No One is Telling the REAL ChatGPT-5.1 Story: It's NOT About Personality, It's The Best Model In the World At Following Instructions—And That Changes Everything (Prompts + Takeaways Included)

Yes, the new ChatGPT-5.1 is warmer! It can also be friendlier. Or quirkier. Or more nerdy.

And we all hated the ChatGPT-5 personality so much that’s all we can talk about.

But that’s not the point.

What we’re sleeping on is WHY this sudden new power exists for ChatGPT-5.1.

Hint: it’s not magic. It’s vastly improved rule-following. Rule-following so good that it shapes personalities and changes how we think about models.

And when you get a model that’s smart, capable, and really good at following instructions, it changes a lot:

• Prompts need to be more precise, because the model listens way better
• The model can be trusted with longer agentic workflows, if set up properly
• The model needs more structure to perform at its best

Here’s what I’ve put together for you:

• My top 10 takeaways (each has engineering and non-tech sections)
• GPT-5.1 actually obeys you. Careful what you say.
• Two brains now: pick “fast” or “deep” (fast can be smart)
• Prompts aren’t wishes anymore—they’re specs
• Your AI can finally have standards and style
• Say TEACH or CRITIQUE, watch it transform
• It can plan–act–verify like a junior PM
• Text-only is over; tools are the real upgrade
• You can literally prompt for skepticism now
• One-off “prompt hacks” are dead; workflows win
• New literacy test: can you spec and judge?
• 6 prompts to take advantage of OpenAI’s best new model:
• GPT-5.1 Prompt Converter

For when your Franken-prompt needs a little cleanup

• Workflow Prompt Builder

Do what GPT-5.1 does best: go from prompts to workflows

• Mode Pack Builder (THINK / TEACH / CRITIQUE)

One brain, multiple personas—watch GPT-5.1 follow instructions

• Reliability Wrapper

Leverage rule-following to make hallucination checks more reliable

• Document Digest

Feed it a 40-pager, get back what you’d actually read

• Decision Helper

Leveraging ChatGPT-5.1’s thoughtfulness to set up a thinking partner for you

In my opinion, the 5.1 release probably should have been ChatGPT-5. It feels closer to what we all heard was coming (especially the personality piece).

But just because it’s arriving a couple of months after ChatGPT-5, don’t sleep on it!

This model feels trustworthy, dependable, and really really good at just getting stuff done. It’s a better writer than GPT-5. It’s easier to manage than GPT-5. I know we are getting kind of used to new models giving us new capabilities at this point. It can feel like just another Friday.

Don’t let it feel like another Friday. Keep that curiosity alive. Keep that capacity for wonder. Keep learning. These models keep getting better, and learning is how we keep our skills sharp.

PS. Curious how GPT-5.1 relates to Claude and Gemini? Rumors are swirling of new models shortly from both. Let’s see what the next couple of weeks hold! In the meantime, enjoy the ChatGPT-5.1!

Subscribers get all these newsletters!

Subscribed

Grab the ChatGPT-5.1 Prompts

I put these prompts to give you a sense of the range of this model.

The GPT-5.1 Prompt Converter and Workflow Prompt Builder clean up your older prompts and turn your recurring tasks into crisp, reusable specs that 5.1-compatible.

Meanwhile, the Mode Pack Builder and Reliability Wrapper start to make 5.1 feel less like “a chatbot” and more like a configurable teammate that can switch personas and tell you what to double-check before you ship.

Then you’ve got Document Digest and Decision Helper quietly doing the executive work: digesting 40-page decks into something human, and forcing real trade-off thinking instead of fake certainty. Together, they show off what 5.1 is actually good at now—not being “warmer,” but taking structure, constraints, and judgment seriously.

Lots to dig into! You can get the details on how these work in the article below.

