Qwen3 235B A22B Thinking API | AIMLAPI (original) (raw)
Qwen3-Thinking excels in deep reasoning, multilingual processing, and large-context tasks (131K tokens), outperforming peers in benchmarks like MMLU (85.4%). Designed for scientific research, multilingual content, and enterprise analytics, it leverages massive-scale parameters for advanced cross-domain problem-solving.
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Qwen3 235B A22B Thinking
AI model optimized for multilingual reasoning, large-context analysis (131K tokens), and complex text-to-text tasks. Ideal for scientific research, enterprise analytics, and multilingual applications.
Qwen3-Thinking Description
Qwen3-Thinking is a cutting-edge text-to-text AI model optimized for complex reasoning, multilingual tasks, and large-context processing. Built on Alibaba Cloud’s advanced infrastructure, it excels in handling intricate workflows requiring deep analytical capabilities.
Technical Specification
Performance Benchmarks
- Context Window: 131K tokens
- Tasks: Text-to-text generation
- Architecture: Transformer-based, 235-billion parameters
Performance Metrics
Qwen3-Thinking boasts significant improvements in reasoning capabilities, excelling in areas like logic, math, and coding, and achieving state-of-the-art results. This version also exhibits enhanced general abilities, including instruction following and text generation. With its improved long-context understanding and extended thinking length, we strongly recommend using it for highly complex reasoning tasks.
Key Capabilities
- Complex Reasoning: Solves multi-step logical problems in mathematics, science, and analytics with high precision.
- Multilingual Proficiency: Fluent in 119 languages and dialects, including low-resource dialects.
- Large-Context Processing: Analyzes documents up to 131K tokens for summarization, knowledge extraction, and document synthesis.
- Tool Integration: Supports function calling and JSON output.
API Pricing
- Input: $0.299 per million tokens
- Output: $2.99 per million tokens
Code Sample
Comparison with Other Models
- Vs. Claude 4 Opus: Qwen3-Thinking focuses on high precision in solving complex tasks with long context, natively supporting a 256K token context window (expandable). Claude 4 Opus excels in coding accuracy and API automation, featuring a 200K token context and a top-tier 72.5% SWE-bench score, designed for stable performance in complex analytical and generative tasks.
- Vs. Gemini 2.5 Flash: Qwen3-Thinking stands out with excellent long-context support and agentic workflows, while Gemini 2.5 Flash is optimized more for speed and cost efficiency with a 128K token context and a 63.8% SWE-bench result.
- Vs. OpenAI o3-mini: Qwen3-Thinking focuses on accelerating agentic workflows and intelligent tool usage, whereas OpenAI o3-mini handles general-purpose tasks effectively, supports a 128K token context, and achieves 69.1% on SWE-bench, targeting a broader range of applications without deep agentic integration.
Limitations
Although Qwen3-Thinking offers outstanding capabilities in long-context processing and agentic task execution, it requires significant computational resources and specialized infrastructure for effective deployment. Like other large models with agentic architectures, it may face challenges when addressing especially novel or ambiguous tasks and benefits from human involvement for quality control, safety, and result correctness. The model’s high complexity can also lead to increased operational costs.
Qwen3-Thinking Description
Qwen3-Thinking is a cutting-edge text-to-text AI model optimized for complex reasoning, multilingual tasks, and large-context processing. Built on Alibaba Cloud’s advanced infrastructure, it excels in handling intricate workflows requiring deep analytical capabilities.
Technical Specification
Performance Benchmarks
- Context Window: 131K tokens
- Tasks: Text-to-text generation
- Architecture: Transformer-based, 235-billion parameters
Performance Metrics
Qwen3-Thinking boasts significant improvements in reasoning capabilities, excelling in areas like logic, math, and coding, and achieving state-of-the-art results. This version also exhibits enhanced general abilities, including instruction following and text generation. With its improved long-context understanding and extended thinking length, we strongly recommend using it for highly complex reasoning tasks.
Key Capabilities
- Complex Reasoning: Solves multi-step logical problems in mathematics, science, and analytics with high precision.
- Multilingual Proficiency: Fluent in 119 languages and dialects, including low-resource dialects.
- Large-Context Processing: Analyzes documents up to 131K tokens for summarization, knowledge extraction, and document synthesis.
- Tool Integration: Supports function calling and JSON output.
API Pricing
- Input: $0.299 per million tokens
- Output: $2.99 per million tokens
Code Sample
Comparison with Other Models
- Vs. Claude 4 Opus: Qwen3-Thinking focuses on high precision in solving complex tasks with long context, natively supporting a 256K token context window (expandable). Claude 4 Opus excels in coding accuracy and API automation, featuring a 200K token context and a top-tier 72.5% SWE-bench score, designed for stable performance in complex analytical and generative tasks.
- Vs. Gemini 2.5 Flash: Qwen3-Thinking stands out with excellent long-context support and agentic workflows, while Gemini 2.5 Flash is optimized more for speed and cost efficiency with a 128K token context and a 63.8% SWE-bench result.
- Vs. OpenAI o3-mini: Qwen3-Thinking focuses on accelerating agentic workflows and intelligent tool usage, whereas OpenAI o3-mini handles general-purpose tasks effectively, supports a 128K token context, and achieves 69.1% on SWE-bench, targeting a broader range of applications without deep agentic integration.
Limitations
Although Qwen3-Thinking offers outstanding capabilities in long-context processing and agentic task execution, it requires significant computational resources and specialized infrastructure for effective deployment. Like other large models with agentic architectures, it may face challenges when addressing especially novel or ambiguous tasks and benefits from human involvement for quality control, safety, and result correctness. The model’s high complexity can also lead to increased operational costs.
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