Beyond the Scoreboard: How to Actually Choose the Right AI Model
AI benchmarks help developers track model progress, but real-world utility requires hands-on testing. Industry experts explain how to evaluate models beyond marketing scorecards.
Every month or so, an artificial intelligence company releases a new model, claiming it is the best in the world. This announcement is typically accompanied by a table filled with mysterious benchmark names, demonstrating how the new model outperforms its competitors across the board. Benchmarks are a core tool for measuring AI capabilities, but how well do they actually reflect real-world performance? To answer this question, one must first understand how these tests are structured. A benchmark is a collection of tasks designed to evaluate a specific ability. Some tests assess knowledge in fields like mathematics and biology through multiple-choice questions, while others require the model to perform practical tasks, such as debugging code. When various models take the same test, their results can be compared to track improvements between versions.
"A benchmark comes with a purpose. It is meant to measure something very specific, and every benchmark tests something different," explains Omer Nevo, CTO at Irregular, a company that develops testing frameworks for the world's leading AI labs. "We specifically focus on benchmarking dangerous capabilities. That means the better a model scores on our tests, the more potential harm it could theoretically cause. In a sense, it is sometimes better not to succeed in these benchmarks. High scores may require delaying a model's release or deploying it only to trusted partners, as OpenAI and Anthropic have done." For AI developers, these evaluations provide a tracking mechanism to identify where a new version has improved and where it still struggles. "A model with stronger benchmark scores tends to be an upgraded model," Nevo notes, while emphasizing that "there is no one-to-one correlation between benchmarks and real-world usage."
The Challenge of Data Contamination
One of the central measurement challenges is ensuring the model has not been pre-exposed to the answers. Nevo points out that AI models are trained on vast amounts of data, much of it scraped from the internet. When test questions, answers, or explanatory solutions are published online, they risk leaking into the training datasets. "This is how a model learns to pass the test instead of truly acquiring knowledge," Nevo explains. "As a result, the benchmark makes the model look phenomenal and capable of everything, yet it may fail on equivalent, unseen questions." One way to counter this issue is by keeping evaluation sets confidential. According to Nevo, more organizations now deploy proprietary evaluations that they run internally without publishing every question and task, thereby reducing the risk of data leakage. However, this creates a new drawback: researchers and the public have less transparency into what is precisely measured and how scores are determined, requiring stakeholders to trust the testing entity.
Moving Beyond Standard Metrics
Even when a model is shielded from test leakage, benchmarks inevitably lose their value over time. As models improve and successfully solve nearly all questions, it becomes harder to differentiate between their capabilities. Consequently, the field demands the continuous development of novel tests that are sufficiently difficult yet meaningful. A task that is too hard will cause all models to fail without yielding useful insights. When an AI lab presents a new model's scoreboard, users should examine which domains show improvement and whether they align with practical needs. An advantage in programming benchmarks may interest software developers, but it does not directly answer whether the model can draft superior text. Nevo recommends searching published results for capabilities close to your intended use case and treating them as an initial filter.
It is also prudent to evaluate the volume of benchmarks a company chooses to display. According to Nevo, some labs avoid publishing results in categories where they lag behind. "When a company publishes very few benchmarks, it is a strong signal that things are not going exceptionally well," he states. Nevertheless, viewing benchmark victories is merely a starting point. "Seeing a model win benchmarks is a good sign," Nevo says, "but it is significantly less accurate than trying it out yourself." Beyond benchmark metrics, practical adoption involves variables such as user interface convenience, pricing, and rate limits. Tzof Bar Or, an AI content creator and CTO at Super8, compares the choice to buying a vehicle: "If I need to drive long distances, I won't buy a racing car." Bar Or emphasizes the importance of evaluating response latency and usage quotas before hitting platform limits. "I want not only the strongest model in the world, but also one I can use continuously within my budget," he explains. Not every task demands the highest-tier model; simpler alternatives can often deliver equivalent results, reserving heavy-duty models for complex workloads.
How to Build Your Own Evaluation
To understand how a tool performs in daily operations, Bar Or recommends monitoring user communities on platforms like X and specialized forums. "Because these models change frequently, there is no substitute for the community's vibe," he notes. Still, enthusiastic online reviews must be tested independently. "The answer to why I use a tool should be 'because I tested it and it works for me,' not 'because influencer X online said so.'"
Nevo suggests starting with your own past workflows: take tasks previously assigned to AI and re-run them using the candidate model. "I would take something as closely aligned with my actual use case as possible, and the best option is to use your actual production workflow," he advises. Tests should also include prompts where previous models failed. To ensure a meaningful comparison, success criteria must be defined in advance. "Did I ask it to write code? Does the code execute? Did I ask a question? Is the answer correct?" Nevo asks. By testing across multiple dimensions, users build a compact, personalized benchmark tailored to their specific needs—one that can be reused whenever a new generation of models emerges.
