Why Some Users Felt Underwhelmed by ChatGPT-5
- Graziano Stefanelli
- 3 hours ago
- 3 min read
ChatGPT-5 arrived with towering expectations: deeper reasoning, a massive context window, and smoother multimodal performance. While the model brings real progress in factual accuracy, speed, and tool-use routing, a noticeable share of early adopters expressed disappointment. Their critiques revolve around perceived incremental gains, stricter guardrails, shifting tone, and higher usage costs.
Expectations versus reality
Anticipated leap in intelligence
Many power users expected GPT-5 to behave like a near-human problem-solver. In practice, gains over GPT-4o are tangible—especially in code reliability and citation accuracy—but not the dramatic jump some imagined. Complex logic puzzles, nested conditionals, and lengthy chains of mathematical reasoning still yield occasional missteps.
Creativity trade-offs
Alignment updates trimmed overt stylistic risk-taking. GPT-5’s prose is clearer and more factual, yet some storytellers and marketers describe the tone as flatter than GPT-4o’s “voice.” Users wanting quirky humor or lyrical flourish now coax the model with more aggressive style prompts.
Stricter safety filters
OpenAI strengthened refusal policies in GPT-5. Security researchers and advanced hobbyists report more blocked requests around penetration-testing code, deepfake detection, and certain medical workflows that GPT-4o previously handled. While enterprises applaud tighter guardrails, tinkerers see lost flexibility.
Pricing and access concerns
Plan | GPT-5 Access | Key Limits |
Free | Base GPT-5 with low daily cap | ~15-20 messages/day; slower during peak |
Plus – $20 / mo | GPT-5 (standard) + GPT-4o mini | ~200 messages/day; priority latency |
Pro – $200 / mo | GPT-5 Pro (more context + higher tool quotas) | 5 000+ messages, expanded API rate |
Many casual users balk at paying for Plus when GPT-5’s improvement over GPT-3.5 feels modest for light tasks. Professionals on Pro praise throughput but note that enterprise-class usage can double monthly costs compared to GPT-4o.
Mixed reactions to performance changes
Reasoning and context
GPT-5 boasts a 400 000-token window—triple GPT-4o’s limit—yet practical gains depend on prompt engineering. Long uploads still require chunking instructions, and latency rises for maximum-length inputs. Users expecting seamless “entire-book” Q&A sometimes confront timeouts or summary truncation.
Deep Research latency spike
During launch week, full-model Deep Research reports took 25 % longer than GPT-4o equivalents. A subsequent patch restored speeds, but the initial slowdown fueled skepticism.
Personality shift
OpenAI adjusted default temperature and safety settings, making replies more formal. Community forums surfaced complaints that GPT-5 felt “robotically polite.” OpenAI later re-introduced legacy GPT-4o personalities as selectable modes, but first impressions had already soured some fans.
Competition amplified disappointment
Feature | GPT-5 | Claude Opus 4.1 | Gemini 2.5 Pro | Perplexity Pro |
Maximum context | 400 K tokens | 200 K tokens | 1 M tokens (lab) | ~100 K tokens |
Default creativity | Balanced, conservative | Moderately expressive | Highly visual | Minimal |
Built-in citations | Deep Research optional | Inline by default | Workspace docs + web | Always on |
Guardrail strictness | High | Moderate | Moderate | Low |
Core strength | Ecosystem breadth, multimodal | Long PDF workflows | Workspace productivity | Fast fact retrieval |
Because rival models excel in niche areas—Claude for long documents, Gemini for Google apps, Perplexity for citation-first search—users naturally benchmarked GPT-5 against whichever competitor matched their use case, amplifying any perceived shortfall.
Where GPT-5 still shines
Multimodal fluency – Strong image and chart interpretation with minimal prompting.
Unified routing – Fast/deep/tool sub-modes optimize latency and cost automatically.
API ecosystem – Thousands of apps updated quickly to GPT-5 endpoints.
Enterprise reliability – Tighter guardrails, watermarking, and audit logs meet regulatory needs.
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ChatGPT-5 meaningfully advances accuracy, context length, and multimodal tooling, yet sky-high expectations and stricter alignment trade-offs made its debut feel less groundbreaking for some users. As AI capabilities approach diminishing-return territory, user sentiment hinges not only on raw improvements but on perceived value, creative freedom, and cost. Future iterations will need to balance safety with flexibility—and deliver clear, step-change utility—to rekindle the excitement that earlier ChatGPT versions inspired.
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