Decoding the Latest GPT Trends: User Feedback, Model Comparison, and Practical Tips

Recently, discussions about GPT have been very active on X/Twitter, involving user preferences for models, performance comparisons, application techniques, and some interesting peripheral topics. This article will delve into these discussions, extract practical information, and help you better understand and use the GPT series of models.

GPT-4o Controversies and User Feedback

From the discussions, it can be seen that users have mixed reviews of GPT-4o. On the one hand, some users have expressed strong dissatisfaction with OpenAI's removal of some features from GPT-4o, believing that ChatGPT without 4o has lost its value, and calling on OpenAI to restore the feature. This reflects users' reliance on specific models and concerns about the decline in model performance.

On the other hand, OpenAI is facing financial pressure and the dilemma of fighting on multiple fronts, which may lead it to make adjustments in its product strategy, thereby affecting the user experience.

Insights:

• Pay attention to model updates: Keep up to date with updates and adjustments to GPT models so you can adjust your usage strategies accordingly.
• Back up important conversations: As model performance may change, it is recommended to back up important conversations and generated content to avoid losses due to model degradation.
• Try multiple models: Do not limit yourself to a single model, try using different models to meet different task requirements.

GPT Model Comparison and Selection

The discussion mentioned several GPT models, including GPT-4, GPT-5 (possibly referring to more advanced models), and AI models from other manufacturers, such as Claude Sonnet. These models differ in performance, applicable scenarios, and other aspects.

• Performance Comparison: It was pointed out that Claude Sonnet 4.6 (Max) scored the same as GPT 5.2 (xhigh) on the Artificial Analysis Intelligence Index, indicating that Claude Sonnet may have performance comparable to GPT in some aspects.
• Application Scenarios: Sider_AI recommends different models for different workflows:
  • Research: Claude Sonnet 4, Gemini 2.5 Pro
  • Writing: GPT-5, Claude Sonnet 4
  • Quick Tasks: GPT-5 mini, Gemini Flash
  • In-depth Analysis: GPT-5 Think, DeepSeek-R1
  • Presentation: Claude Sonnet 4, Gemini 2.5 Pro

Practical Tips:

1. Define task requirements: Before selecting a model, define your task type (e.g., research, writing, code generation).
2. Refer to performance indicators: Refer to AI performance evaluation indicators (such as the Intelligence Index mentioned above) to understand the model's performance in specific areas.
3. Try different models: Compare the performance of different models on the same task and choose the most suitable model.
4. Consider cost factors: Different models may have different pricing, choose the appropriate model based on your budget.

GPT Application Techniques and Cases

The discussion also involved some GPT application techniques and cases, demonstrating GPT's potential in different fields.

• Code Explanation and Learning: Using GPT to explain complex code can help understand code logic and learn new technologies. For example, use Claude to explain Karpathy's 200 lines of GPT code and learn concepts such as MoE, mlx lib, and freezing.
• Content Creation: GPT can be used to generate content such as articles, images, and NFTs. However, attention should also be paid to content quality and originality. Some users complain that the quality of articles generated by GPT is not high, and suggest using other models such as Grok 4.20.
• Image Processing: GPT can be used for image processing tasks such as image resizing and format conversion. Some users used GPT-5.3-Codex-Spark to resize 5 images in 1 minute and maintain perfect quality.
• Assisted Decision-Making: GPT can even be used for assisted decision-making, such as someone using ChatGPT for face reading.

Practical Tips:

• Precise Prompt: Writing clear and specific prompts can improve the output quality of GPT. You can try adding polite words such as "Please" & "thankyou" in the prompt. Some users believe that this can get better results (although this may only be a psychological suggestion).
• Iterative Optimization: Iteratively optimize the output of GPT, constantly adjust the prompt until you get satisfactory results.
• Combine with Professional Knowledge: Combine the output of GPT with your own professional knowledge for judgment and modification, and avoid blindly trusting the results of GPT.

Precautions and Potential Risks

The discussion also mentioned some precautions and potential risks of using GPT.

• Model Training Data: The content generated by consumer AI tools (ChatGPT Free/Plus/Pro, Claude Free/Pro/Max) may be used for model training and may be disclosed to relevant departments.
• Copyright Issues: The content generated using GPT may involve copyright issues, and attention should be paid to avoiding infringement.

Suggestions:

• Protect Personal Privacy: Avoid entering sensitive information into GPT to protect personal privacy.
• Respect Intellectual Property: When using content generated by GPT, pay attention to copyright issues and avoid infringement.
• Critical Thinking: Maintain critical thinking about the output of GPT and do not blindly trust it.

Summary

The discussions on X/Twitter reflect users' expectations and concerns about GPT models, as well as their continuous exploration of model performance and application scenarios. Understanding these discussions can help us better understand the latest developments of GPT, master practical usage skills, and avoid potential risks. The key is to pay attention to model updates, choose the right model, optimize prompts, and maintain critical thinking. Only in this way can we fully leverage the potential of GPT and bring convenience to our work and life.