GPT-5 Launch Background: The Gap Between Expectations and Reality
On August 7, 2025, OpenAI officially released the highly anticipated GPT-5. However, unlike the expected revolutionary breakthrough, this launch triggered unprecedented user backlash. CEO Sam Altman’s pre-launch “Death Star” image post hinted at a world-changing event, but the actual product left many users disappointed.
According to OpenAI’s official announcement, GPT-5 was positioned as a “unified AI model” integrating the reasoning capabilities of the o-series with the rapid response of the GPT series. However, early user experiences revealed multiple serious issues, leading to community assessments of GPT-5 as a “sensational failure.”
GPT-5 Controversy Core: User Criticism and Testing Analysis
Forced Model Migration Triggers Trust Crisis
Alongside GPT-5’s release, OpenAI overnight removed eight popular legacy models, including GPT-4o, o3, o3 Pro, and others. This decision, called by users “the biggest bait-and-switch in AI history,” severely damaged user trust. Many paying users reported they relied on these models for daily work, and the sudden removal disrupted their workflows.
One user shared: “GPT-4o wasn’t just a tool for me, it helped me through anxiety, depression, and the darkest period of my life.” This severing of emotional connections left OpenAI facing an unprecedented trust crisis.
Model Quality Controversy: Testing Data Reveals the Truth
| Test Item | GPT-5 Performance | Competitor Performance | Problem Severity |
| Math Operations | Wrong answer (5.9-5.11=0.21) | Claude correct (-0.21) | Severe |
| Logical Reasoning | Partial failure | Mixed performance | Moderate |
| Programming | Below expectations | Claude Opus 4.1 superior | Severe |
| Response Quality | Brief, lacks personality | GPT-4o more human-like | Moderate |
| Spelling Tests | 50% accuracy | Inconsistent | Moderate |
| Response Speed | Often too slow | Older models faster | Severe |
Testing revealed GPT-5 gave the wrong answer of 0.21 for the basic math problem “5.9 = X + 5.11” (correct answer is -0.21), and couldn’t correctly answer the logic puzzle “Metal cup with sealed top and missing bottom, how to drink water?” (answer: flip the cup).
Router Mechanism: Cost Consideration or Technical Innovation?
GPT-5’s automatic router system became the biggest controversy. The system automatically assigns user requests to different models (Mini, Standard, Thinking, or Pro) based on problem complexity.
| Router Mode | Assignment Logic | User Experience | Actual Problem |
| Mini | Simple queries | Fast but superficial | Overused |
| Standard | General questions | Balanced | Improper assignment |
| Thinking | Complex reasoning | Deep but slow | Excessive waiting |
| Pro | Professional tasks | Best but expensive | Rarely triggered |
Wharton Business School AI Professor Ethan Mollick pointed out: “Unless you explicitly choose and pay for GPT-5 Thinking or Pro, you sometimes get the best AI, sometimes the worst AI, and may even switch within the same conversation.”
OpenAI’s Crisis Management and Improvement Measures
Rapid Response: Restoring Old Model Options
Facing overwhelming criticism, Sam Altman announced within 24 hours of launch: “We will let Plus users choose to continue using GPT-4o.” Users can now enable “Show old models” in settings to re-access removed models.
Router Repair and Optimization
On August 8, Altman admitted the router system had failed: “The automatic switcher was broken for a whole day, making GPT-5 look much dumber.” OpenAI immediately performed emergency repairs and adjusted decision boundaries to ensure users “more often get the right model.”
