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35% Off All Qwen Models on Eden AI

Summarize this article with:

What Is Qwen?

Qwen is Alibaba’s open-weight LLM family and one of the most capable open-source AI model series in 2026. It is designed for developers who need strong models for reasoning, coding, vision, and multilingual applications.

One key feature is its hybrid thinking mode. This lets developers choose between faster answers for simple tasks and deeper reasoning for more complex work.

The Qwen family also includes specialized models like Qwen3-Coder for coding and Qwen2.5-VL for vision-language use cases. It supports 119 languages, and many Qwen models are available under the Apache 2.0 license, making them easier to use in production.

Qwen also benchmarks competitively against leading models like GPT-4o and Claude, especially across coding, reasoning, and multimodal tasks.

Which Qwen Models Are Available on Eden AI?

Eden AI gives developers access to a wide range of Qwen models, covering text generation, coding, vision, audio, and translation use cases. The main text and chat models include:

  • qwen-turbo-latest - Best for fast, low-cost, high-volume tasks.
  • qwen-plus - Best for balanced performance across general-purpose applications.
  • qwen3-max - Best for reasoning, complex prompts, and higher-quality outputs.
  • qwen3.7-max - Best for flagship long-context tasks with up to 1M tokens.
  • qwen3.6-flash - Best for fast long-context workflows.
  • qwen3.6-plus - Best for long-context tasks with a balance of speed and quality.
  • qwen3-coder-flash - Best for fast coding assistance and lightweight developer tasks.
  • qwen3-coder-plus - Best for advanced code generation, debugging, and technical workflows.
  • qwen3-coder-30b-a3b-instruct - Best for code generation with strong instruction-following.

All Qwen models are accessible through Eden AI’s unified API with a single API key.

Launch Offer: 35% Off All Qwen Models

To celebrate the launch of Qwen on Eden AI, all Qwen models in the Eden AI catalog are available with 35% off during the launch period. The discount applies automatically to every Qwen model, so developers can test reasoning, coding, vision, and lightweight chat workloads at a lower cost.

Model Standard Price Launch Price
Qwen3-235B-A22B $0.40 / 1M input tokens ~$0.26 / 1M input tokens 35% off
Qwen3-32B $0.18 / 1M input tokens ~$0.12 / 1M input tokens 35% off
Qwen-Turbo $0.05 / 1M input tokens ~$0.03 / 1M input tokens 35% off

Launch prices are approximate and based on the 35% discount applied during the launch period.

For example, a team spending $300/month saves over $100 every month during the promo period.

Why Use Qwen Through Eden AI?

Using Qwen through Eden AI gives developers more than model access - it makes integration, fallback, and team management easier.

  • One API key for Qwen and 50+ other providers
    Access Qwen alongside other leading AI providers without creating and managing separate accounts, credentials, and integrations.
  • OpenAI-compatible endpoint
    Plug Qwen into your existing stack with minimal code changes, especially if your application already uses an OpenAI-style request format.
  • Automatic fallback routing
    If a Qwen model is unavailable, you can automatically route requests to another model or provider without breaking your application.
  • Unified billing and usage dashboard
    Track usage, costs, and team consumption across Qwen and all other providers from one place.

How to Start Using Qwen on Eden AI

  1. Sign up or log in at edenai.co
    Create your Eden AI account or access your existing workspace.
  2. Select a Qwen model from the provider catalog
    Choose the Qwen model that fits your use case, from fast chat completion to coding, reasoning, or multimodal tasks.
  3. Call Qwen directly through the Eden AI API
    Send your request through Eden AI’s API endpoint and enjoy 35% off all Qwen models, applied automatically during the launch period.
import requests

url = "https://api.edenai.run/v3/chat/completions"
headers = {
    "Authorization": "Bearer <your-api-key>",
    "Content-Type": "application/json"
}
payload = {
    "model": "qwen/qwen3-vl-235b-a22b-thinking",
    "messages": [
        {
            "role": "user",
            "content": "Hello, Qwen!"
        }
    ]
}

response = requests.post(url, headers=headers, json=payload)

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