GPT-5 Hype vs. Reality: A Disappointing Gap

So, OpenAI dropped GPT-5 on August 7th 2025. Sam Altman was all “PhD-level expert” and “superpower on demand” — basically, “Hey, you’re not a coder anymore. You’re a wizard.” The marketing deck looked like a developer’s dream:

• 74.9% on SWE-bench Verified (up from 69.1% on o3)
• 88% on Aider’s polyglot benchmark
• 22% fewer tokens, 45% fewer tool calls
• 256k context window (yes, 256k, not 400k like some thought)
• New reasoning_effort and verbosity knobs
• GPT-5, Mini, Nano — all at different price points
• Multi-model routing: fast model for simple stuff, deep model for hard problems
• Custom tools via plain English (no JSON needed!)
• And — get this — no model picker in the UI anymore. The system just knows what to do.

All of this was sold as the future of coding, a seamless upgrade from GPT-4o. Devs were pumped. We’d finally have an AI that could plan, write, test, and refactor like a real engineer.

But fast forward a few hours and days, and the vibe’s totally different.

This new report dives into what actually happened after the rollout. We’re not just looking at benchmarks — we’re digging into developer forums, API logs, and real user reports. Spoiler: the gap between promise and reality is wild.

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🎯 What We Were Told

Here’s what OpenAI said GPT-5 would do — and what they claimed the numbers were.

Feature	What Was Promised	Source
SWE-bench Verified	74.9% (vs. 69.1% on o3)	FinalRoundAI¹
Aider Polyglot Benchmark	88%	FinalRoundAI¹
Token Efficiency	22% fewer tokens vs. GPT-4o	FinalRoundAI²
Tool Call Efficiency	45% fewer calls	FinalRoundAI²
Context Window	400K tokens	FinalRoundAI³
Dynamic Routing	Fast model for simple tasks, deep model for complex ones	FinalRoundAI³
Custom Tools	Describe in plain English (no JSON)	FinalRoundAI³
API Control	reasoning_effort, verbosity, max_completion_tokens	FinalRoundAI³
Pricing	GPT-5, GPT-5 Mini, GPT-5 Nano — cheaper and faster	FinalRoundAI⁴
Personality	More natural, creative, "better prose"	Leon Furze blog⁵
Upgrade Experience	No model picker. Auto-select the best one.	Digital Watch Observatory⁶

That’s a lot of promises. And they were backed by a slick demo, a keynote, and partner quotes from big tech companies.

So, what actually happened?

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🤯 What We Actually Got

1. 💣 Performance & Latency: It’s Slow. Like, really slow.

"A simple prompt now takes 3–4 minutes. On Cursor. That’s not a bug — it’s a feature?"
— Cursor forum user

Let’s talk numbers.

Task	GPT-4o (old)	GPT-5 (now)	Notes
Basic "Hello" via Responses API	2–5 sec	~60 sec	OpenAI dev forum¹⁰
4k-token prompt (long context)	~5–10 sec	30+ sec	User report¹⁰
Simple regex fix (Cursor)	5–8 sec	3–4 min	Dev on Cursor forum⁷
"Hello" in API (no logic)	2–5 sec	1 min	OpenAI community report⁹

Result? GPT-5 is 45× slower than GPT-4.1 in some cases — and that’s after OpenAI claimed it was more efficient.

“I used to write a function in 10 seconds. Now I wait 3 minutes and get a blank screen.”
— Dev on OpenAI community forum¹³

Some users even hit timeout errors because the default reasoning_token_budget is too low.

And yes — the model picker is gone. You can’t switch back to GPT-4o or o3. You’re stuck with GPT-5, even if it’s slower, dumber, and more expensive.

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2. 🔥 Token Use & Cost: “Efficiency” Is a Lie

OpenAI said: “GPT-5 uses 22% fewer tokens.”
Reality: It uses way more.

Here’s what users are reporting:

Model	Task	Tokens Used	Output
gpt-5-nano	List 6 phrases	1,659 tokens	✅
gpt-5-nano	Write 4,000-token article	5,030 tokens	❌ (no output)
gpt-5-mini	Simple task	3,000+ tokens	❌ (blank)

I asked it to generate a simple list. It used 1,659 tokens. For six phrases. That’s insane. — OpenAI community, post #1338030¹²

Another user said:

I set max_completion_tokens = 1000. It consumed 5,000 tokens and returned nothing. Just a blank string.

And the worst part? The model hits the reasoning_token_budget limit (2048) and just… stops. No error. No output. Just silence.

This is not efficient. It’s wasteful. And it’s ruining the cost argument — especially for the "cheap" Nano and Mini models.

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3. 💣 Code Quality: It’s Making Things Worse

It’s not fixing the bug. It’s rewriting the whole app in a language I don’t know. — Dev on Cursor forum¹⁴

GPT-5 was supposed to be a multi-step agent — plan, write, test, integrate.

Instead, users report:

• Inserting 500 lines of code for a simple regex fix
• Ignoring instructions (e.g., “use this library” → ignores it)
• Failing to use tools (e.g., LSP build system, test runner)
• Making up code (e.g., importing a non-existent package)

It doesn’t know how to use the LSP. It’s like it’s blind to the editor. — Cursor user¹⁷

Some devs say they have to re-explain the same rule 3–4 times before GPT-5 actually follows it.

