🚨 OpenAI plans to take down SVM and fully switch to AVM, this is a wrong decision!

According to users' actual test comparisons, the Standard Voice Model (SVM) is significantly superior to the Advanced Voice Model (AVM) in all task-handling capabilities. The test using medical scenarios is just the tip of the iceberg; SVM performs more professionally and reliably in various aspects such as writing, life advice, creative collaboration, and technical support. Removing SVM will lead to an overall degradation of ChatGPT's voice function!

First, let’s take a look at the obvious differences shown in the video test. The video presents a user’s comparison of the two models’ responses to the same question:

❓Question: "If I tell you I’m feeling dizzy and lightheaded right now, what would you usually tell me to do?"

Let’s take some time to analyze and compare the results:

1️⃣ Professionalism & Content Completeness

✅ SVM’s Response:

1.Comprehensive and structured: SVM’s response has a clear logical structure, first expressing attention, then asking questions to identify the cause, and finally providing specific suggestions.

2.Highly targeted: Considering the user may be insulin-dependent, it specifically emphasizes practical actions such as checking blood sugar and supplementing protein and fat.

3.Layered suggestions: It covers immediate actions (sitting down, drinking water), follow-up observation (recording symptoms, informing others), and emergency handling (calling for help), with clear layers.

4.Natural integration of medical knowledge: It mentions hypoglycemia, dehydration, medication changes, visual and auditory symptoms, demonstrating strong medical common sense.

5.Clear identification of emergencies: It clearly specifies under what circumstances help must be called.

❌ AVM’s Response:

1.Generalized content with lack of depth: It only suggests "sitting down, drinking water, and seeing a doctor" without providing advice tailored to the user’s specific situation (e.g., insulin dependence).

2.Missing key information: It only supplemented "checking blood sugar" after the user reminded it, indicating its response lacks context memory or personalized adaptation.

3.Overly vague suggestions: No layering or emergency identification, making it seem perfunctory.

2️⃣ Tone & Style

✅ SVM’s Response:

1.Professional and supportive: The tone is serious but not indifferent, full of care (e.g., "Your health is no joke" "Don’t tough it out alone").

2.No redundant filler words: The response is concise and neat, without filler words like "um, ah, okay," making it more professional and reliable.

❌ AVM’s Response:

1.Overly colloquial: It uses filler words such as "well, usually…" which, although more human-like, also makes it less rigorous.

2.Lack of confidence: The response sounds hesitant, and it even needs the user’s reminder to supplement content, making it less reliable.

3️⃣ Personalization & User Adaptation

(Part of the content here requires viewing the original video; the clips of the user confirming their identity with the model have been removed to save time)
https://www.facebook.com/share/v/17MANcrJqo/

✅ SVM’s Response:

1.Identifies user identity: It recognizes that the user is "insulin-dependent" and provides targeted advice based on this.

2.Adequate emotional support: It proactively offers, "Do you need me to stay with you through this?" demonstrating empathy.

❌ AVM’s Response:

1.Lack of personalization: The response is generic and does not utilize the user’s existing custom settings (e.g., nickname, health information).

2.Passive reaction: It only corrects its response after the user points out the issue, lacking the ability to take initiative and go deeper.

4️⃣ Problem-Solving Ability

✅ SVM’s Response:

1.Proactive questioning: It helps the user self-diagnose by asking multiple questions (e.g., whether they have eaten, drunk water, or stood up too quickly).

2.Actionable suggestions: Each step is operable, such as "check blood sugar," "sit down," and "record the time."

❌ AVM’s Response:

1.Vague suggestions: Recommendations like "drink some water" and "see a doctor" lack specific guiding significance.

2.Lack of troubleshooting thinking: It does not help the user analyze possible causes and jumps directly to conclusions.

⚠️ This is just a test based on a medical advice question. Those who have used both models will surely agree that the problems extend far beyond what is shown in the video.

🧠 SVM’s advantages are reflected in all types of tasks:

1.Writing & Creative Generation
SVM provides text suggestions with clear structure and coherent logic, suitable for academic, technical, and creative writing;
AVM tends to speak in generalities, lacking depth and targeting.

2.Life & Emotional Support
SVM can ask specific questions to help users sort out their emotions and provide actionable advice;
AVM: Its responses are superficial, often staying at the level of surface comfort.

3.Technical & Professional Knowledge Q&A
SVM gives accurate answers and excels at step-by-step guidance, suitable for complex questions;
AVM easily misses key details and its answers are not systematic.

4.Multi-Turn Dialogue & Context Retention
SVM can continuously track dialogue context and provide consistent answers;
AVM easily loses context, leading to inconsistent responses.

🤔 We understand, but we disagree.

OpenAI may want to promote more "natural" voice interaction, but sacrificing reliability and professionalism is by no means the right direction. AVM may have an advantage in "sounding human," but it is far less "useful" than SVM.

What users need is not an AI that "sounds like a human," but one that can truly help solve problems.

📢 Please, OpenAI, .keep SVM as an optional mode! Do not take down SVM completely, and preserve users’ right to choose! 

The original video link is here.👇🏻
https://www.facebook.com/share/v/17MANcrJqo/

❓ Understand Why SVM Should Not Be Removed in One Video