Overview

Qwen3.5 stands as a flagship thinking model within the Resayil ecosystem, engineered for complex reasoning and extended context processing. With 397 billion parameters operating at BF16 precision, this architecture delivers state-of-the-art performance on intricate technical tasks and multilingual workflows. The massive 262,144 token context window enables seamless analysis of extensive codebases or legal documents without fragmentation. Developers integrating this model gain immediate access to superior logical deduction capabilities, making it ideal for agentic workflows and deep research pipelines requiring high fidelity across both Arabic and English languages.

Integration is streamlined for immediate production deployment, allowing API builders to execute their first call within minutes using standard endpoints. The model maintains a 1x credit multiplier, offering enterprise-grade intelligence at a baseline cost structure suitable for scaling applications. Whether validating research hypotheses or deploying customer-facing assistants, Qwen3.5 provides the stability and linguistic nuance required for critical operations. Its robust alignment ensures reliable output quality, reducing the need for extensive post-processing while supporting high-volume throughput for demanding enterprise environments.

Specifications

Display Name qwen3.5

Family Qwen3.5

Category Thinking

Parameters 397B

Context Window 262,144 tokens

Quantization BF16

Min Tier Starter

Status Available

Pricing

1×

credits per 1,000 tokens

1K 1 Credits

10K 10 Credits

100K 100 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",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

import anthropic

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

message = client.messages.create(
    model="qwen3.5",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

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",
            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",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Reviewing long contracts for compliance issues and potential risks.

Generating complex software code across multiple files and languages.

Summarizing lengthy research papers into concise executive summaries.

Processing entire codebases for refactoring and bug identification.

Analyzing full books for thematic elements and character arcs.

In-Depth Guide

Full Guide

Complete Guide to qwen3.5 — LLM Resayil

qwen3.5