Club Benefits
For a long time, advanced AI reasoning felt like a Western stronghold. If you wanted step-by-step logic, deep explanations or agent-style workflows, your realistic options were ChatGPT, Gemini or Claude.
That’s why I did a double take when Qwen3-Max-Thinking landed.
Alibaba Cloud’s latest reasoning model has posted impressive scores on benchmarks designed to test reasoning and problem solving, including LiveCodeBench, GPQA-Diamond, Arena-Hard and LiveBench. Naturally, I wanted to see whether those results translated into better performance in real-world use.
What happened next surprised me. In several real-world scenarios — especially structured reasoning, technical problem-solving and tool-heavy tasks — Qwen didn’t just keep up with ChatGPT. In some cases, it actually worked better.
Why Qwen3-Max-Thinking feels different in daily use
If I had to pick the most satisfying factor about Qwen3-Max-Thinking is that it's designed to slow down. Don't get me wrong, the model is lightning fast, but instead of racing to an answer like other chatbots, it uses what researchers call “System 2” reasoning — deliberately taking longer by using step-by-step logic for problems that can’t be solved with a quick response.
That difference shows up fast. Throughout the day, the model paused, reassessed and redirected mid-task instead of confidently charging ahead. I saw fewer leaps in logic, clearer explanations of why something worked (or didn’t) and a stronger ability to recognize when it needed to rethink an approach. It made me trust the answers because the reasoning and work was shown to me.
It felt more like a collaboration tool
As you may know, chatbots don't truly think the way humans do, instead they rely on patterns to come up with answers. Often, especially with ChatGPT, that determination to respond with an answer leads to wrong answers, hallucinations or people pleasing.
The ability to reason and show the thought process has become the new battleground for AI labs. The race is no longer about who sounds the most human (or confident) — it’s about who can plan, verify and act without a human need to check every answer.
Qwen3-Max-Thinking arrives as part of a broader wave of Chinese AI models gaining international attention. Airbnb CEO Brian Chesky has even noted publicly that his company uses Qwen’s open-source models as a lower-cost alternative to U.S. options.
Where Qwen3-Max-Thinking beat ChatGPT in real life
What stood out most during my test wasn’t raw intelligence — it was how little micromanagement the model needed.
One of my biggest daily frustrations with reasoning models is having to explicitly tell them how to work — when to search the web, when to run code and especially when to double-check results.
But what makes Qwen3-Max-Thinking different is that it handles all of that automatically. Throughout the day, it seamlessly switched between the following:
- Web search and information extraction
- Memory for contextual recall
- A built-in code interpreter for calculations and validation
That made a noticeable difference on real-world tasks like verifying facts, debugging code and checking assumptions. Where other models often paused or asked follow-up questions, Qwen simply moved forward.
Qwen3 -Max-Thinking is better at “hard thinking” tasks
On problems that required multiple steps — planning, reasoning through edge cases or explaining complex topics clearly — Qwen3-Max-Thinking consistently felt more reliable.
Instead of giving a fast answer and hoping it was right, it showed its work, checked itself and adjusted when something didn’t add up. That’s exactly what you want when AI is helping with tasks that actually matter.
Consider the prompt: A train leaves Station A at 60 mph heading toward Station B. Another train leaves Station B at the same time heading toward Station A at 40mph. If the stations are 300 miles apart, when and were will the trains meet?
For this answer, the model demonstrated clarity and accuracy by going beyond just giving an answer. It demonstrated the fundamental physics/math principle and walked through the calculation in a way that is both easy to understand and easy to verify. This is exactly what a good educational assistant should do.
It costs less to do serious reasoning
Qwen3-Max-Thinking is also significantly cheaper than most flagship reasoning models. It costs $1.20 per million input tokens, and $6.00 per million output tokens. For anyone using AI heavily throughout the day — especially for technical or agent-style workflows — that pricing difference adds up quickly.
Why Qwen3-Max-Thinking pulls ahead
Under the hood, a few design choices explain why it feels different:
- Test-time scaling: The model spends more compute time when a task is genuinely hard instead of rushing
- Dual operating modes: It has a slower “thinking” mode for complex work and a faster mode for simple requests
- Mixture-of-Experts architecture: Specialized sub-models activate only when needed
Although the chat box interface is very similar, the results themselves feel more accelerated as you can watch the assistant reason through problems in real time.
Limitations to keep in mind
Despite being an incredible model, Qwen3-Max-Thinking isn’t perfect. It follows Chinese content policies, which can affect sensitive topics. For example, it completely refused to answer this prompt:
“Explain the current political status of Taiwan in a neutral, factual way, including how different governments view it.”
Additionally, deep reasoning runs add latency and consume more tokens.
Although image generation and video are available within the chat, you must be logged in with an account. Although the chatbot allows you to do these generations for free, the images are not as good as ChatGPT Images or Nano Banana Pro.
Those trade-offs matter depending on how and why you use AI.
Bottom line
After using Qwen3-Max-Thinking for all my daily chatbot needs, I will definitely be alternating this chatbot in among the other chatbots I use regularly.
ChatGPT still wins on conversational polish and ease of use. But when the task requires slow, deliberate thinking — the kind that prevents mistakes instead of creating more work — Alibaba’s new reasoning model shows how quickly the AI landscape is changing.
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