Qwen Model Family
Developed by Alibaba Group's Qwen Team, Qwen is a family of state-of-the-art large language and multimodal models designed for comprehensive AI capabilities and multilingual performance. The latest Qwen3 generation features balanced model architectures including reintroduced Mixture-of-Experts (MoE) variants (Qwen3-30B-A3B and Qwen3-235B-A22B) alongside dense models up to 32B parameters, enabling efficient resource utilization through dynamic parameter activation. With support for 119 languages and dialects, hybrid thinking modes that seamlessly alternate between reasoning and instruction-following without model switching, and extended context windows (up to 1M tokens in Qwen3-2507), Qwen excels in tasks like multilingual understanding, code generation, agentic workflows, and complex problem-solving. The models utilize advanced Byte-level Byte Pair Encoding with a 151,646-token vocabulary, structured ChatML formatting for conversational interactions, and robust tool calling capabilities with parallel execution support. Available in both proprietary and open-weight versions with flexible licensing, comprehensive model variants (Base, Instruct, Thinking, and hybrid modes), and enhanced Model Context Protocol support, Qwen is ideal for developers seeking powerful, multilingual AI systems with sophisticated reasoning capabilities and minimal deployment complexity.
Featured Model: Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo
Qwen3-Coder-480B-A35B-Instruct is the Qwen3's most agentic code model, featuring Significant Performance on Agentic Coding, Agentic Browser-Use and other foundational coding tasks, achieving results comparable to Claude Sonnet.
Price per 1M input tokens
$0.30
Price per 1M output tokens
$1.20
Release Date
07/26/2025
Context Size
262,144
Quantization
fp4
# Assume openai>=1.0.0
from openai import OpenAI
# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
api_key="$DEEPINFRA_TOKEN",
base_url="https://api.deepinfra.com/v1/openai",
)
chat_completion = openai.chat.completions.create(
model="Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo",
messages=[{"role": "user", "content": "Hello"}],
)
print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)
# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
Available Qwen Models
Qwen series offers a comprehensive suite of dense and mixture-of-experts models.
| Model | Context | $ per 1M input tokens | $ per 1M output tokens | Actions |
|---|---|---|---|---|
| Qwen3-Coder-480B-A35B-Instruct-Turbo | 256k | $0.30 | $1.20 | |
| Qwen3-Coder-480B-A35B-Instruct | 256k | $0.40 | $1.60 | |
| Qwen3-235B-A22B-Thinking-2507 | 256k | $0.13 | $0.60 | |
| Qwen3-235B-A22B-Instruct-2507 | 256k | $0.13 | $0.60 | |
| QwQ-32B | 128k | $0.075 | $0.15 | |
| Qwen3-235B-A22B | 40k | $0.13 | $0.60 | |
| Qwen3-32B | 40k | $0.10 | $0.30 | |
| Qwen3-30B-A3B | 40k | $0.08 | $0.29 | |
| Qwen3-14B | 40k | $0.06 | $0.24 | |
| Qwen2.5-72B-Instruct | 32k | $0.12 | $0.39 | |
| Qwen2.5-Coder-32B-Instruct | 32k | $0.06 | $0.15 | |
| Qwen2.5-7B-Instruct | 32k | $0.04 | $0.10 |
FAQ
What is Qwen AI?
Are the Qwen models on Deepinfra optimized for low latency?
What's the difference between Qwen embeddings and rerankers?
What tasks are Qwen models best suited for?
- Multilingual understanding with native support for 119 languages and dialects
- Code generation & programming, with strengthened Model Context Protocol (MCP) support
- Optimized for AI agent applications with robust tool calling and parallel execution capabilities
- Complex reasoning tasks; hybrid thinking modes enable deep problem-solving and chain-of-thought reasoning
- Multimodal applications: Vision understanding combined with text processing for image analysis and description tasks
- Conversational AI with structured ChatML formatting for natural dialogue interactions
- Flexible system message support for various persona-based applications (role playing and character AI)
- Long-form content with extended context windows which support lengthy document processing and generation
How do I integrate Qwen models into my application?
What are the pricing details for using Qwen models on DeepInfra?
- Input Tokens: between $0.04 and $0.40 per million
- Output Tokens: between $0.10 and $1.60 per million
How do I get started using Qwen on DeepInfra?
- Get your API key
- Test models directly from the browser, cURL, or SDKs
- Review pricing on your usage dashboard
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