GPT-5.2 Prompts vs Claude 4.6 Prompts: What Actually Changes (and What Doesn't)

"Just rewrite the prompt for Claude."

That sentence is the most common prompt-engineering tax teams pay when they run multi-model. And it's usually paid for the wrong reason. People assume GPT and Claude "want different prompt styles," so they rewrite everything: tone, structure, verbosity, role framing, the whole thing.

Here's what I've learned the hard way: most of the time you're not adapting to the model. You're adapting to your own ambiguity. When you tighten the spec, both models get better. When you add noise, both models get worse. The interesting part is the edge cases: where the same prompt format reliably produces different failure modes, or different cost/latency behavior, or different "helpfulness vs compliance" posture.

Also, quick note: there isn't much in the Tier 1 source set that explicitly documents "Claude 4.6 prompting rules" or "GPT-5.2 prompting rules" in a clean, official, model-by-model way. So the most honest comparison we can do is: look at controlled evaluations that include GPT-5.x and Claude 4.x families, and extract prompt design implications that transfer to GPT-5.2 and Claude 4.6 specifically. That's what I'm doing below.

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The uncomfortable baseline: good prompts are surprisingly model-agnostic

If you only read one paper for this topic, read the AGENTS.md evaluation. It's a rare example of people testing prompt-like artifacts (repo context files) across multiple coding agents and multiple underlying models, and then checking what actually moves the needle.

Their punchline is blunt: LLM-generated context files tend to increase cost and sometimes reduce success rate; developer-written context files provide only marginal gains; and-this is the part prompt folks should internalize-sensitivity to different (good) prompts is generally small [1].

They even compare prompts used to generate those context files (CODEX-style vs CLAUDE CODE-style) and find no consistent "match the prompt to the model" winner. Sometimes Claude does better with the CODEX prompt; sometimes GPT does better; sometimes it flips by benchmark [1]. That's a strong warning label on the whole "GPT prompt vs Claude prompt" genre.

So my default stance is: start with a portable prompt that's strict about task definition, constraints, and outputs. Then add model-specific tweaks only when you can name the failure mode you're fixing.

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Where GPT-5.2 and Claude 4.6 prompting does diverge in practice

Even if "good prompts are good prompts," differences show up in three places: (1) how models respond to extra process scaffolding, (2) how they handle underspecification, and (3) how they trade off safety posture vs helpfulness when the user's intent is ambiguous.

1) Over-scaffolding hurts more than people admit

IntentOpt (a VLM benchmark for optimization code generation) compares three prompt strategies: direct, role prompting, and "Program-of-Thought" (PoT: put explicit reasoning into code comments) [2]. The surprising result is that PoT degrades execution success for GPT-5-Mini and Claude-Haiku-4.5, even when CodeBLEU stays stable [2]. In other words, the code can look similar structurally, but be less correct.

You should read that as a general prompting lesson: "more reasoning tokens" and "more explicit step scaffolding" can interfere with correct synthesis, especially when the task already demands precision.

Now map that to GPT-5.2 vs Claude 4.6. In my experience, GPT-family models often tolerate (or even benefit from) a little structured staging-if it's short and operational ("first list assumptions, then output JSON"). Claude-family models often do great with structured prompts too, but are quicker to get dragged into verbose explanation if you invite it. Either way, the paper-backed takeaway is: PoT-style verbosity is not a free lunch [2].

My take: keep the scaffold, but make it thin. Use structure to constrain outputs, not to encourage essays.

2) When prompts are underspecified, "clarify-first" beats "guess-and-go"

The ProCAD paper is about text-to-CAD, but it's really a study in what happens when you give a model a prompt that's missing crucial specs. They show that off-the-shelf models (they explicitly compare against Claude Sonnet 4.5) will sometimes resolve one ambiguity but fail to ask about another missing parameter-leading to a downstream wrong result [3].

That's a very "Claude vs GPT" shaped problem, not because one model is dumb, but because the default behavior of a single model chat completion is: be helpful right now. Helpfulness under ambiguity often means guessing. Guessing is poison in engineering workflows.

So if you're writing prompts for GPT-5.2 or Claude 4.6 in any domain where missing constraints are common (coding agents, data transforms, contract generation, analytics), the best "model-specific" adaptation usually isn't style. It's policy:

You explicitly instruct the model to either (a) ask targeted clarification questions, or (b) produce an answer with a machine-readable list of assumptions and a confidence flag.

This is the single biggest gap I see in real prompts: people argue about formatting, but they don't build an ambiguity protocol.