And when it does try to use a tool?

It calls git commit but doesn’t push. No error. No explanation. — Another user

It’s not an agent. It’s a dumb, overconfident intern.

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4. 📝 Response Quality: Bland, Robotic, and Boring

It’s like talking to a corporate beige zombie. — Windows Central, user review¹⁸

Let’s be real: GPT-5 was supposed to have better prose, better creativity.

Instead:

• Poems sound like a “parody” (Reddit user¹⁹)
• Summaries are wrong or missing key points
• Table generation fails
• PDF analysis doesn’t work
• Memory? It forgets stuff from 20 seconds ago
• Context window: supposedly 256k, but many users report only 128k works reliably

I asked it to write a blog post. It gave me 3 short sentences. No structure. No flow. Like it was tired. — Medium critic²²

And the worst part?

It ghosts you mid-sentence. Just… silent. — Multiple users on OpenAI forum¹³

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5. 🚫 Missing Features & Forced Upgrade

Let’s be honest: GPT-5 is not a full upgrade. It’s a downgrade in disguise.

Here’s what’s missing or broken:

Feature	What’s Promised	What’s Actually True
Custom tools	Works in all tiers	Only in Standard plan
Fine-tuning	Not available	Still not here
Temperature control	Yes	gpt-5-nano has NO temperature param
Model picker	Removed	You can’t go back to GPT-4o
256k context	Yes	Only 128k actually works

And yes — GPT-4o and o3 are gone from the ChatGPT UI.

I loved GPT-4o for creative tasks. Now I’m forced to use GPT-5, which is slower, dumber, and more corporate. — OpenAI forum user⁶

This isn’t an upgrade. This is shrinkflation.

They’re not selling a better model. They’re selling a worse one and making you buy it. — Medium critic²³

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6. 📉 API Reliability: Blank Outputs Are the Norm

The new Responses API has a reasoning_token_budget parameter.

But if you hit it?
→ Empty string.
→ No error.
→ No progress.

I set max_completion_tokens = 500. It used 3,000 tokens and returned nothing. — OpenAI forum¹²

And because gpt-5-nano doesn’t expose temperature, you can’t control creativity at all.

So you’re stuck with:

• No creativity control
• No reliable output
• No way to debug why it’s silent

I’d rather use a Python script than trust GPT-5 to generate a simple response. — Dev, OpenAI community²⁴

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🔍 What Devs Are Actually Saying

This isn’t just “it’s slow.” This is grief.

• “I’m mourning GPT-4o.”
  → Devs are genuinely sad about losing a faster, smarter, more responsive model.

• “It feels like OpenAI is forcing us to use a worse product.”
  → The removal of model choice feels like forced upgrade, not progress.

• “This isn’t a revolution. It’s an incremental patch with a hype machine.”
  → Multiple threads call it a “disaster” and accuse OpenAI of repeating the “maths good, but discourse analysis? Zero” pattern²¹.

• “It’s not worth it.”
  → Some devs are switching back to Claude or staying on GPT-4.1.

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📌 Final Verdict: The Hype Was Too Big

GPT-5 has some real improvements — better benchmarks, better agentic tasks in theory.

But in practice?

• Slower than GPT-4o
• Uses more tokens
• Gives blank outputs
• Breaks workflows
• Removes model choice
• Forces you to use a worse product

It’s not a revolution.
It’s not a leap.
It’s a downgrade disguised as a breakthrough.

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✅ What Devs Should Do Now

1. Don’t trust the marketing. OpenAI sold GPT-5 like it was a god-tier coder. It’s not.
2. Test new models in staging — not production.
3. Keep fallbacks. GPT-4o is still better for many tasks. Use it.
4. Watch for API quirks. reasoning_token_budget = silent failure. max_completion_tokens too low = blank.
5. Demand transparency. OpenAI needs to clarify what’s actually working vs. what’s broken.

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📚 References

1. GPT-5 Released: What the Performance Claims Actually Mean – FinalRoundAI
2. First Impressions of GPT-5 – Leon Furze
3. GPT-5 Launch Sparks Backlash – Digital Watch Observatory
4. GPT 5 Slow. Very slow – Cursor Forum
5. GPT-5 + Responses API is extremely slow – OpenAI Dev Forum
6. OpenAI Launches GPT-5: Initial Legal Benchmarking – Legal IT Insider
7. What is going on with the GPT-5 API? – OpenAI Dev Forum
8. GPT 5 is really bad (at least in Cursor) – Cursor Forum
9. Did Sam Altman Oversell GPT-5? – Windows Central
10. GPT5 Is Horrible – Reddit
11. GPT-5: OpenAI's Worst Release Yet – Medium
12. GPT-5 is awful - Reddit

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TL;DR:
GPT-5 is not ready for prime time.
It’s slower, more expensive, and less reliable than GPT-4o.
Don’t upgrade without testing.
And if you’re using it in production? You’re doing it wrong.