ChatGPT-5.1: Ten Takeaways Nobody’s Talking About

===================================================

OpenAI dropped GPT-5.1 on November 12th, and the internet immediately latched onto the wrong story. Everyone’s talking about the emotions, the warmth, how the model feels more human. They’re missing the point entirely.

The real story is that GPT-5.1 is the most agentic, production-ready model OpenAI has shipped. This isn’t just another incremental bump—it represents a fundamental shift in how we should think about working with AI. Where earlier models were powerful but unpredictable, 5.1 is powerful and increasingly controllable. That changes everything.

I’ve spent the past few days testing it, reading through OpenAI’s updated documentation, and talking to teams already building on it. What follows are ten takeaways that actually matter—not the surface-level personality changes everyone’s obsessing over, but the structural improvements that will determine who succeeds with AI in production and who gets stuck in prototype purgatory.

⸻

1. Sharper instruction-following

GPT-5.1 is explicitly tuned to follow instructions more faithfully than GPT-5 and earlier models. OpenAI frames it as “warmer, more intelligent, and better at following your instructions,” but that middle part—the instruction following—is what actually matters.

If your prompt says “three bullets, then a one-sentence summary,” it’s much more likely to do exactly that. If your system prompt says “don’t apologize” or “don’t restate the question,” it will try to obey. The new prompting guide explicitly tells developers to reduce conflicting instructions because 5.1 will actually honor them.

This is both upside and downside. In older models, sloppy, conflicting instructions often got “averaged out.” People got used to that. Now, contradictions like “be very concise and explain in detail” are more likely to cause weird behavior or oscillation. Instruction following is better, but it’s still probabilistic—long prompts, hidden defaults, or vague language can still lead to drift.

OpenAI’s GPT-5.1 usage guide and prompting guide both call out stronger instruction following and the need to simplify prompts. The message is clear: you have to treat your prompts and system messages like real specs. That means separating tone, tools, safety, and workflow rules instead of piling everything into one paragraph. When behavior is off, the first debugging step is to look for conflicting instructions, not “maybe the model got worse.”

For everyday users, your “settings” now matter more. If you tell ChatGPT “be brief,” “explain everything,” and “sound friendly and upbeat” in the same breath, you’ll get more friction. Keeping instructions simple and non-contradictory—one main goal, one preferred tone—will have a visible effect on answer quality.

⸻

2. Two brains: Instant vs Thinking (and reasoning_effort)

GPT-5.1 comes in two main variants: Instant, the default “fast” model, and Thinking, the advanced reasoning model. Thinking adapts how long it thinks—faster for simple tasks, more persistent for complex ones. Developers can also set reasoning_effort to “none,” which effectively turns 5.1 into a non-reasoning model for low-latency use cases.

In ChatGPT, you’ll see different model options in the selector. Atlas and other surfaces may auto-route you. Simple requests feel snappier than “full thinking mode” but still smart. Harder questions—multi-step reasoning, debugging, planning—trigger visibly longer “thinking” in GPT-5.1 Thinking. I’ve had queries run for multiple minutes that would have taken seconds on GPT-5.

“None” doesn’t mean “dumb.” You still get 5.1’s language skill and tool-calling, just without expensive chain-of-thought. And “more reasoning” doesn’t always mean “better”—for some tasks, overthinking can produce convoluted answers or unnecessary tool calls. There will be workloads where Instant is strictly better.

OpenAI’s GPT-5.1 blog and developer post explain Instant vs Thinking and the reasoning_effort settings in detail. The implications are straightforward: latency versus depth becomes a first-class design parameter. You’ll route “known-pattern” tasks like templated replies and simple transforms to Instant or none, and reserve Thinking with higher reasoning effort for gnarly problems. Cost, speed, and reliability trade-offs now depend on how you route across these modes.

For everyday use, you no longer have to guess why the model is slow. Use the quick model for day-to-day stuff—emails, summaries, simple explanations—and switch to the “thinking” model when you want it to really wrestle with something: big decisions, complex documents, or confusing data.

