GPT-5
Reddit take
AI summary of the Reddit excerpts below — not a quote
GPT-5 is generally characterized as a high-reasoning model that excels at technical tasks, coding, and multi-stage logic, though it introduces a significant shift in prompting methodology. While many users report high precision and native chain-of-thought capabilities, others are critical of its terse output style in non-technical fields and its tendency to stall or 'break down' when faced with contradictory instructions. Opinions are mixed on whether it represents a major leap over GPT-4o, with some users finding it slower or only marginally better in benchmarks.
Pros
- Native advanced reasoning and multi-stage chain-of-thought capabilities without needing explicit system instructions
- High precision in following complex instructions and structured prompts
- Strong performance in coding tasks, including HTML/CSS and responsive design
- Effective for web scraping and generating personalized educational quizzes
- Introduces structured tagging (HTML-like elements) for more granular prompt control
Cons
- Outputs can be overly terse, disjointed, or restricted to list/table formats for humanities-type questions
- Prone to processing 'breakdowns' or wasted power when instructions are contradictory
- Reports of significant latency, with simple changes sometimes taking minutes to process
- Limited parameter support (verbosity and temperature) when used through specific environments like Microsoft SK
- Perceived by some as a plateau in AI development rather than a major generational leap
Caveats
- Requires a shift in prompting strategy toward 'structured tags' (e.g., <context_gathering>) which some users dismiss as unnecessary bloat
- Reasoning performance is reported by some to be lower than other models like o3
- Model behavior may vary significantly depending on the platform or 'stack' it is integrated into
Best for
- Reasoning-heavy tasks and complex logic chains
- Coding, script generation, and technical troubleshooting
- Structured systems that benefit from HTML-style prompt tagging
Avoid if
- You require conversational, flowing prose for humanities or creative writing
- You are working within the Microsoft SK environment where control parameters are currently limited
- Your workflow involves prompts with potentially contradictory constraints
This summary uses balanced time weighting with about a 6-month half-life. The evidence comes from late 2025 with a median source age of approximately 383 days; because AI services change rapidly, this older data may not reflect the most current version of the model.
Sentiment
17 positive (61%) · 5 mixed (18%) · 6 negative (21%)
Trust + time weighted score: +41% · raw score 39%
What redditors said (10 of 28)
Use a gpt 5 based prompt builder to make the right prompt. Then let Claude improve it, then run it on the gpt 5 prompt optimizer.
Parent context: It actually is: it shows a perceptual bias (pattern recognition) and a corrective meta-cognitive feedback loop. That is an example, just not the usual “prompt-output” format most people here expect Reply: But again, you didn't show anything. You talked about it, but gave no actual concrete details and then randomly posted a single sentence that's supposed to make sense?
Here’s how I just trained GPT-5 to self-correct a perceptual bias in real time.
My first impression of GPT-5 is that its answers are way too terse. It often replies in list- or table-like formats, the flow feels disjointed, and it’s tiring to read. What’s more, even though it clearly has reasoning ability, it almost never reasons proactively on non-math, non-coding tasks—especially humanities-type questions.
My impression was that the reasoning of these models like gpt-5 take over a lot of the task of prompt engineering.
or is GPT-5 now so advanced that it doesn’t really matter how precisely I phrase things?
GPT-5 will have a breakdown if you give it contradictory instructions. While Claude would just follow the last thing it read, GPT-5 will literally waste processing power trying to reconcile "never do X" and "always do X" in the same prompt.
As far as I can tell, GPT-5 natively does CoT and advanced reasoning without the need for explicit instructions in the system prompt
Especially with reasoning-heavy models (e.g., GPT-5), a tight CI reduces waffle and compels decisions.
Same here, been running GPT-5 inside BlackboxAI and it’s actually been really good for me. Like when I need quick code fixes or small scripts, it usually just works right away.
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