Why I Use TRAE: Free LLMs, Stability, and 1M Token Context

My Main Reason for Using TRAE: Free Programming LLMs

Yes — the TRAE China version lets you try multiple large models for free. As shown in the screenshot, all of these models are available at no cost for trial. Here’s the full list of free models:

Doubao-Seed-2.0-Code、 Doubao-Seed-1.8、 Doubao-Seed-Code、 MiniMax-M2.7、 MiniMax-M2.5、 GLM-5.1、 GLM-5V-Turbo、 GLM-5、 DeepSeek-V4-Pro、 DeepSeek-V4-Flash、 Kimi-K2.6、 Kimi-K2.5、 Qwen3.6-Plus、 Qwen3.5-Plus、

But here’s the catch: when using these free models, you often need to wait anywhere from 1 to 10 minutes. In my experience, the average wait is around 3 minutes. But honestly — when you’re heading to bed or stepping away for a coffee, waiting 3–10 minutes is perfectly acceptable.

Another thing worth noting: TRAE also supports custom models. You can top up credits directly on DeepSeek’s official platform, or on Alibaba Cloud, then use your API key inside TRAE to call models. As shown below:

My Second Main Reason for Using TRAE: Fewer Freezes and Timeouts During Task Execution

When I previously used Copilot’s LLMs for AI coding, a recurring problem was the model getting stuck on a command, effectively blocking all subsequent tasks.

On TRAE, I encounter far fewer of these situations. Moreover, the entire workflow requires very few manual permission confirmations. This frees up my time and lets me run more tasks in parallel.

In fact, I’m currently juggling 4 projects simultaneously:

1. TRAE : rendering astronomical survey data into images.
2. GitHub Codespaces : an offline old-photo AI restoration tool built with C# and WPF on Windows.
3. Local VS 2026 IDE : a pet costume image generator built with C# and WPF — for example, dressing a puppy in a spacesuit or a kitten in a gothic dress.
4. Local VS 2026 IDE : deploying LLMs locally on Windows with C# and WPF, and benchmarking model performance across different GPUs and CPUs.

My Third Main Reason for Using TRAE: DeepSeek v4 Pro Supports a 1-Million-Token Context Window

I’ve observed that Claude Sonnet 4.6 and Opus 4.7 both show noticeable code quality degradation once the task context exceeds 168K tokens.

DeepSeek v4 Pro, by contrast, supports a 1-million-token context window. This allows it to maintain consistent code quality even when working on large-scale projects.

My Next Blog: Rendering Astronomical Survey Data into Images

I love astronomy. I love looking at images of the universe. That’s why I built this project. I hope to share it with you soon — I think you’ll enjoy it too.

Beyond Earth lie the stars and the vast cosmic ocean. That is the ultimate destination for humanity.

A Brief About Me

I’ve worked at NetEase Games, Baidu, Tencent (8 years), and Meituan (nearly 7 years), leading large-scale R&D projects and managing teams of 100+ engineers.

Now, I’m building an AI startup.

Why? The world runs on uncertainty — staying in corporate roles too long breeds addiction to certainty. Starting an AI venture is like setting sail into uncharted waters.

Feel free to reach out: mailto:[e-mail].