⸻

3. Prompts as mini-specs, not wishes

The 5.1 prompting guide explicitly treats prompts as “small specifications” that define role, objective, inputs, and output format. The model is tuned to respect these patterns, especially for production “agents.”

Well-structured prompts like “You are my project manager. I’ll paste context. Output: 3 risks, 3 next steps, 1-paragraph summary—nothing else” yield more predictable, repeatable behavior. Unstructured, chatty prompts still work for casual use but are harder to reuse or automate.

There’s diminishing returns on verbosity. Very long “spec prompts” with redundant or conflicting rules can backfire. The sweet spot is usually a short role description, clear goal, clear input, and explicit output shape. Older “magic incantations” like “act as…” or “step by step…” matter less than crisp structure.

The 5.1 prompting guide and Cookbook examples like “Build a coding agent with GPT-5.1” show spec-style prompts in real agents. For developers, you should standardize prompt templates as if they were interfaces: one for “summarize document,” one for “write support reply,” one for “propose plan.” They should live in version-controlled config, with tests or evals. Consistency across these specs will matter more than clever phrasing.

For everyone else, you don’t need to learn jargon. Just adopt a simple habit: say who it is (”you’re my writing assistant”), what you want, what you’re giving it, and how you’d like the answer formatted. That alone is enough to make ChatGPT feel dramatically more reliable.

⸻

4. Configurable behavior and personalities

GPT-5.1 leans into configurability. OpenAI calls out more “enjoyable to talk to” behavior and rolls out personality presets like Default, Professional, Friendly, Candid, Quirky, Efficient, Nerdy, and Cynical, plus deeper style controls.

In ChatGPT, you can pick or tune how formal, playful, or blunt you want the assistant to be. Those settings persist across chats instead of resetting every time. Combined with stronger instruction-following, this means tone and verbosity can actually feel consistent.

Personalities are still prompts under the hood. If you stack your own instructions on top—”no emojis, be brutally direct”—they can conflict with the preset like “Friendly” or “Quirky” and you’ll get mixed results. Also, “warmer” models can feel too chatty unless you explicitly ask for concision.

OpenAI’s GPT-5.1 announcement and consumer coverage detail the personality system and tone improvements. For developers, you can now ship differentiated “voices” for different agents: a formal enterprise assistant, a casual onboarding helper, a terse internal tool. Those are just different spec blocks. But you’ll want internal standards so marketing, legal, and support don’t all reinvent conflicting personas.

For everyday users, you can stop fighting the default voice. If you hate bubbly, tell it once: “Be direct, concise, and neutral—no emojis, no cheerleading.” Combine that with a personality preset that’s closest to you and you’ll get a much less annoying day-to-day assistant.

⸻

5. Modes and “soft types” for behavior

Because 5.1 is more literal, you can define simple “modes” like REVIEW, TEACH, or PLAN and treat them like soft types—each with specific rules for structure and tone. The prompting guide leans into this pattern for agents, with different sections for planning, updates, final answers, and so on.

You can say, “When I start with TEACH:, explain like I’m new, give one example, and provide a 3-step practice exercise. When I start with CRITIQUE:, only point out issues and suggestions—no rewrites.” With 5.1, the model will usually respect those contracts in a reusable way.

These modes are still enforced “by vibes,” not by a compiler. The model can and will sometimes violate the contract, especially if later instructions contradict the mode. Mode definitions need to be short and unambiguous. Long lists of rules make violations more likely.

The 5.1 prompting guide’s sections on user update specs, solution persistence, and formatting rules are all examples of “soft typed” behaviors. In applications, you can define explicit sub-modes for the same model—planning versus execution versus critique—and swap them via system messages or tags. This gives you differentiated tools without needing different models. It also makes evals easier: you test each mode separately.

In plain chat, you can get eighty percent of the benefit by using a few consistent keywords: THINK, TEACH, CRITIQUE, JUST DO IT. Each should map to a clear style in your instructions. Over time, ChatGPT will feel like a small toolbox of behaviors instead of one generic assistant.

