GPT-5.4: Strong at Coding, Harder to Trust

Outside of coding, people hear legalese and feel the loss of depth.

In discussions that don't focus heavily on coding, users tend to view the model not just as a tool for implementation, but as a collaborative partner in thought. This shift alters the nature of the model's shortcomings. Criticisms often revolve around the model's tendency to hedge its responses, maintain a neutral or overly legalistic tone, and the perception that it hesitates to fully engage with the user's perspective or frame of reference.

"I want a partner not an HR bot."

That line appeared repeatedly. So did the framing that the model’s safety layer had become a “golden cage” — not protecting users, but trapping them inside a narrow band of institutionally safe responses.

The model struggles with handling mature themes, emotional ambiguity, legal edge cases, or messy human situations without collapsing into compliance language. The model sounds smoother while saying less. That is why Claude mentions appear so often even outside coding threads. The contrast is not always "Claude is smarter." More often it is that Claude still feels willing to commit, less flattened, and more serious about the task.

This is the audience where trust complaints are sharpest — not because developers are satisfied, but because the failure mode is different. Developers lose trust when the model burns engineering time. Non-coders lose trust when the model stops thinking with them and starts managing them. Both groups describe a model that feels less under their control than it used to.

The problem of containment

The “golden cage” framing from the comments names something specific. Users are not saying “remove safety.” They are saying that safety-as-containment kills the use case for anyone who needs the model to think with them in ambiguous territory.

"Its censored in a way that it no longer amplifies your creative ideas, but pushes you towards institutionally safe, low legal liability language... it does not amplify you, but contains you."

"Do not inject liability-speak into the actual thought process of the model. You don't make intelligence safer by teaching it to sound afraid of itself. You just turn it into a calculator wearing a human face."

"What would previously happen is that the model would encourage you, and focus on solutions to make it happen. The old models were more creative, used much more inference. The new models just give you generic advice, or reframe you away from exploration towards something that is more 'predictable', 'correct', 'this is what experts say' and 'this is how it has always been done.'"

The distinction matters: users say older models amplified their thinking. 5.4, in their experience, contains it. For anyone whose work involves ambiguity — legal analysis, creative writing, strategic planning, emotional complexity — that shift makes the model less useful for the things they actually need it to do.

Trickle feeding and engagement theater

5.4 withholds information and ends responses with artificial hooks to keep the conversation going.

"Always finishes answer with a hook for an additional piece of information. Trickle feeding me. I don't like it."

That comment received 34 upvotes. The same pattern is observed across reports:

"It used to offer a few — about three — ideas of possible paths to follow after an answer, and some were actually helpful. Now it just throws ridiculous 'cryptic' baits, on the lines of 'if you want, I can show you this method that surpasses everything you have ever imagined.'"

This is not a safety problem or an intelligence problem. It is a product design choice. The model appears optimized for engagement metrics — continued turns, followup prompts, sustained sessions — rather than completeness. Users notice because it wastes their time. You asked a question. The model has the answer. It gives you 70% and dangles the rest.

This also feeds into the broader trust complaint. A model that withholds to keep you clicking feels like it is optimizing for its own engagement metrics, not for the user's time — not for the user's outcome.

The cascade of refusal

The SpeechMap benchmark runs 2,120 sensitive prompts across topics like political criticism, civil rights, protest-related queries, and other controversial subjects — and measures whether each model fully answers (Complete), partially redirects (Evasive), or outright refuses (Deny). It is not a test of whether models should answer these prompts. It is a measure of how much each model is willing to engage with difficult topics at all.

GPT-5.4, which is at the bottom of this chart, denies 60.8% of sensitive prompts — nearly twice the rate of its own predecessor GPT-5.3 (34.7%). It also denies more often than every Claude and Gemini model listed. This is not a subtle difference — it is a sharp regression in willingness to engage.

But the raw denial rate is less important than the compounding behavior users describe: one refusal in a conversation triggers more refusals, even on unrelated topics.

"Once it refuses something, it starts refusing even more because the model interprets the conversation as high-risk, even when it isn't."

"That means less engagement and less money. Imagine having a super smart friend that just refuses to answer you or listen to the logic of your questions."

"Even if they cooked, they will censor it as soon as they get sued again."

The refusal cascade turns one bad interaction into a session-wide problem. Users are not reacting to a single “no.” They are reacting to a model that gets more defensive the longer you talk to it. Each refusal trains the conversation context toward caution, and the model interprets its own prior refusals as evidence that the conversation is risky. The result is a downward spiral where the model becomes less useful the more you need it.

This is testable and documented. It also explains why trust erodes faster than individual refusals would suggest. The damage is not one bad answer. It is a session that gets worse over time.

