Qwen3.5-27B
VerifiedProcesses long multimodal sequences across text, images, and video.
About Qwen3.5-27B
Built as an open-weight release, Qwen3.5-27B integrates vision and language capabilities into a single transformer architecture. Its 262144-token context window allows it to handle extended documents, multi-turn conversations, and lengthy video transcripts without truncation. The design emphasizes unified processing of text, still images, and video frames.
Strengths include coherent reasoning over mixed inputs and the flexibility of an openly available 27B-parameter checkpoint. Users can fine-tune or deploy the model locally for custom multimodal pipelines. Typical applications range from video summarization and image-grounded question answering to long-form document understanding that incorporates visual evidence.
Capabilities
How Qwen3.5-27B compares
Qwen3.5-27B (striped bar) vs other multimodal on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · Qwen3.5-27B ranks #44 of 122
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Long-form Video Analysis
The model processes extended video sequences alongside text and images to extract insights, summarize events, and answer detailed questions about content spanning hours of footage.
Multilingual Code Development
It generates, debugs, and explains code in multiple languages while following intricate technical specifications and maintaining coherence across very large codebases.
Cross-Modal Instruction Tasks
Users can issue complex multimodal instructions involving text, images, and video, with the model delivering accurate responses that integrate all input types over long contexts.
Strengths & limitations
Strengths
- +Very large context window
- +Native video input support
- +Strong general reasoning
- +Efficient 27B scale
Limitations
- –Video processing increases compute cost
- –May lag behind larger models on hardest tasks
- –Multimodal quality varies by input type
Cost calculator
Estimate what Qwen3.5-27B would cost for your usage.
Based on Qwen3.5-27B's $0.20/1M input · $1.56/1M output. Estimate only — actual cost varies by provider and caching.
Download & self-host Qwen3.5-27B
This is an open-weight model. Download the weights from Hugging Face or load it directly with Transformers.
# Install the Hugging Face CLI
pip install -U "huggingface_hub[cli]"
# Download the model weights
hf download Qwen/Qwen3.5-27B
# Or load it directly in Python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-27B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-27B", device_map="auto")Inference providers
Hosted APIs that serve Qwen3.5-27B (via Hugging Face Inference Providers).
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/qwen3.5-27b",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: qwen/qwen3.5-27b
Editor's verdict
Qwen3.5-27B is Alibaba Qwen's open-weight multimodal with a 262K-token context window.
At $1.56 per 1M output tokens, it is mid-priced for its class.
As an open-weight model you can self-host it (28B parameters) or call it through a hosted API.
Best suited to very large context window and native video input support.
Frequently asked questions
The model supports a context length of 262144 tokens, enabling processing of lengthy documents, conversations, or video transcripts in a single pass.
User reviews
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Other Qwen models
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