⸻

6. Agentic behavior: plan–act–summarize

GPT-5.1 is positioned as a flagship model for “agentic tasks”—things where the model plans, uses tools, and iterates, not just answers once. The Cookbook’s new 5.1 guides focus heavily on agents that gather context, plan, act, verify, and then summarize.

When prompted correctly, 5.1 will outline a plan, call tools like search, code, or files, adjust the plan based on tool outputs, and only then give a final answer. For example, a coding agent might read files, generate patches, run tests, and iterate before proposing a pull request.

The “agent” behavior is not automatic. If your prompt doesn’t spell out planning and verification steps, 5.1 will still happily act like a one-shot chatbot. And more agentic behavior also raises new failure modes: infinite loops, over-use of tools, or “doing too much” when the user just wanted a quick answer.

The 5.1 developer blog and recipes like “Build a coding agent with GPT-5.1” describe these patterns and tools like apply_patch and shell that support them. For developers, you should design explicit agent loops: under what conditions should the model re-plan, re-query tools, or stop? Logging, guardrails, and evaluation become more important. You’re not just calling a model—you’re designing a small autonomous worker whose behavior is governed by your prompt spec plus tool set.

For everyday users, you can start thinking in terms of “mini-projects” instead of “one answer at a time.” For instance: “Read these three docs, list the open questions, then draft me a one-page plan that answers as many as possible.” You’re delegating a whole sequence of steps, not just asking for a summary.

⸻

7. Tools as normal, not advanced

5.1 is designed to work with a full tool stack: web search, code execution, file reading, and for developers, custom tools and APIs. OpenAI markets it as the flagship for coding and agentic tasks, with strong tool-calling performance even in non-reasoning mode.

In ChatGPT, it can automatically use search when needed, read uploaded files, and run code in certain contexts. In apps, 5.1 can orchestrate calls to your own APIs, databases, or services instead of just generating text.

Tool use isn’t magic. The model still needs clear descriptions of what each tool does, what inputs are allowed, and when it should not call a tool—for example, for sensitive operations. And external tools introduce real-world failure modes: timeouts, API errors, stale data.

The 5.1 developer docs and Cookbook show examples of tool integration and agent design. For developers, you should treat 5.1 as an orchestrator over your APIs, not just a text generator. The hard work is designing good tool schemas, clear descriptions, and safety checks. Success will depend more on the quality of your tools and prompts than on squeezing out slightly better text generation.

For everyday users, you don’t need to know what “tools” are under the hood. Just remember you can say things like “use the web and show me sources” or “summarize this PDF into three bullets for a VP.” That’s you asking the model to reach outside itself instead of hallucinating everything from memory.

⸻

8. Reliability you can prompt for

OpenAI keeps improving safety and reliability evals—for jailbreak resistance, mental health, and political bias. 5.1 plus the prompting guide explicitly encourage building self-checks and verification into prompts and workflows, not treating “hallucinations” as unfixable magic.

You can ask 5.1 to explain its reasoning at a high level, to list what should be verified externally, or to output in a structured way that you can automatically sanity-check. In agent flows, you can make it verify via tools like search or database calls before answering.

Even with better safety and bias scores, 5.1 will still hallucinate, especially when forced to answer without tools or when asked for obscure facts. And chain-of-thought isn’t a lie detector: a well-worded but wrong reasoning trace is still wrong.

OpenAI’s model and safety docs, plus the 5.1 guide, recommend defensive prompting and structured outputs for critical tasks. For developers, you should design patterns like “answer plus uncertainty plus verification checklist,” use tools to validate key claims where possible, and build evals that probe for failure modes in your specific domain. Reliability becomes a product of prompt design plus tools plus monitoring, not just “this model is good.”

