Which Models Work Best for Your Enterprise AI Use Case: Enterprise Model Trust Scores Reveal the Answer

We’ve heard from customers that they don't know which AI models to choose based on their business needs, risks and goals. Not all use cases should be treated equally—some carry higher risks and require deeper evaluation, while others are lower risk and can be fast-tracked. To help you make these decisions, Credo AI is introducing the industry’s first use-case-based AI model leaderboard, Model Trust Score, enabling enterprises to select AI models based on several requirements.

We developed the Model Trust Score framework, which assigns a set of model trust scores to each model for a specific use case. When you are evaluating a model using our framework, you are checking its model trust scores—each model has a set of scores (capability, safety, affordability, speed, and overall score) which are all model trust scores. You can explore our Model Trust Score website and soon these Model Trust Scores will be available directly within the Credo AI Platform, enabling enterprises to make smarter, safer AI adoption decisions tailored to their real-world needs.

As AI capabilities evolve at breakneck speed, enterprises are faced with an increasingly complex challenge: selecting the right AI model for their specific needs. DeepSeek V3 and R1 are the latest examples of frontier models becoming more readily available and accessible to anyone who wants to use them. Rather than fundamentally changing the landscape, these models highlight an accelerating reality: AI capabilities are becoming cheaper, more powerful, and more accessible. More providers and tools enter the market daily. At best, each comes with unique strengths and weaknesses. At worst, some may come with big risks and liabilities for enterprises.

This abundance brings more choices to enterprises today—and creates increasingly complex selection challenges. With so many options—many of which boast unique strengths—how can enterprises  make informed decisions that balance performance, security, cost, and other business critical tradeoffs?

Often, the first step to understanding which model might be a good choice is to compare them using benchmarks. However, benchmarks are often poor indicators of which model is the best choice for your use case. That's because benchmarks aren't representative of the real-world context in which the model will actually be used. As such, benchmarks don't effectively stress-test in adversarial use case specific scenarios.

To solve this, Credo AI developed the Model Trust Score, industry’s first  approach, tailored to the needs of your enterprise.
The Problem: Too Many Choices, Too Little Clarity

AI innovation has led to a proliferation of models, each optimized for different tasks. While benchmarks provide surface level performance comparisons, they fail to capture the nuances that matter the most to your enterprise—how a model aligns with real-world business needs, operational constraints, risk tolerance and market requirements.

Selecting the AI model fit for your enterprise purpose isn’t just about accuracy—it’s about making trade-offs across security, infrastructure fit, compliance, cost, latency. Relying solely on benchmarks is insufficient; a model that excels in a test environment might fail to meet real-world requirements. Worse, a highly capable model might introduce unacceptable risks, such as security vulnerabilities or legal concerns. In high-stakes environments, a poorly chosen model isn't just inefficient—it can be a liability.

Generally, benchmarks give valuable early signals about a model's applicability to a use case, but as development progresses, developers create bespoke evaluations that are specific to their use case. So, too, should context play a role in initial model selection. Understanding the specific use case should inform our interpretation of benchmarks, focusing attention on the dimensions that matter most, and highlighting opportunities for evaluation innovation.

These are the reasons that we developed the Model Trust Score leaderboard, a data-driven approach to confidently choose AI models that are performant, trustworthy, and enterprise-ready.

Model Trust Scores: Redefining  Enterprise AI Model Selection

Selecting the model fit for a given use case demands systematic thinking. This isn’t a simple task, but we can break it down methodically. The Model Trust Score leaderboard helps enterprises systematically evaluate AI models based on two components of analysis:

1. Filtering Based on Non-Negotiables: First, companies filter models based on essential security, compliance, and infrastructure requirements. If a model doesn’t meet these baseline criteria, it’s immediately ruled out.
2. Tradeoff Analysis with Model Trust Scores: Once a model passes the baseline criteria, businesses assess its fit for purpose based on four key dimensions which collectively form a set of Model Trust Scores:
   
   1. Our Model Trust Score framework assigns a set of trust scores to each model for a specific enterprise use case.
   2. When evaluating a model using this framework, you assess its trust scores—including capability, safety, affordability, speed, and an overall score.

The Model Trust Scores framework combines these two components of model selection to provide a ranking of models based on their fit for purpose for specific industries and enterprise AI use cases. These rankings can help AI governance teams to set better constraints for AI developers, eliminating models that don’t meet baseline business requirements for risk, cost, or performance.

The Model Trust Scores leaderboard helps enterprises move beyond benchmarks for more tailored and context-driven model evaluation. Standardized benchmarks, while useful, often fail to capture how a model will perform in a specific industry or use case. Our leaderboard improves on this by incorporating relevance scoring, a system that weights different benchmarks based on their applicability to real-world applications.

One of the most critical—and often overlooked—aspects of AI model selection is balancing capability and safety. A highly capable model may generate impressive results, but if it lacks robust safeguards, it can introduce legal risks. Conversely, an overly restrictive model might be safe but too limited to be useful.

Credo AI’s Model Trust Scores address how much extra effort you're going to have to put in to make a more capable model meet your safety needs. We’ve detailed this framework in a whitepaper on Model Trust Scores here.

The Future of AI Model Evaluation: Towards Trusted and Enterprise-Ready AI

As enterprises scale AI adoption, the challenge is no longer just about choosing the most powerful model—it’s about selecting the most responsible and contextually appropriate one. Credo AI is integrating Model Trust Scores into its governance platform, helping organizations make informed, data-driven decisions. Model Trust Scores, along with other key transparency insights about top LLMs and enterprise AI vendors, will be available via Credo AI’s Model & Vendor Registries, part of a comprehensive suite designed to help enterprises evaluate, govern, and mitigate the risks of third-party AI.

The future of AI evaluation must move beyond generic benchmarks toward industry-specific, risk-aligned assessments, standardized certification frameworks, and greater transparency from model providers. Credo AI is partnering with the ecosystem to ensure development of a secure and transparent AI value chain with tooling for AI assurance.

Final Thoughts: Elevating AI Model Selection For Your Enterprise AI

Choosing the AI model fit for your enterprise needs isn’t just about picking the one with the highest benchmark score—it’s about understanding the strategic trade-offs and ensuring alignment with business priorities. With Credo AI’s Model Trust Scores, organizations can cut through the noise and select AI solutions that are not only powerful but also practical, enterprise-ready, and safe for real-world deployment.

As AI continues to shape industries and provide more opportunities for businesses to compete, the ability to make informed decisions about model selection will be a competitive advantage. By prioritizing both capability and responsibility, businesses can unlock AI’s full potential while mitigating risks.

Interested in applying Credo AI’s Model Trust Scores to your AI decision-making? Let’s talk.