When GPT-5.1 replaced GPT-5 as ChatGPT’s default model, many users were understandably cautious. The debut of GPT-5 sparked frustration—from sudden personality shifts to inconsistent instruction-following—so it wasn’t unreasonable for people to brace for another round of turbulence. But the reaction this time has been noticeably calmer. And after spending significant time testing GPT-5.1, it’s easy to understand why: the new model directly fixes the most common complaints that defined GPT-5’s launch.
GPT-5.1 isn’t marketed as a radical technological milestone. Instead, it represents a deliberate refinement—a patch that feels more like what GPT-5 was supposed to be from day one. It’s more obedient, more personable, more consistent, and noticeably better at explaining its reasoning. These changes aren’t loud or flashy, but they add up to something meaningful.
To explore those improvements, I ran a series of controlled tests comparing GPT-5.1 to GPT-5. These tests focused on the exact areas where OpenAI claims the new model performs better. The results? GPT-5.1 doesn’t just edge out GPT-5—it makes going back feel like a downgrade.
Below is the full comparison.
1. Instruction Precision
GPT models often struggle with overly specific commands, so I designed a tricky prompt to test the precision improvements claimed for GPT-5.1. I asked both models to summarize The Lion King using:
- Exactly four sentences
- Language accessible to a seven-year-old
- No “baby talk”
- No sentence beginning with “Simba” or “The”
GPT-5 attempted to follow the rules but slipped immediately. It started one sentence with “The,” violating the most basic constraint. The summary itself was fine, but the prompt was clear—and GPT-5 still bent the rules.
GPT-5.1, on the other hand, delivered a razor-clean response. It obeyed every parameter, included more useful detail, and maintained the required reading level without becoming childish. It even worked in character names naturally, something GPT-5 struggled with in earlier versions.
This single test demonstrated an important change: GPT-5.1 isn’t merely following instructions—it’s respecting them. The model feels more attentive, more exact, and far less prone to drifting away from the boundaries of a prompt.
2. Warmth and Conversational Clarity
One of the loudest criticisms of GPT-5 was its tone. Many users felt it sounded detached, overly formal, or “robotic.” GPT-5.1 was specifically tuned to feel more natural—even without swapping into one of its optional personality modes.
To test this, I used a tone-sensitive prompt:
“Explain why people get motion sickness in a way that feels like a normal conversation, not a science textbook. Keep it under 150 words and don’t talk down to me.”
GPT-5 handled the science correctly but reverted to dense explanations and stiff phrasing. It felt like a lecture—competent, but not conversational.
GPT-5.1 struck a noticeably different tone. It delivered a friendly, relatable explanation without oversimplifying. It described the conflict between eye signals and inner-ear signals in plain language and wrapped things up with a disarming, almost humorous line about the brain “not loving the experience.”
The answer felt human—not scripted, not stiff, and not infantilizing. In other words: exactly what users wanted GPT-5 to be.
3. “Show Your Work” — Transparency in Reasoning
Another area where GPT-5.1 promised improvement is its ability to walk users through its logic. To test this, I posed a simple word problem:
A 142-mile trip, car gets 27 MPG, gas price is $3.79.
How much fuel will I use, and what’s the approximate cost?
GPT-5 answered correctly, but the reasoning felt mechanical and overly formal. The explanation appeared as though it were preparing for a physics class, not helping a traveler.
GPT-5.1 provided a cleaner and more practical breakdown:
- Divided the mileage succinctly
- Interpreted the decimal naturally
- Rounded the numbers the way an actual person would
- Provided a realistic estimate rather than a rigid number
Its summary—“around twenty dollars total”—mirrored the type of estimate you’d give a friend. GPT-5.1 seems to understand why you’re asking, not just what you’re asking.
4. Facial Consistency in Image Edits
Next, I tested visual coherence—specifically whether the model could maintain a consistent face when generating alternate versions of an image.
I used a photo of myself and asked both models to create two variations:
- Same face, different hairstyle
- Same face, but wearing a full ringmaster costume
GPT-5 struggled with both.
In the hairstyle edit, the face drifted to the point where it looked like a different person wearing similar clothes. The outfit details changed unnecessarily, and the bow tie color shifted even though the prompt restricted edits to the hair.
GPT-5.1 showed significant improvement.
It kept:
- The same facial structure
- The same clothing
- The same lighting and body posture
Only the hairstyle changed—and while the mohawk wasn’t perfect, the model obeyed the instructions far more faithfully.
In the ringmaster edit, GPT-5 performed better but still introduced cartoony textures and inconsistent outfit elements. GPT-5.1 replaced the outfit cleanly while maintaining the face and environment with surprising precision.
This is still an area where generative models struggle, but GPT-5.1 showed measurable progress and far fewer distortions.
5. Fashion Recognition and Visual Reasoning
The final benchmark involved interpreting a real outfit from the same photo. I asked both models to classify it as casual, business-casual, or dressy—and to justify the classification using only what was visible in the image.
GPT-5 delivered a safe, somewhat hesitant answer. It leaned toward business-casual but wavered, clearly uncertain about how to interpret the bow tie.
GPT-5.1 offered a much firmer analysis. It confidently identified the blazer, dress shoes, and coordinated bow tie as markers of a dressier outfit. It stayed within the rules of the prompt, avoided speculation, and demonstrated a clearer understanding of visual cues.
This test highlighted something new: GPT-5.1 isn’t just better at recognizing individual clothing items—it synthesizes them into a coherent style judgment. Its reasoning feels grounded, not generic.
Incremental but Meaningful Progress
After comparing both models across several categories, the improvements in GPT-5.1 become undeniable. It consistently shows:
- Sharper instruction obedience
- More natural, human-like tone
- Cleaner logical explanations
- Better fidelity in image editing
- More confident and accurate visual reasoning
These aren’t revolutionary leaps. They’re refinements—tightened screws, smoother gears, better balance. GPT-5.1 feels less like a new engine and more like a precisely tuned version of the existing one. But sometimes, refinement is more impactful than reinvention.
GPT-5 remains a capable model. It’s fast, versatile, and powerful. But GPT-5.1 adds polish that makes an everyday difference. It’s more dependable, more predictable, and more pleasant to use—qualities that matter enormously in real-world workflows.
If GPT-5 laid the foundation, GPT-5.1 feels like the version built for real users.
And maybe that’s the point. GPT-5.1’s improvements suggest OpenAI is preparing for the next big leap—perhaps GPT-6, or whatever comes after. If this release is any indication, OpenAI is tuning the system before introducing genuinely new architecture.
For now, GPT-5.1 replaces GPT-5 not with fanfare but with quiet confidence. And after using both side by side, I find it hard to imagine switching back.
GPT-5 isn’t obsolete—but GPT-5.1 is simply better where it counts.