We are spoilt for choice today when it comes to AI models. Multiple models are available today that have top-of-the-line intelligence, can be either open-source or closed, and can be hosted nearly anywhere in the world. The model providers themselves have also made an effort to standardize their API interfaces. As an example, both Anthropic and Gemini models can be accessed via the OpenAI SDK, making it almost as simple as one line of code change to move from model to model.
Beyond official SDK support, an ecosystem of interoperability frameworks has arisen to help developers avoid getting tied to any single model or provider. Tools like LangChain and LiteLLM abstract away the specifics of each model’s API, allowing developers to swap out back-end LLMs with minimal changes.
It’s easy to see the surface-level appeal of such technical interoperability.
Companies building AI applications are understandably wary of betting the farm on a single model or vendor. The AI space is moving too quickly for comfort and today’s leading model might be eclipsed next month by a cheaper, faster, or more accurate alternative. No one wants to be stuck with yesterday’s tech or locked into high prices. Many teams also want resilience and flexibility. What if your chosen provider has an outage or policy change? Being able to switch to another model quickly can keep your application running. This is why frameworks for runtime model switching and “hot swap” capabilities are getting so much attention. Architecting your solution to be model-agnostic lets you pick the right model for each task and update that choice as models improve. On paper, technical interoperability is a no-brainer for any company that wants to stay agile and avoid vendor lock-in.
So Model Interoperability is Easy, or is it?
The reality is that making your application truly model-independent requires much more than a compatible API call. At Newtuple, we’ve learned first-hand that simply swapping one LLM for another can lead to unpredictable behavior and significant risk if not done carefully. Technical interoperability may solve the integration problem but it doesn’t automatically solve the quality and consistency problem. Each model has its own “personality” and quirks, which means two compliant LLMs given the same prompt can produce divergent outputs. Unless you account for these differences upfront, you might find your application breaking or underperforming when you flip that switch.
Example 1
To illustrate, consider our recent engagement with a legal tech startup. Their application was initially built and tuned on Sonnet 3.5. The client wanted to try OpenAI’s models for reasons such as cost and a larger context window. We develop all our applications to be extremely modular, so the integration itself was straightforward.
But the outcomes were not.
The same LLM pipeline that yielded accurate answers with Sonnet 3.5 started returning subtly different answers with GPT-4o.
On paper, both Sonnet 3.5 and GPT-4o are high-performing LLMs and have very close scores on any benchmark, but they have different strengths. In our case, we observed GPT-4o followed the instructions in the prompt very literally. It stuck exactly to the format and phrasing requested, whereas Sonnet 3.5 had been a bit more interpretive in filling in the intended details.
The result? 4o sometimes omitted contextual nuances that Sonnet would implicitly include because Sonnet tended to “read between the lines” more. These differences in how the models handled the task led to lower accuracy on the client’s evaluation metrics when we switched over. We had to go back and adjust prompt templates and post-processing logic to realign the outputs, which introduced unexpected delays. The supposed quick swap turned into a cycle of benchmarking and retuning to reach parity. This isn’t to say one model was better than the other overall – but it was better for the specific pipeline as originally written. Switching models meant those prompts had to be revisited.
Example 2
We saw a similar phenomenon with a financial services client working on an agent-based system for structured data extraction from thousands of documents. Their solution uses a multi-agent framework to parse financial statements, where the agent would chain through multiple steps (finding relevant sections, extracting figures, performing calculations, etc.).
Initially, they prototyped the agent with a particular model that had a more creative reasoning style, which helped in interpreting loosely structured sections. When we tried switching the agent to a different provider’s model, we observed the agent’s performance on certain steps diverged significantly. Some models would follow the step-by-step instructions rigorously but unimaginatively, getting stuck if the data wasn’t in an expected format. Others were more flexible and could infer what to do but at the cost of occasionally going off-script. In one test, Model A successfully extracted a complex figure across two tables because it improvised a method to join the data, whereas Model B (with the same agent code) gave up or returned an incomplete result because it strictly followed the given step-by-step approach without that leap. We had essentially uncovered that each model has distinct problem-solving tendencies. Even though both models were plugged into the same agent framework (same prompts, same tools), the outcomes differed. Without careful evaluation, blindly switching would have meant the client shipping a less reliable system.
The takeaway
These examples underscore a key point: LLMs are not commodities that can be swapped in and out like identical parts. This also highlights that application tuning often ends up model-specific: what works well for one model might underperform for another. Even small AI engineering choices can have different effects. We’ve observed that something as subtle as where you insert retrieved context (e.g. results from a vector database lookup) can yield very different outcomes between models. Placing the context paragraphs at the top vs. the bottom of the prompt might not matter for one LLM but could change the behavior of another entirely. One model might treat top-of-prompt context as gospel and base its answer solely on that, while another model might blend it more with the user query if it’s placed later. These idiosyncrasies mean that naive one-to-one model replacement – without adjusting for the model’s “personality” – can undermine your application’s performance.