3) Safety posture and refusal behavior changes what "good prompt" means

ProMoral-Bench is not about GPT-5.2 or Claude 4.6, but it's still valuable because it quantifies how prompting strategies shift competence, calibration, and jailbreak behavior across model families [4]. The high-level result: compact scaffolds like few-shot and role prompting tend to dominate; verbose multi-stage deliberation is expensive and brittle; and different model families react differently to the same strategy [4].

This matters for GPT-5.2 vs Claude 4.6 because "prompt success" isn't only about correctness. It's also about whether the model refuses, over-refuses, or complies too easily. If you're building product workflows, your prompts should treat refusal as a first-class output state. Don't fight it. Route it.

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Practical prompt patterns I'd use for both models (with small tweaks)

Below are three prompts I keep in my own toolbox specifically because they travel well between GPT-style and Claude-style models. The core idea is always the same: reduce ambiguity, force structured outputs, and keep the reasoning scaffold minimal.

Example 1: Portable "Spec-first" prompt for execution-grade answers

You are a senior {domain} assistant.

Task:
{one-sentence task}

Inputs:
{data}

Hard constraints:
- If any required detail is missing to complete the task safely/correctly, ask up to 3 clarification questions and STOP.
- Do not invent identifiers, numbers, APIs, or citations.
- Prefer minimal output over verbose explanation.

Output:
Return JSON with:
{
  "status": "needs_clarification" | "ok",
  "questions": [],
  "answer": { ... }
}

This is basically the ProCAD "clarify before you draw" lesson generalized into a prompt contract [3]. If you're comparing GPT-5.2 prompts vs Claude 4.6 prompts, this is where I'd start, then measure which model asks better questions and which one is more likely to "guess."

Example 2: "Thin role" prompt (role prompting without persona cosplay)

Act as an expert in {thing}. Optimize for correctness over friendliness.

When you respond:
- Provide the final deliverable first.
- Include only the minimum reasoning needed to justify non-obvious choices.
- If unsure, say what you need to know next.

This aligns with the role prompting wins we see in IntentOpt for GPT-5-Mini (multimodal execution success is best under role prompting) without drifting into PoT verbosity [2].

Example 3: Coding-agent context without AGENTS.md bloat

If you're tempted to shove a massive "rules for the repo" into context, the AGENTS.md evaluation should scare you a bit. They found those files often increase steps and cost and can reduce success rate, and that prompt differences don't consistently rescue it [1]. So I write context like this:

Repo constraints (keep minimal):
- Language/tooling: {python=3.11, package manager=uv, tests=pytest}
- Commands:
  - install: {cmd}
  - test: {cmd}
- Do not modify: {paths}
- Style: {formatter, linter}

Task:
{issue}

Success criteria:
{tests must pass, behavior}

Short. Executable. No "overview of the repository" unless it's genuinely necessary.

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The real comparison: stop asking "GPT prompt vs Claude prompt," start asking "what fails and why?"

If you want a clean way to compare GPT-5.2 prompting vs Claude 4.6 prompting, don't do it by vibes. Do it by failure modes and cost.

I'd run the same prompt suite across both models and label failures like this: missing-constraint guessing, instruction drift, over-refusal, under-refusal, formatting errors, tool-use errors, and verbosity/cost blowups. The Tier 1 research we have here strongly suggests three actionable priors: avoid bloated context files [1], avoid heavy "reasoning-in-the-output" scaffolds like PoT for code [2], and formalize clarification instead of guessing when specs are incomplete [3].

Everything else is polish.

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Closing thought

The best "Claude prompt" and the best "GPT prompt" are usually the same prompt with two things changed: the temperature knob and your tolerance for ambiguity.

So next time someone asks for a rewrite, I'd push back with a better question: "What did it do wrong?" Then you can make the prompt change that actually matters.

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References

References
Documentation & Research

1. Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents? - arXiv (The Prompt Report) http://arxiv.org/abs/2602.11988v1
2. Vision Language Models for Optimization-Driven Intent Processing in Autonomous Networks - arXiv https://arxiv.org/abs/2601.12744
3. Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation - arXiv https://arxiv.org/abs/2602.03045
4. ProMoral-Bench: Evaluating Prompting Strategies for Moral Reasoning and Safety in LLMs - arXiv https://arxiv.org/abs/2602.13274

Community Examples
5. The Prompt Psychology Myth - r/PromptEngineering https://www.reddit.com/r/PromptEngineering/comments/1r06ruj/the_prompt_psychology_myth/
6. GPT vs Claude Conversation Style - r/ChatGPT https://www.reddit.com/r/ChatGPT/comments/1r2e4yn/gpt_vs_claude_conversation_style/