Nehal Gajraj
October 24, 2025
8 min read
October 24, 2025
8 min read
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For developers and product builders, one assumption has guided the last few years of LLM application development. To improve your product, just swap in the latest frontier large language model. Flip a single switch and your tool’s capabilities level up.
But that era is over. We’re now seeing that new models like Anthropic’s Claude Sonnet 4.5 and OpenAI’s GPT-5-Codex have diverged in fundamental ways. The choice of which model to use is no longer a simple engineering decision but a critical product decision. Flip that switch today… and the very texture of your product changes.
The one-size-fits-all model era is over; the model you choose now expresses something integral about what your product is and does, as well as, how it works. Whether you want it to or not.
In this blog, we’ll explore three surprising takeaways from this new era: why your LLM is now a statement about your product, how models now have distinct personalities and styles, and why your prompts have to now evolve from monolithic instructions to adaptive systems.
Choosing a model is no longer a straightforward decision where the main consequence of your choice is having to implement a new API. It is now a product decision about the user experience you want to create, the failure modes you can tolerate, the economics you want to optimize for, and the metrics you want to excel in.
Models have developed distinct “personalities,” ways of reasoning, and instincts that directly shape how your product feels and behaves that go beyond just whether its output is technically right or wrong. Choose a different model and everything from what your tool is capable of to how it communicates with your users is significantly different.
So, in a world where traditional benchmarks that primarily or exclusively measure quantitative aspects of a model’s performance are no longer enough, what can you turn to for the data you need to chart your product’s direction? You could survey your team or your users or conduct focus groups but that could lack objectivity if you don’t do it in a rigorous manner.
To make this choice objective for our team, we focused on creating an internal North Star metrics matrix at CodeRabbit. Our metrics don’t just look at raw performance or accuracy. We also take into account readability, verbosity, signal-to-noise ratios, and more.
These kinds of metrics shift the focus from raw performance accuracy or leaderboard performance to what matters to our product and to our users. For example, a flood of low-impact suggestions, even if technically correct, burns user attention and consumes tokens. A theoretically “smarter” model can easily create a worse product experience if the output doesn’t align with your users’ workflow.
I would strongly recommend creating your own North Star metrics to better gauge whether a new model meets your products’ and users’ needs. These shouldn’t be static metrics but should be informed by user feedback and user behavior in your product and evolve over time. Your goal is to find the right list of criteria to measure that predict your users preferences.
What you’ll find is that the right model is the one whose instincts match the designed product behavior and your users’ needs, not the one at the top of any external leaderboard.
Models are (now more than ever) “grown, not built,” and as a result, the latest generation has developed distinct instincts and behaviors. Different post-training cookbooks have fundamentally changed the direction of each model class. A prompt that works perfectly for one model will not work the same in another. Their fundamental approaches to the same task have diverged.
One powerful analogy that drives this point home is to think of the models as different professional archetypes. Sonnet 4.5 is like a meticulous accountant turned developer, meanwhile GPT-5-Codex is an upright ethical coder, GPT-5 is a bug-hunting detailed developer, and Sonnet 4 was a hyper-active new grad. The GPT-5 model class would make logical jumps further out in the solution space compared to the Claude model class, which tends to stay near the prompts itself. Which model is right for your use case and product, depends entirely on what you are wanting your product to achieve.
At CodeRabbit, we take a methodical approach to model evaluation and characterization. We then use this data to improve how we prompt and deploy models, ensuring we are always using the right model for each use case within our product. To give you an example of how we look at the different models, let’s compare Sonnet 4.5 and GPT-5-Codex. Based on extensive internal use and evals, we characterized Sonnet 4.5 as a “high-recall point-fixer,” aiming for comprehensive coverage. In contrast, GPT-5-Codex acts as a “patch generator,” preferring surgical, local changes.
These qualitative differences translate into hard, operational differences.
Dimension | Claude Sonnet 4.5 | GPT-5-Codex |
Default Word Choice | “Critical,” “Add,” “Remove,” “Consider” | “Fix,” “Guard,” “Prevent,” “Restore,” “Drop” |
Example-Efficiency | Remembers imperatives; benefits from explicit rules | Needs fewer examples; follows the formatting on longer context without additional prompting |
Thinking Style | More cautious, catches more bugs but not as many of the critical one | Variable or elastic, less depth when not needed without need to reiterate the rules. Catches more of the hard-to-find bugs |
Behavioral Tendencies | Wider spray of point-fixes, more commentary and hedging, inquisitive, more human-like review, finds more critical and non-critical issues | Verbose research-style rationales, notes on second-order effects to code, compact and balanced towards a code reviewer |
Review Comment Structure | What’s wrong, why it’s wrong, concrete fix with code chunk | What to do, why do it, concrete fix with effects and code chunk |
Context Awareness | Aware of its own context window, tracks token budget, persists/compresses based on headroom | Lacks explicit context window awareness (like cooking without a clock) |
Verbosity | Higher, easier to read, double the word count | Lower, harder to read, information-dense |
Because the fundamental behaviors of models have diverged, a prompt written for one model will not work “as is” on another anymore. For example, a directive-heavy prompt designed for Claude can feel over-constrained on GPT-5-Codex, and a prompt optimized for Codex to explore deep reasoning behavior will likely underperform on Claude. That means that the era of the monolithic, one-size-fits-all prompt is over.
So, what does that mean for engineering teams who want to switch between models or adopt the newest models as they’re released? It means even more prompt engineering! But before you groan at the thought — there are some hacks to make this easier.
The first practical solution we’ve found at CodeRabbit is to introduce “prompt subunits.” This architecture consists of a model-agnostic core prompt that defines the core tasks and general instructions. This is then layered on top of smaller, model-specific prompt subunits that handle style, formatting, and examples – and which can be customized to individual models.
When it comes to Codex and Sonnet 4.5, the implementation details for these subunits are likely to be starkly different. We’ve found a few tricks from our prompt testing with both models that we would like to share:
Claude: Use strong language like "DO" and "DO NOT." Anthropic models pay attention to the latest information in a system prompt and are excellent at following output format specifications, even in long contexts. They prefer being told explicitly what to do.
GPT-5: Use general instructions that are clearly aligned. OpenAI models’ attention decreases from top to bottom in a system prompt. These models may forget output format instructions in long contexts. They prefer generic guidance and tend to "think on guidance," demonstrating a deeper reasoning process.
The second solution is to implement continuous updates driven by user feedback and internal evaluations. The best practice for optimizing an AI code-review bot or for that matter any LLM applications isn’t using an external benchmark; it’s checking to see if users accept the output.
Evals are more important than ever but have to be designed more tightly around acceptability by users instead of raw performance since one model might be technically correct significantly more than another model but might drown the user in nitpicky and verbose comments, diluting its value to users. By measuring the metrics that matter ~ acceptance rate, signal-to-noise ratio, p95 latency, cost, among others - and tuning prompts in small steps, the system will remain aligned with user expectations and product goals. The last thing you want is great quantitative results on benchmarks and tests but low user acceptance.
This shift from one-size-fits-all prompt engineering to a new model specific paradigm is critical. The days of brittle, monolithic prompts and plug-and-play model swaps are over. Instead, modular prompting, paired with deliberate model choice, give your product resilience.
The ground will keep shifting as models evolve so your LLM stack and prompts shouldn’t be static. Treat it like a living system. Tune, test, listen, repeat.
Also, be sure to check out our published detailed benchmarks on how the latest models behave in production. That gives you more data on what to expect from them.
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