How to Use Qwen 3.5 Plus API with OpenClaw in 2026

OpenClaw is a popular open-source automation framework that lets you orchestrate AI models, APIs, and workflows through a declarative configuration. Pairing it with Qwen 3.5 Plus gives you access to Alibaba's most capable language model for coding, reasoning, and multilingual tasks -- all within your automated pipelines.

This guide walks through the complete setup, from configuration to production-ready examples.

Why Qwen 3.5 Plus for OpenClaw?

OpenClaw supports any OpenAI-compatible endpoint, making Qwen 3.5 Plus a drop-in replacement. Here is why it is a strong choice:

128K context window -- Process large documents, full codebases, or lengthy conversation histories in your automation chains
Strong coding performance -- Generate, review, and refactor code as part of CI/CD pipelines
Cost efficiency -- At $0.60/$3.60 per 1M tokens through Hypereal AI, it is one of the most affordable frontier-class models
Multilingual strength -- Handle content in 29+ languages without switching models

Prerequisites

Before you begin, make sure you have:

OpenClaw installed (pip install openclaw or via Docker)
An API key from Hypereal AI (free signup at hypereal.ai -- 35 free credits included)
Python 3.10+ or Node.js 18+

Step 1: Configure the Provider

Add Qwen 3.5 Plus as a provider in your OpenClaw configuration file:

# openclaw.yaml
providers:
  - id: qwen-3-5-plus
    type: openai-compatible
    config:
      base_url: "https://hypereal.tech/api/v1"
      api_key: "${HYPEREAL_API_KEY}"
      model: "qwen-3.5-plus"
      max_tokens: 4096
      temperature: 0.7

Set your API key as an environment variable:

export HYPEREAL_API_KEY="your-hypereal-api-key"

Step 2: Define Your Workflow

Here is a basic OpenClaw workflow that uses Qwen 3.5 Plus for code review:

# workflows/code-review.yaml
name: automated-code-review
description: Review pull request diffs with Qwen 3.5 Plus

steps:
  - name: fetch-diff
    action: git.diff
    config:
      base: main
      head: "{{ branch }}"

  - name: review-code
    action: llm.chat
    provider: qwen-3-5-plus
    input:
      system: |
        You are a senior software engineer performing a code review.
        Focus on: bugs, security issues, performance, and readability.
        Be specific and actionable in your feedback.
      user: |
        Review this diff and provide feedback:

        ```
        {{ steps.fetch-diff.output }}
        ```

  - name: post-comment
    action: github.comment
    config:
      body: "{{ steps.review-code.output }}"

Step 3: Python Integration

For more control, use OpenClaw's Python SDK with Qwen 3.5 Plus directly:

from openclaw import Pipeline, LLMStep
from openai import OpenAI

client = OpenAI(
    api_key="your-hypereal-api-key",
    base_url="https://hypereal.tech/api/v1"
)

# Define a reusable Qwen step
def qwen_step(prompt: str, system: str = "You are a helpful assistant.") -> str:
    response = client.chat.completions.create(
        model="qwen-3.5-plus",
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": prompt}
        ],
        max_tokens=4096,
        temperature=0.7
    )
    return response.choices[0].message.content

# Use in a pipeline
pipeline = Pipeline("content-generation")

# Step 1: Generate an outline
outline = qwen_step(
    prompt="Create a detailed outline for a blog post about WebAssembly in 2026.",
    system="You are a technical writer specializing in web development."
)

# Step 2: Expand each section
article = qwen_step(
    prompt=f"Expand this outline into a full article:\n\n{outline}",
    system="You are a technical writer. Write clear, detailed sections with code examples."
)

print(article)

Step 4: TypeScript Integration

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.HYPEREAL_API_KEY,
  baseURL: "https://hypereal.tech/api/v1",
});

interface WorkflowStep {
  name: string;
  prompt: string;
  system?: string;
}

async function runWorkflow(steps: WorkflowStep[]): Promise<Record<string, string>> {
  const results: Record<string, string> = {};

  for (const step of steps) {
    const response = await client.chat.completions.create({
      model: "qwen-3.5-plus",
      messages: [
        { role: "system", content: step.system ?? "You are a helpful assistant." },
        {
          role: "user",
          content: step.prompt.replace(
            /\{\{\s*(\w+)\s*\}\}/g,
            (_, key) => results[key] ?? ""
          ),
        },
      ],
      max_tokens: 4096,
    });

    results[step.name] = response.choices[0].message.content ?? "";
  }

  return results;
}

// Example: multi-step data processing workflow
const output = await runWorkflow([
  {
    name: "extract",
    prompt: "Extract all company names and revenue figures from this report: ...",
    system: "You are a data extraction specialist. Return structured JSON.",
  },
  {
    name: "analyze",
    prompt: "Analyze the following extracted data and identify trends:\n\n{{ extract }}",
    system: "You are a business analyst. Provide clear insights.",
  },
]);

console.log(output.analyze);

Advanced: Chaining Multiple Models

OpenClaw lets you chain different models in a single workflow. Use Qwen 3.5 Plus for reasoning and pair it with other models for specialized tasks:

# workflows/multi-model.yaml
name: smart-content-pipeline

steps:
  - name: research
    action: llm.chat
    provider: qwen-3-5-plus
    input:
      system: "You are a research analyst."
      user: "Summarize the latest developments in {{ topic }}."

  - name: generate-image-prompt
    action: llm.chat
    provider: qwen-3-5-plus
    input:
      system: "You are a prompt engineer for image generation models."
      user: "Based on this research, create a detailed image prompt:\n\n{{ steps.research.output }}"

  - name: generate-image
    action: image.generate
    provider: flux-pro
    input:
      prompt: "{{ steps.generate-image-prompt.output }}"

Pricing

When using Qwen 3.5 Plus through Hypereal AI:

	Input	Output
Per 1M tokens	$0.60	$3.60

This is 40% cheaper than the official Alibaba pricing ($1.00/$6.00). Every Hypereal account starts with 35 free credits.

Troubleshooting

Connection timeout: Make sure your base_url ends with /api/v1 and not /api/v1/chat/completions. The OpenAI SDK appends the path automatically.

Model not found: Use exactly qwen-3.5-plus as the model name in Hypereal. Other variants like qwen-plus or qwen3.5-plus will not work.

Rate limiting: The free tier has rate limits. For production workloads, upgrade to a paid plan at hypereal.ai.

Next Steps

Browse 200+ models in the Hypereal AI playground
Read the OpenClaw documentation for advanced workflow patterns
Explore streaming and function calling with Qwen 3.5 Plus

Try Hypereal AI free -- 35 credits, no credit card required.