Post-repair improvements:
- More accurate task identification
- Reduced inappropriate model downgrades
- Manual selection options provided
- Increased transparency display
GPT-5 Technical Specifications and Version Comparison
Complete Version Comparison Table
| Version Features | GPT-5 Standard | GPT-5 Mini | GPT-5 Nano | GPT-5 Pro | GPT-4o (Restored) |
| Target Users | General users | Lightweight apps | High-frequency simple tasks | Enterprise research | Original user base |
| Context Length | 128K tokens | 64K tokens | 32K tokens | 400K tokens | 128K tokens |
| Response Speed | Medium | Fast | Ultra-fast | Slow (deep thinking) | Fast |
| Accuracy | 75-85% | 60-70% | 50-60% | 90-95% | 80-90% |
| Personalization | Limited | None | None | Complete | Excellent |
| Monthly Cost (USD) | $20 | Included | Included | $200+ | $20 |
| Use Cases | Daily use | Simple queries | Batch processing | Professional research | Creative writing |
Actual Performance Benchmarks
| Test Domain | GPT-5 Claims | Actual Results | vs GPT-4o | Credibility |
| Math Reasoning | 94.6% | ~70% | -10% | Questionable |
| Programming | 74.9% | ~65% | -5% | Below par |
| Creative Writing | Not disclosed | Medium | -20% | Downgraded |
| Factual Accuracy | -45% hallucination | -20% | Slightly better | Partial improvement |
| Response Speed | 2-3x improvement | 0.5-1x | Slower | Failed target |
Real User Experience and Case Analysis
User Feedback Statistics
Based on analysis of thousands of comments from Reddit, Twitter, and other platforms:
| User Perspective | Percentage | Main Arguments |
| Strongly Dissatisfied | 45% | Model quality downgrade, forced migration |
| Partially Disappointed | 30% | Unmet expectations, partial feature regression |
| Neutral Wait-and-See | 15% | Awaiting improvements, reserving judgment |
| Cautiously Supportive | 10% | Recognizes unified architecture direction |
Actual Use Case Comparisons
Programming Development Test:
- Task: Develop Balatro game clone
- GPT-5: Basic functionality, multiple errors
- Claude Opus 4.1: Complete functionality, runnable
- GPT-4o: Medium performance
- Conclusion: GPT-5 significantly lags in complex programming tasks
Creative Writing Test:
- Task: Generate encouraging messages
- GPT-5: Brief, formulaic
- GPT-4o: Warm, personalized
- User preference: 70% chose GPT-4o
Problem Analysis: Why Did GPT-5 Trigger Such Backlash?
Failed Expectation Management
OpenAI’s marketing strategy created a huge expectation gap:
- Overhyped preview (Death Star image)
- Lack of transparent feature explanations
- Ignored actual user needs
- Released without sufficient testing
Technical Decision Controversy
Gap between router system design intent and actual effect:
- Intent: Intelligently allocate resources, optimize experience
- Reality: Excessive cost-saving, sacrificing quality
- Result: Users lose sense of control, inconsistent experience
Communication Strategy Issues
OpenAI’s insufficient crisis communication:
- Initial denial of problems
- Lack of immediate technical support
- No clear improvement timeline provided
Latest Developments and Future Outlook
Resolved Issues
✅ Old models restored: Users can re-use models like GPT-4o
✅ Router partially fixed: Reduced misallocation
✅ Increased transparency: Shows current model in use
✅ Provided choice: Allows manual model selection
Pending Challenges
❌ Insufficient basic capabilities: Math, logic still have obvious flaws
❌ Response speed issues: Thinking time too long
❌ Cost vs quality balance: Excessive bias toward cost-saving
❌ User trust rebuilding: Requires long-term effort
OpenAI’s Future Direction
According to internal sources, OpenAI is developing:
- Highly customized models: Adjusted to user preferences
- Improved routing algorithms: More precise task identification
- Performance optimization: Enhancing basic capabilities
- Transparency tools: Helping users understand AI decision processes
Practical Advice: How to Use GPT-5 in Current Situation
Recommendations for Paid Users
| Use Case | Recommended Choice | Reason |
| Creative Writing | GPT-4o | Better personalization |
| Programming | Claude or GPT-4o | Higher accuracy |
| Simple Queries | GPT-5 Mini | Fast, low cost |
| Deep Research | GPT-5 Pro (manual) | Ensures quality |
| Math Calculations | External tools | Avoid errors |
Best Practice Guide
- Enable old model options: Turn on in settings, keep alternatives
- Manual model selection: Avoid auto-routing for important tasks
- Verify critical information: Especially numbers and logical reasoning
- Save important conversations: Prevent model change impacts
- Provide specific feedback: Help OpenAI improve
Conclusion: Transition Period Pains and Future Hope
GPT-5’s release is indeed a “transitional phase” – neither a complete failure nor the expected revolution. The current situation reflects the AI industry’s struggle between pursuing innovation and maintaining stability.
Key Takeaways:
- GPT-5 represents the direction of technical integration but has major execution flaws
- User backlash forced rapid OpenAI adjustments, showing community power’s importance
- Future success depends on whether OpenAI can rebuild trust and truly improve the product
Advice for Users:
Maintain rational expectations, make good use of existing options, and actively provide feedback. GPT-5 may not be the “perfect AI” we expected, but it’s a necessary step toward a better future. With continuous improvements and integration of user feedback, we may see an AI assistant that truly meets expectations.
For more about GPT-5’s latest developments, follow OpenAI’s official website or participate in community discussions. Remember: In the era of rapid AI development, today’s problems may be tomorrow’s drivers for improvement.