For everyday users, instead of just asking “is this right?” ask: “Give me your answer, then list two things I should double-check before I rely on it.” Or: “Explain how confident you are and why.” That uses the model to improve your own skepticism instead of replacing it.

⸻

9. Workflows over one-off tricks

5.1 is strong enough that the bottleneck is no longer “can the model do this?” but “do you have a repeatable way of asking?” The Cookbook and docs emphasize pattern-based prompting and agents instead of clever one-off prompts.

Teams that win with 5.1 aren’t the ones with the fanciest “prompt hack.” They’re the ones who turn high-value tasks—support replies, code review, research summaries—into stable workflows with fixed prompts, tools, and output formats.

Ad-hoc prompting is still fine for exploration and personal use. But for anything that touches customers, colleagues, or production systems, improvisation doesn’t scale. Workflows need to be documented, shared, and tested.

Most new Cookbook examples—coding agents, planning agents, self-evolving agents—are basically workflows implemented with prompts and tools. For developers, you should identify a small number of core workflows like triage, summarization, recommendation, drafting, and QA, and invest in making those bulletproof instead of chasing dozens of niche use cases. This is where prompt libraries, evals, and prompt-as-config systems earn their keep.

For everyday users, whenever ChatGPT helps you with something you’ll need again—status emails, meeting recaps, lesson plans—save the prompt that worked. Next time, drop in new details instead of reinventing it. Five good workflows will change your day more than fifty random “AI tricks.”

⸻

10. New AI literacy: specs plus judgment

In the 5.1 era, “AI literacy” is less about knowing how transformers work and more about two skills: writing simple, non-conflicting instructions—specs—and applying human judgment to the outputs. OpenAI’s docs implicitly assume this: everything is about better instructions and better evaluation, not teaching you matrix math.

People who get the most from 5.1 are the ones who can describe what they want clearly and then decide whether the answer is good enough, needs tweaks, or should be rejected. They don’t just ask “give me something.” They ask “give me this, in this form, and here is how I will use it.”

There’s still value in understanding models at a deeper level—especially for people setting policy, doing safety work, or building infrastructure—but most knowledge workers will never need that. The risk is overconfidence: good-looking answers can hide subtle errors, so judgment has to stay sharp.

The GPT-5 and 5.1 prompting guides, plus OpenAI’s safety write-ups, all implicitly teach “spec plus eval” thinking as the right posture. For developers, your comparative advantage isn’t just knowing models and APIs. It’s designing good human-plus-AI systems: clear instructions, well-chosen tools, sensible guardrails, and monitoring. You become part spec-writer, part product manager, part reviewer.

For everyday users, you don’t need to become a “prompt engineer.” You need to be able to say what you want without contradictions and to look at an answer and decide: trust, tweak, or toss. If you can do that consistently, GPT-5.1 turns from a novelty into a genuinely useful partner.

⸻

What This Actually Means

Every few months we get a new model release, and every time there’s a rush to declare it revolutionary or dismissively incremental. GPT-5.1 is neither. It’s evolutionary in the most important sense: it makes the useful things more reliable and the reliable things more useful.

The pattern is clear. We’re moving from models that require constant coaxing and prayer to models that respect specifications and execute them. We’re moving from one-size-fits-all to configurable modes and personalities. We’re moving from chatbots to orchestrators that can plan, verify, and iterate.

The teams and individuals who win with this aren’t the ones hunting for clever tricks or the perfect prompt. They’re the ones building repeatable workflows, testing them, and treating prompts like the specifications they’ve become. They’re the ones who understand that reliability comes from design—clear instructions, appropriate tools, and human judgment—not from hoping the model gets smarter.

If you’re building production systems, the work ahead is designing those workflows, not just calling a better API. If you’re using ChatGPT every day, the work ahead is defining a handful of workflows that actually matter to you and making them bulletproof.

The model is ready. Let’s have some fun :)

I make this Substack thanks to readers like you! Learn about all my Substack tiers here

Subscribed