0.15/1M 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/qwen3.5-9b",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: qwen/qwen3.5-9b
Editor's verdict
Qwen3.5-9B is Alibaba Qwen's open-weight multimodal with a 262K-token context window.
At $0.15 per 1M output tokens, it is very cost-efficient for its class, served by 4 providers.
As an open-weight model you can self-host it or call it through a hosted API.
Best suited to very large 262k token context window and native support for text, image, and video inputs.
Frequently asked questions
The model supports a context length of 262144 tokens.
User reviews
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Other Qwen models
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