Vision Qwen

Qwen 3.5 397B

Qwen 3.5 with 397B parameters (MoE)

397B
Parameters
33K
Context Window
3.5×
Credit Rate
Starter
Min Tier

Overview

Qwen 3.5 397B represents a significant leap in large-scale language modeling, designed specifically for complex reasoning and high-fidelity generation. Built on a Mixture of Experts architecture, this model leverages 397 billion parameters to deliver exceptional performance across coding, mathematical problem-solving, and nuanced natural language understanding. With a robust context window of 32,768 tokens, it handles extensive documentation and multi-turn conversations without losing coherence. The FP16 quantization ensures an optimal balance between precision and inference speed, making it suitable for demanding production environments where accuracy is paramount.

Available under the permissive APACHE-2.0 license, Qwen 3.5 397B offers developers unparalleled flexibility for integration and commercial deployment. On LLM Resayil, this model is accessible from the starter tier, ensuring broad availability for projects of all sizes. While the credit multiplier is set at 3.5x relative to the base rate, the trade-off delivers superior logical deduction and reduced hallucination rates compared to smaller variants. Choose this model when your application requires deep analytical capabilities and reliable output for critical workflows, maximizing the value of every token processed through our infrastructure.

Specifications

Display Name Qwen 3.5 397B
Family Qwen
Category Vision
Parameters 397B
Context Window 32,768 tokens
Quantization FP16
License APACHE-2.0
Min Tier Starter
Status Available

Pricing

3.5×
credits per token
1K 3,500 Credits
10K 35,000 Credits
100K 350,000 Credits
View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="qwen3.5:397b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)
const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "qwen3.5:397b",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);
curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "qwen3.5:397b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Complex reasoning and logical problem solving tasks
Advanced code generation and software debugging assistance
Detailed technical documentation and analysis report writing
Multi-step planning and strategic decision support systems
Scientific research summarization and data interpretation tasks

Related Models

Start building with Qwen 3.5 397B

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