How to Really Approach Interoperability
So, does this mean pursuing model interoperability is a bad idea? Not at all. It is desirable to keep your options open – but it must be done with a proper process and respect for the complexities involved. In our experience, successful LLM interoperability requires going beyond just technical API compatibility and achieving what we might call operational interoperability. This means instituting the right practices to evaluate and validate any model changes systematically. Over time, Newtuple has developed a structured approach to help clients safely navigate model switching:
Define “Good Output” Early and Rigorously: From the start of your project, establish clear qualitative and quantitative criteria for what constitutes a good result for your use case. This might be a set of example Q&A pairs the model should handle, a desired format for responses, accuracy targets on domain-specific test queries, or even multi-turn interaction guidelines for agents. Having this golden dataset makes it easier to objectively compare models. For multi-turn agent workflows, define the expected agent behavior or successful end state before worrying about which model is powering it. Essentially, you want a reference to validate against. When we began working with the legal tech client, we helped them compile a golden dataset of legal questions and expected answers. That proved invaluable later when evaluating outputs from different models side by side to see which model actually met the “good output” criteria more often.
Implement Rigorous Evaluation and Testing Pipelines: Treat each LLM (and prompt chain) as a versioned component in your stack. Whenever you consider switching models (or even just upgrading to a new version of the same model), run extensive tests using a fixed evaluation dataset and track the results. Automated evaluation tools can measure accuracy, relevance, or consistency against your expected outputs. We often set up regression tests for LLM behavior – not unlike unit tests in traditional software – that get triggered whenever we swap the model or tweak prompts. These include quantitative metrics (e.g. exact match accuracy, BLEU score for expected text, etc. depending on the task) and qualitative reviews for things harder to measure (like tone or adherence to guidelines). It’s important to integrate these evaluations into your development pipeline. For example, before fully deploying a new model, you might run a batch job that compares its answers to the previous model’s answers on 1000 sample inputs and flags significant differences for review. Maintaining version control over prompts, chain logic, and model parameters is also critical – that way if a switch causes a drop in performance, you can pinpoint whether it was the model or some other change. In short, test, test, test – and back your decisions with data. Proper evaluation reduces the guesswork when dealing with models that evolve or behave differently.
Establish Objective Criteria for Model Switching: Finally, have a clear rubric for when and why you would switch models in production. The choice of LLM should ultimately serve the business needs, so define those needs explicitly. For instance, you might decide that if Model X and Model Y have comparable accuracy, latency will be the tiebreaker (maybe you need faster responses for a better UX). Or you might prioritize cost if budget is a concern – e.g. only switch if the cheaper model maintains at least 95% of the accuracy of the current model. Other criteria could be context window length (if your application needs to feed in huge documents, a model with 100k token context might be non-negotiable), or even strategic considerations like provider stability and support. Some companies also leverage provider diversification incentives – for example, using an alternative model when it comes with better data privacy terms or to avoid hitting rate limits on the primary provider. The key is to quantify these factors where possible. It’s much easier to make a decision (and to justify it to stakeholders) if you can say: “Model B costs 50% less and is only 2% less accurate on our tests, so the cost savings outweigh the small accuracy drop,” or “Model C would reduce latency by 200ms but our evaluation shows it misses 1 in 10 key facts, which is unacceptable by our quality bar.” By creating an objective scorecard (accuracy, latency, cost, etc.) you ensure model switching isn’t driven by hype or pressure, but by measured business benefits.
In our engagements, we’ve found that following the above steps brings much-needed discipline to the interoperability process. By the time you are nearing production readiness, you should have this framework in place. It’s fine (even smart) to experiment with multiple LLMs during R&D, but once you start converging on a solution, you need to lock down what “good” looks like and put guardrails around changes.
LLM Model interoperability does not mean constantly swapping models on a whim – it means having the freedom to swap when it makes sense, with confidence in the outcome. It’s akin to a multi-cloud strategy in traditional IT: you abstract your deployment so you can move between cloud providers, but you still do cost-benefit analysis and thorough testing before actually moving a production workload from, say, AWS to Google Cloud.
Conclusion
Technical interoperability (unified APIs, multi-model SDKs, etc.) is just the beginning. It solves the integration headache and is a necessary foundation – indeed, it’s an important development that today we have tools and standards making LLMs more plug-and-play across vendors. But, true interoperability in a production AI system is more nuanced. It requires operational maturity: solid evaluation pipelines, careful change management, and an understanding of each model’s distinct behavior. The goal is to reap the benefits of flexibility (avoiding lock-in, leveraging the best models for the job, negotiating better pricing, and ensuring uptime through fallbacks) without sacrificing quality or reliability. Achieving this balance is harder than it looks at first glance, but it’s doable with a methodical approach.
Bottom line: If you plan for interoperability from day one – define your success criteria, test rigorously, and know your switch triggers – you’ll be in a strong position to take advantage of the rapid advances in LLMs. You’ll be able to swap out models confidently when a better option comes along or use a mix-and-match approach to get the best of each model, all while delivering consistent results to your end users. In a field that’s moving as fast as generative AI, that agility can be a game-changer. Just remember that every model swap in production is a big deal that needs to be treated with the same care as a major code deployment. With the right preparation, you can have both interoperability and stability – and truly future-proof your LLM-powered applications.
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