GPT-5.2's Unpopular Criticisms | Generated by AI
Question: What are unpopular ideas about GPT-5.2?
Answer:
GPT-5.2, released by OpenAI on December 11-12, 2025, as an incremental update in the GPT-5 series, has garnered mixed reactions. While it excels in certain benchmarks (e.g., long-context reasoning, coding, and professional tasks), several unpopular or critical opinions have emerged from users, developers, and observers shortly after launch:
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It’s overhyped and underwhelming as an incremental release: Many view it as a minor “.2” update rather than a groundbreaking leap, especially following the rushed “code red” response to Google’s Gemini 3. Critics argue it feels like cost-cutting or efficiency tweaks disguised as major progress, with performance gains not justifying the hype or price increase (40% higher API costs in some cases).
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Stricter safety guardrails and increased censorship: A common complaint is that GPT-5.2 is more “safetymaxxed,” refusing more queries or lecturing users on sensitive topics, making it less useful or enjoyable compared to predecessors like GPT-4o or competitors like Gemini 3 Pro, which handle controversial questions more openly.
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Disappointing in real-world tasks despite benchmark wins: It shines on specific evals (e.g., 100% on AIME 2025 math, high on SWE-Bench Pro for coding) but underperforms on others (e.g., SimpleBench, where it lags behind older models like Claude’s Sonnet). Users report it struggles with intuitive understanding, context retention, or practical workflows, feeling “colder,” less personality-driven, or prone to unnecessary clarifications.
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Not the best overall model: Unpopular among some enthusiasts is the claim that competitors like Anthropic’s Claude Opus 4.5 or Google’s Gemini 3 Pro are superior for everyday chat, frontend development, or balanced reasoning, with GPT-5.2 being “smarter” only in niche professional/agentic tasks but not the most reliable or enjoyable.
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Benchmark optimization over generalization: Critics suggest heavy RL (reinforcement learning) tuning for benchmarks has led to poor generalization, making it excel in tested scenarios but falter in novel or long-horizon problems.
These views contrast with OpenAI’s positioning of GPT-5.2 as ideal for economic-value tasks (e.g., spreadsheets, code, multi-step projects), but early user feedback highlights frustration over trade-offs in usability and freedom.
References:
- https://openai.com/index/introducing-gpt-5-2/
- https://arstechnica.com/information-technology/2025/12/openai-releases-gpt-5-2-after-code-red-google-threat-alert/
- https://www.theverge.com/ai-artificial-intelligence/842529/openai-gpt-5-2-new-model-chatgpt
- https://venturebeat.com/ai/gpt-5-2-first-impressions-a-powerful-update-especially-for-business-tasks
- https://mashable.com/article/openai-gpt-5-2-vs-google-gemini-3-how-they-compare