Qwen3-235B-A22B

Advanced multilingual AI with 128K-token context, excelling in coding, reasoning, and enterprise applications.

Qwen 3 Model Description

Qwen3-235B-A22B, developed by Alibaba Cloud, is a flagship large language model leveraging a Mixture-of-Experts (MoE) architecture. With 235 billion total parameters and 22 billion active per inference, it delivers top-tier performance in coding, math, and reasoning across 119 languages. Optimized for enterprise tasks like software development and research, it’s accessible via AI/ML API.

Technical Specifications

Performance Benchmarks

Qwen3-235B-A22B uses a Transformer-based MoE architecture, activating 22 billion of its 235 billion parameters per token via top-8 expert selection, reducing compute costs. It features Rotary Positional Embeddings and Group-Query Attention for efficiency. Pre-trained on 36 trillion tokens across 119 languages, it uses RLHF and a four-stage post-training process for hybrid reasoning.

• Context Window: 32K tokens natively, extendable to 128K with YaRN.

• Benchmarks:

  • Outperforms OpenAI’s o3-mini on AIME (math) and Codeforces (coding).
  • Surpasses Gemini 2.5 Pro on BFCL (reasoning) and LiveCodeBench.
  • MMLU score: 0.828, competitive with DeepSeek R1.

• Performance: 40.1 tokens/second output speed, 0.54s latency (TTFT).

• API Pricing:

  • Input tokens: $0.21 per million tokens
  • Output tokens: $0.63 per million tokens
  • Cost for 1,000 tokens: $0.00021 (input) +$0.00063 (output) = $0.00084 total

Performance Metrics

Qwen3-235B-A22B comparison

Key Capabilities

Qwen3-235B-A22B excels in hybrid reasoning, toggling between thinking mode (/think) for step-by-step problem-solving and non-thinking mode (/no_think) for rapid responses. It supports 119 languages, enabling seamless global applications like multilingual chatbots and translation. With a 128K-token context, it processes large datasets, codebases, and documents with high coherence, using XML delimiters for structure retention.

• Coding Excellence: Outperforms OpenAI’s o1 on LiveCodeBench, supporting 40+ languages (Python, Java, Haskell, etc.). Generates, debugs, and refactors complex codebases with precision.
• Advanced Reasoning: Surpasses o3-mini on AIME for math and BFCL for logical reasoning, ideal for intricate problem-solving.
• Multilingual Proficiency: Natively handles 119 languages, powering cross-lingual tasks like semantic analysis and translation.
• Enterprise Applications: Drives biomedical literature parsing, financial risk modeling, e-commerce intent prediction, and legal document analysis.
• Agentic Workflows: Supports tool-calling, Model Context Protocol (MCP), and function calling for autonomous AI agents.
• API Features: Offers streaming, OpenAI-API compatibility, and structured output generation for real-time integration.

Optimal Use Cases

Qwen3-235B-A22B is tailored for high-complexity enterprise scenarios requiring deep reasoning and scalability:

• Software Development: Autonomous code generation, debugging, and refactoring for large-scale projects, with superior performance on Codeforces and LiveCodeBench.
• Biomedical Research: Parsing dense medical literature, structuring clinical notes, and generating patient dialogues with high accuracy.
• Financial Modeling: Risk analysis, regulatory query answering, and financial document summarization with precise numerical reasoning.
• Multilingual E-commerce: Semantic product categorization, user intent prediction, and multilingual chatbot deployment across 119 languages.
• Legal Analysis: Multi-document review for regulatory compliance and legal research, leveraging 128K-token context for coherence.

Comparison with Other Models

Qwen3-235B-A22B stands out among leading models due to its MoE efficiency and multilingual capabilities:

• vs. OpenAI’s o3-mini: Outperforms in math (AIME) and coding (Codeforces), with lower latency (0.54s TTFT vs. 0.7s). Offers broader language support (119 vs. ~20 languages).
• vs. Google’s Gemini 2.5 Pro: Excels in reasoning (BFCL) and coding (LiveCodeBench), with a larger context window (128K vs. 96K tokens) and more efficient inference via MoE.
• vs. DeepSeek R1: Matches MMLU performance (0.828) but surpasses in multilingual tasks and enterprise scalability, with cheaper API pricing.
• vs. GPT-4.1: Competitive in coding and reasoning, with lower costs and native 119-language support, unlike GPT-4.1’s English focus.