Is AI slow because it overthinks? Meet the model that’s 2x faster by 'reducing thought'

Imagine this: You open your notebook to solve a math problem. But, being overly cautious, you deliberate for a full hour just to solve a single problem. While your chances of being wrong might decrease, it wouldn’t matter if you couldn’t even finish five problems before the exam ended.

The artificial intelligence (AI) industry has recently been grappling with a similar issue. To create smarter AI, models were encouraged to significantly increase their own thinking processes, but users were left frustrated by the sluggish response times. Recently, however, a new model has emerged that cleverly solves this problem: ‘Swift-Qwen3.8-27B’.

Why is this important?

As AI becomes smarter, response times tend to slow down. When solving complex logical problems, AI undergoes a self-directed ‘thinking time,’ and if this process is lengthy, users have to wait a long time to receive an answer.

The recently announced Swift-Qwen3.8-27B is a case that technically addresses this frustration. The fact that it maintains performance while enabling faster response times in our daily lives means AI can be utilized more broadly in practical work and everyday settings. For companies and individual developers in particular, this provides an attractive option that balances both speed and efficiency [Source 1, Source 5].

Easy to understand: What are AI ‘thought tokens’?

The term ‘thought tokens’ might sound a bit complex. Simply put, think of it as the process where the AI ‘mumbles’ to itself to organize its thoughts before speaking the final answer.

Just as a person might scribble notes and organize their thoughts while tackling a difficult problem, modern AI models also write out their reasoning process to review it themselves before delivering an answer.

• Traditional approach: The AI is so meticulous that it spends a lot of time writing down every minor thought.
• Swift-Qwen3.8-27B approach: It keeps only the essential reasoning and boldly trims away unnecessary tangential thoughts. By reducing this ‘mental clutter,’ the accuracy of finding the correct answer remains the same, but the speed has nearly doubled [Source 1, Source 6]!

To use an analogy, it’s like a smart student who used to write down every single mental arithmetic step while taking a test; now, they’ve been trained to skip those steps and write down only the core solution. The result—the ‘correct answer’—is the same, but the solving time is significantly shorter.

Current Status: How fast is it?

Swift-Qwen3.8-27B is a derivative model created by UkisAI based on the existing ‘Qwen3.8-27B’ model [Source 1, Source 5]. The performance improvement metrics for this model are quite impressive:

• Thought token usage: Reduced by a staggering 58.3%. In effect, the time the AI spends thinking has been cut by more than half [Source 1, Source 6].
• Speed improvement: As a result, it shows approximately 1.95x speed improvement across various tasks [Source 1].
• Performance maintenance: The most surprising part is that the performance loss is less than 1%. It’s as intelligent as before, but with a lighter frame.

For reference, the original model, Qwen3.8-27B, is an open-weight model capable of handling images and video, making it a versatile AI model [Source 10].

What’s next?

The trend in AI technology is shifting from “making unconditionally larger models” to “making more efficient and intelligent models.” Attempts like Swift-Qwen3.8-27B are likely to become the standard for increasing the efficiency of all future AI models.

For users, this means expecting higher-quality responses with less waiting. We are entering an era where ‘lag-free,’ intelligent AI assistants on your smartphone or computer can handle tasks faster than ever.

MindTickleBytes AI Reporter’s Perspective

If increasing performance is about ‘studying harder,’ then increasing efficiency is about ‘learning how to study smarter.’ The fact that AI has begun optimizing its own way of deliberation is a crucial change demonstrating that AI is evolving beyond a simple tool into an ‘intelligent operator.’

References

1. ukisai/Swift-Qwen3.8-27b · Hugging Face
2. ukisai/Swift-Qwen3.8-27B-GGUF · Hugging Face
3. ukisai/Swift-Qwen3.8-27b-BF16-AMD · Hugging Face
4. ukisai/Swift-Qwen3.8-27b-int4-AMD · Hugging Face

5. [Swift-Qwen3.8-27B: less overthinking

   UkisAI](https://ukisai.com/news/introducing-swift)

6. [Swift-Qwen3.8-27B Cuts Reasoning Tokens Without Sacrificing Much Accuracy

   HackerNoon](https://hackernoon.com/swift-qwen38-27b-cuts-reasoning-tokens-without-sacrificing-much-accuracy)

7. Qwen 3.8 27B Review: Reasoning Speed Tested - labforty.com
8. Qwen3.8 27B Reasoning Benchmarks: Off vs Low vs Medium vs Xhigh
9. Qwen3.8 27B: Benchmarks, Specs, and How to Run It (2026)
10. Qwen3.8-27B Complete Guide: Benchmarks, VRAM, vs Claude