Qwen3 Max (Preview) (non-reasoning)

Given that input tokens contribute to the overall cost, and verbose prompts can sometimes lead to more verbose outputs, optimizing your prompts is a key strategy.

• Be Direct and Concise: Formulate prompts that are clear, specific, and avoid unnecessary preamble.
• Provide Examples: Instead of lengthy instructions, use few-shot examples to guide the model's desired output format and length.
• Iterate and Refine: Test different prompt variations to find the most efficient way to achieve the desired result with minimal input tokens.

Qwen3 Max (Preview) is known for its verbosity. Since output tokens are priced higher, managing the length of the model's responses is critical for cost control.

• Explicitly Request Brevity: Include instructions like "be concise," "limit to X sentences," or "provide only the key points."
• Set Token Limits: Utilize API parameters to cap the maximum number of output tokens, preventing excessively long responses.
• Post-processing Summarization: For tasks where detailed output is initially needed but a shorter version is ultimately required, consider using a cheaper, faster model for summarization.

The model's higher latency and slower output speed make it less ideal for real-time, single-request applications. However, these characteristics can be mitigated through batching.

• Group Requests: Combine multiple independent prompts into a single API call if your application allows. This amortizes the latency overhead across several tasks.
• Asynchronous Processing: Design your system to handle responses asynchronously, allowing the model to process requests in the background without blocking user interactions.
• Schedule Non-Urgent Tasks: Prioritize real-time tasks for faster models and route less time-sensitive, intelligence-heavy tasks to Qwen3 Max (Preview) in batches.

Qwen3 Max (Preview) excels in intelligence. Focusing its use on tasks where this strength is paramount can justify its operational characteristics.

• Complex Analysis: Utilize it for deep data analysis, intricate problem-solving, or generating comprehensive research summaries.
• Creative Content Generation: Employ it for tasks requiring high creativity, nuanced language, or detailed storytelling where quality of output is prioritized over speed.
• Knowledge Extraction: Ideal for extracting specific, complex information from large, unstructured datasets where understanding context is key. Avoid simple Q&A or chat applications.

Qwen3 Max (Preview) stands out primarily for its exceptional raw intelligence, scoring 49 on the Artificial Analysis Intelligence Index. This places it among the top models for complex understanding, problem-solving, and generating nuanced, high-quality responses.

Its primary drawbacks are its notably slow output speed (36 tokens/s) and high verbosity (generating 14M tokens during evaluation). These factors can lead to increased processing times, higher costs due to more tokens, and reduced responsiveness in applications.

Qwen3 Max (Preview) offers competitive pricing with $1.20 per 1M input tokens and $6.00 per 1M output tokens. While these unit prices are favorable, its high verbosity means that total costs for verbose tasks can accumulate quickly.

It features a very large context window of 262k tokens. This allows the model to process and maintain understanding of extensive amounts of information within a single interaction, making it suitable for tasks involving long documents or complex dialogues.

Due to its relatively high latency (1.68 seconds for time to first token) and slow output speed, Qwen3 Max (Preview) is generally not ideal for real-time or highly interactive applications where immediate responses are critical for user experience.

Alibaba Cloud is the sole provider for Qwen3 Max (Preview). Users can access and integrate the model directly through Alibaba Cloud's platform and services.