i like deepseek and Qwen

Before May 2026, I had never used DeepSeek, Qwen 3.6 Plus, or any other Chinese LLMs for programming. As readers of my previous blog might recall, I primarily relied on GitHub Copilot’s models, favoring Claude Sonnet 3.6 and Claude Opus 4.7 (a bit pricey—if you’re wealthy, pretend I didn’t say that). My secondary choice was GPT Codex 5.3.

So when I first considered using DeepSeek or Qwen 3.6 Plus, I was skeptical—worried their code quality wouldn’t meet my standards.

I knew strategies like syntax/structure constraints and cross-model code reviews could mitigate risks, but I still wanted the base model’s capability to be as strong as possible.

First Steps with DeepSeek

I started by topping up credits on DeepSeek’s official platform. Proof below: 图1

Over the next few days, I intensively tested DeepSeek v4 Pro. To give you a clear picture, here’s my usage breakdown:

May 17, 2026

Cost: 18.28 RMB (≈ $2.53) Total tokens: 66,488,180 Input (cached): 61,606,016 Input (uncached): 4,193,347 Output: 687,817

May 20, 2026

Cost: 6.61 RMB (≈ $0.92) Total tokens: 38,690,681 Input (cached): 37,049,600 Input (uncached): 1,387,345 Output: 253,736

图2

If I maintain my recent high-intensity AI coding pace with DeepSeek v4 Pro: Daily cost: ~40 RMB (≈ $5.55); Monthly cost (30 days): ~1,200 RMB (≈ $166.50).

Is this cheap? Compared to Copilot Pro+ ($39/month for 1,500 premium requests, e.g., one Claude Sonnet 4.6 call), no.

But compared to Copilot’s post-June 2026 pricing (see my first blog), it’s a bargain.

Important Note:DeepSeek v4 Pro is currently 25% off until May 31, 2026 (see screenshot below). After June, prices will revert to standard rates. 图3

I’ll share updated billing data in a follow-up blog to track real-world costs post-discount.

What’s Next?

In my next post, I’ll analyze Qwen 3.6 Plus’s AI coding costs.

After that, I’ll dive into: 1、Token-saving strategies without sacrificing code quality. 2、Cost-cutting methods that don’t rely on reducing token usage. 3、Balancing affordability and reliability—how to save money while maintaining high code standards.

A Shoutout to Google Gemini

Today, I must praise Google Gemini. When I pasted an image asking for help, it returned a step-by-step guide image—truly impressive! 图4

About Me

I’ve worked at Tencent (8 years), Meituan (7 years), Baidu, and NetEase Games, leading large-scale R&D projects.

Now, I’m building an AI startup—because uncertainty fuels innovation, and corporate roles breed complacency.

Reach out: mailto: HummingbirdLabs@outlook.com.

Let’s discuss AI coding, cost optimization, or the future of LLMs.