GPT-5.1 First Look: Smarter, Warmer… But Not a Breakthrough

2025’s flagship model season kicked off yesterday with the unexpected arrival of GPT-5.1, with OpenAI getting their release out before Gemini 3. While we’re still waiting for API access (and therefore can’t run proper, high-volume benchmark testing yet), we can take a close look at the release notes, early examples, and some small-scale hands-on tests within ChatGPT.

Here are my early impressions - what actually improved, how it compares to the wider market, and whether I think most teams should upgrade.

5.1 Arrives… Without Mini or Nano

OpenAI only released the flagship model - GPT-5.1 - along with two interface variants:

• GPT-5.1 Instant
• GPT-5.1 Thinking

It’s not clear yet whether these are actually different models or simply two endpoints on the “hybrid reasoning” spectrum (my guess: same base model, different configuration).

Notably missing, though, are Mini and Nano versions. That’s unfortunate for a lot of teams - especially because GPT-5 Mini and Nano never really offered compelling value against similarly priced Google models (Gemini 2.5 Flash basically beat them across the board). A new, stronger mini-model would have mattered.

But for now, we’ve only got the flagship.

A Warmer, More Conversational Default Style

One of the loudest complaints about GPT-5 was that it felt robotic - especially compared to GPT-4, which many people used for emotional conversations, therapy-style support, or simply “having a chat.”

OpenAI’s first major change in 5.1:

making the default tone warmer, more human, and more empathetic.

Examples show clear differences

In OpenAI’s own comparison:

• GPT-5 jumps straight into tasks
• GPT-5.1 pauses, interprets emotional subtext, and responds more like an actual person

It also changes the formatting - fewer numbered lists, fewer click-bait-like headers, more conversational grouping (“If your mind feels scattered”, “If you need to unwind”).

Is this groundbreaking?

No - Gemini 2.5 Flash and Pro have been doing this for months.

But GPT-5.1 finally catches up, and for many people, that alone will make ChatGPT feel better to use.

Instruction Following: A Real Fix at Last

Next up: length control and instruction adherence.

OpenAI’s example uses a trivial test - “reply in exactly six words” - but it highlights a real problem: GPT-5 (and most OpenAI models) tended to start following instructions, then drift into long, rambling outputs.

GPT-5.1 fixes this.

Not only does it obey the constraint, it does so reliably across multiple turns.

Again, this wasn’t something Google struggled with - Gemini 2.5 has been rock-solid on short-form constraints. But this update finally makes OpenAI competitive again for:

• LinkedIn posts
• Email snippets
• Micro-copy
• Chatbots
• Any product where concise outputs matter

It’s a quality-of-life win more than a headline feature, but a meaningful one.

Adaptive Reasoning: The Big, Unproven New Feature

This is the feature OpenAI is marketing most heavily:

GPT-5.1 can now decide how much “thinking” to do based on task difficulty.

In theory:

• Easy tasks → respond faster with fewer tokens
• Difficult tasks → think longer, reason deeper

OpenAI claims:

• 57% fewer tokens used on easy tasks
• 71% more tokens used on difficult tasks

But there’s a catch:

We don’t know how they defined “difficulty,” and in early tests I couldn’t consistently reproduce the behaviour.

Some quick tests I ran:

Right now, the pattern isn’t clear.

The danger is obvious:

If “hard” tasks always trigger longer thinking, you might just end up paying more for the same outcome.

Until we can benchmark this with API access, the jury is still out.

Clearer Thinking Output

Another improvement: reducing jargon and making the “thinking” traces easier to understand.

This matters for hybrid-reasoning models because previously:

• The thinking section was often dense, mathematical, or filled with undefined abbreviations
• It made the final output harder to control
• It sometimes caused formatting issues in production apps

GPT-5.1 definitely explains itself more clearly - but so does Gemini if you simply ask it to. It’s an improvement, but not an industry-moving one.

Tone, Style, and Personalisation

One genuinely useful part of the release is the new tone/style personalisation inside ChatGPT.

While I won’t use this feature personally within the interface, it might signal something bigger:

👉 better tone control through the API

If true, this could help for:

• creative writing
• sales copy
• brand-voice-specific email generation
• personalised tutoring
• emotionally aware chatbots

This is one area where OpenAI historically had the edge over Google, so an improvement here matters.

So… Should You Switch Your Stack to GPT-5.1?

Right now?

Probably not - unless you care a lot about tone/emotional warmth.

Here are my recommendations based on the evidence so far:

Worth upgrading if your use case involves:

• emotionally intelligent chatbots
• therapy-ish conversations
• tutoring or mentoring tools
• creative writing
• short-form writing where tone matters more than raw correctness

Not worth upgrading yet if your use case involves:

• analysis
• structured reasoning
• math
• data interpretation
• high-volume, low-latency automation
• code generation
• anything requiring predictable reasoning cost

Frankly, GPT-5.1 seems designed mainly to improve the ChatGPT user experience, not to dethrone Gemini 2.5 Pro in pure performance.

The Competitive Landscape

This is the first OpenAI release in a while that feels like:

“catching up to Google”

rather than

“pushing the state of the art.”

If Gemini 3 is genuinely a major leap over 2.5 Pro, GPT-5.1 might feel outdated within a week.

But I’m also encouraged by the version number.

We now have:

• GPT-5
• GPT-5.1
• (hopefully in the future) GPT-5.2, 5.3, etc.

If OpenAI moves to a faster iteration cadence, these incremental improvements may stack in interesting ways.

Final Thoughts

GPT-5.1 isn’t groundbreaking - but it’s a solid, necessary step. It makes ChatGPT better for everyday users, fixes some longstanding issues with instruction following, and introduces adaptive reasoning (which might matter once we test it properly).

For now, though, I won’t be telling clients to rush into upgrading.

We’ll revisit all of this once the API drops and we can run large-scale tests across structured benchmarks, coding tasks, real-world prompts, and our own SpringPrompt eval suite.

If you’re interested in an applied breakdown of how GPT-5.1 might fit into your own LLM stack - or whether it’s worth switching from Gemini or Claude - feel free to reach out. I’m happy to run a quick prompt/LLM audit and give honest recommendations.