DEVELOPER/Coding Questions - DeepSeek vs Qwen 2.5-Max

DeepSeek vs Qwen 2.5-Max. High level comparison for CODING related Prompts. Evaluated based on 1) Code Generation 2) Intelligent Answers to Open Ended Questions 3) Providing Information that is Upto Date Which one is better at code generation? Which model is better at giving more complete or technical answers to questions? # **DeepSeek vs. Quen LM 2.5 Max – Which AI Model is Better?** Hey everyone! In this video, I’m comparing **DeepSeek** and **Quen LM 2.5 Max**, two AI models that are emerging as strong competitors to ChatGPT and Claude. This is a **first impressions** comparison based on quick tests, not an in-depth benchmark. I'll be assessing both models on tasks related to **software development, AI reasoning, and real-world data accuracy.** ### **Comparison Criteria:** 🔹 Ability to generate code from **long, detailed requirements** 🔹 Answering **high-level, open-ended questions** in a human-like manner 🔹 Providing **real-world, up-to-date information** 🔹 Handling **simple code generation tasks** Instead of using a **web chat interface**, I tested these models by having them read **source files and pseudocode**, then complete missing sections. The generated code also had to pass **unit tests, including edge cases.** --- ## **Code Generation: DeepSeek vs. Quen LM 2.5 Max** ### **Quen LM 2.5 Max** ❌ **Doesn’t accept C# files** – I had to copy my pseudocode into a text file before uploading. 🐢 **Slower code generation** – The model took longer to display output. 🔴 **Failed one tricky edge case** – However, when I tested the same case in ChatGPT, it also failed. So, the issue might be with how the requirements were written. ### **DeepSeek** ✅ **Faster code generation** – Much quicker at producing code. ✅ **Accepts C# source files** – A big advantage over Quen. ✅ **Passed all unit tests immediately** – Including edge cases. 📌 **Final Code Generation Scores:** ⭐ **DeepSeek – 8/10** ⭐ **Quen LM 2.5 Max – 6/10** Personally, I prefer **uploading actual source files** rather than copying and pasting large text blocks. --- ## **Handling Open-Ended Software Development Questions** I asked both models a **complex, opinion-based question**: 🛠️ *Is Entity Framework a good enough abstraction, or should it be used with a repository pattern?* This is a tricky one because developers have strong, divided opinions. I expected the AI to either **hallucinate** or provide **long-winded, vague answers.** ### **Quen LM 2.5 Max** ✔️ Provided a **detailed explanation** of both approaches. ✔️ Mentioned **Domain-Driven Design (DDD)** – a good architectural insight. ❌ Didn’t convincingly explain why some developers prefer **just** Entity Framework. ## **Final Verdict – DeepSeek vs. Quen LM 2.5 Max** | Category | DeepSeek | Quen LM 2.5 Max | |----------------------------------|---------|---------------| | **Code Generation** | 8/10 | 6/10 | | **Handling Open-Ended Questions** | 7/10 | 7.5/10 | | **Beginner-Friendly Answers** | 6/10 | 7.5/10 | | **Realistic Learning Timelines** | 5/10 | 10/10 | | **Real-World Data Accuracy** | 10/10 | 7/10 | ### **Which AI Should You Use?** ✅ **For code generation:** Use **DeepSeek** – it's **faster** and more reliable. ✅ **For research and writing:** Use **Quen LM 2.5 Max** – it’s **better for high-level questions** and **structured answers.** Both models still have room for improvement. Interestingly, their **answers often feel strangely similar**, making me wonder if one has trained on data from the other. --- in the comments! If you enjoyed this breakdown: 👍 **Like the video** 🔔 **Subscribe for more AI tech reviews** 💬 **Drop your thoughts below!** Thanks for watching, and see you in the next one! 🚀 --- Let me know if you want any tweaks or additional sections! 😊 00:00 - Introduction 00:41 - Code Generation 02:08 - Entity Framework 04:14 - To Know List 06:08 - Effort to Progress 07:30 - Average Salary 08:25 - Final Verdict