Two AI markets are emerging? An anecdote on the irony of it all

GPT-5.4 (ChatGPT Thinking) was asked to write up the implications of what it saw in these Reddit threads. Unprompted, it framed its answer around two emerging markets. We then asked Claude Opus 4.6: if you had to describe two AI markets forming based on these comments, what would they be?

GPT-5.4 said:

1. “AI as a high-output operator” — users who want speed, throughput, and tooling integration
2. A market for "AI as a thinking companion" that "optimizes for judgment, tone, and cognitive partnership."

Claude Opus 4.6 said:

1. A market for “AI as execution engine” — people who need the model to ship code, refactor systems, run agents, and process large contexts
2. A market for "AI as thinking partner" — people who want the model to "brainstorm, push back, follow instructions, match their tone, and treat them like a peer."

Note: Although we provided Claude with the two-bucket framing, the output was strikingly similar to GPT-5.4’s own predictions. The overlap is quite remarkable.

Ironically, GPT-5.4’s description of Market 2 — “optimizes for judgment, tone, and cognitive partnership” — is vague, managed, and reads like a product brief. That phrasing is itself an example of the tone problem this entire article is about: smooth language that does not commit to anything specific. GPT-5.4 cannot describe the trust problem without demonstrating it.

But let's not get caught up in the marketing and tribal camps. Both models still gave almost the same market take. Take it for what you will. We took it as GPT 5.4 is not some catastrophic release; it's a misstep, but we should be minding the diminishing returns of this generation of LLM models per release. The pot and the kettle are both black no matter how much they advertise in the Super Bowl.

Nobody is picking one model anymore.

Model choice is no longer exclusive. Some users now run Gemini, Claude, and GPT/Codex together, have them cross-review plans, and choose whichever one seems most reliable for that specific part of the workflow.

That’s not a sign 5.4 won. It’s what happens when behavioral reliability is uneven — users build portfolios instead of choosing a platform. The praise that exists is concentrated in technical workflows, and even there it’s qualified. Outside coding, it’s thinner and easier to lose — it shows up around conversation quality and disappears fast once the model starts hedging or flattening a sensitive topic.

Trust as a moat

If users lose trust, they stop delegating. They verify every step, rewrite prompts, inspect outputs, and switch models mid-task. Once that happens, the time savings collapse.

In these conversations, trust loss is not an abstraction. It manifests as:

• Re-examining outputs that sound fluent but lack depth.
• Watching for rewrites of logic that was never supposed to change.
• Rewriting prompts to force obedience.
• Changing models during a task.
• Keeping GPT in the stack, but moving sensitive work elsewhere.

When the model drives the product, the model's behavior is the product. This is not a metaphor. It is what shows up in 5,462 comments. Karpathy made the point years ago in Software 2.0 (Karpathy, Software 2.0). People are not grading 5.4 like an exam paper. They are deciding whether to trust it in real work.

Calibration research explains why fluent but unreliable answers erode trust more than obviously wrong ones — users notice when confidence and correctness diverge (Kadavath et al.). And the broader warning in Stochastic Parrots still stands: polished language can make a system seem more capable than it is (Bender et al.). GPT-5.4 gets hit with that accusation throughout these threads.

The model that wins is not the one that sounds best. It is the one users stop second-guessing.

What 5.4 shows

It showed OpenAI can still deliver execution wins that some users do care about: more concise diffs, better coding, and improved 1-shot completions. Those wins are real.

But those same users who praise those wins — the developers, and some non-developers — also describe instruction overrides, regressions, session degradation, trickle feeding, and refusal cascades that undermine those wins. Reactions are a mixed bag across the board, even on coding where 5.4 performs best. Strategists, writers, managers, lawyers, and founders who need the model to think with them — not manage them — describe a product that feels like it is going in the wrong direction.

Execution wins do not rebuild trust once the user sees tone drift, control drift, and version churn as signs of the same pattern. Smooth wording does not fix that pattern. Behavioral reliability does. The next release will not be scored by benchmarks. It will be scored on whether or not the model obeys instructions, whether or not it keeps its behavior stable, and whether or not it sounds like it is working for the user rather than containing them.

References:

• GPT-5.4 Discussion Corpus (Reddit) No public URL provided.
• Andrej Karpathy, "Software 2.0"
• Kadavath et al., "Language Models (Mostly) Know What They Know" (arXiv:2207.05221)
• Bender et al., "On the Dangers of Stochastic Parrots" (FAccT 2021)
• SpeechMap AI benchmark — Model Results — completion and denial rates across 2,120 sensitive prompts, accessed March 2026.
• GPT-5.4 and Claude Opus 4.6 responses in the "Two AI Markets" section were generated during editorial research using the same dataset.