Choosing the Right Model for the Use Case

Introduction

Selecting the right AI model for deployment is a critical decision that can significantly impact the performance, cost, and user experience of your application. With a wide variety of models available—each with unique strengths and trade-offs—it’s essential to evaluate them carefully based on relevant criteria. In this post, we’ll explore the three key factors to consider when comparing models for deployment: quality, cost, and speed. Understanding how these factors interact and influence your application will help you make informed choices that align with your technical requirements and business goals

Comparing Models for Deployment

When comparing models or updating models for deployment there are three primary factors to consider: quality, cost and speed.

1. Quality refers to how well the model performs on your specific task, often measured by its accuracy, reliability, and overall effectiveness. In other words, it reflects the model’s intelligence or ability to generate useful outputs.
2. Cost encompasses the direct expenses associated with using the model. For hosted models, this is typically the price per token. For self-hosted models, cost is determined by the hardware requirements—such as the number and type of GPUs needed to run the model efficiently.
3. Speed (or latency) measures how quickly the model responds to user queries, which is critical for user experience in real-time applications.

These parameters are often interrelated. Larger, more capable models tend to deliver higher quality but are usually slower and more expensive to run. Conversely, smaller models are faster and more cost-effective but may sacrifice some performance or accuracy. Because of these trade-offs, it’s important to evaluate each parameter independently and select a model that best aligns with the needs and constraints of your downstream application.

Measuring Model Quality

Model quality is notoriously difficult to measure for specific tasks. There are broadly two approaches to evaluate the quality of a model:

Task-specific evals:

One effective way to assess model quality is to create a small gold-standard benchmark tailored to your individual task. This involves hand-crafting a set of examples with clear ground truth outputs, allowing you to directly compare how different models perform on the exact requirements of your application. While this approach requires significant effort to develop and maintain, it is highly representative of model performance for your specific use case. As a result, task-specific evaluations often yield a more accurate reflection of model quality than general-purpose benchmarks.

General-purpose evals:

Alternatively, many general-purpose benchmarks exist to measure model performance across a wide range of tasks, such as math, coding, reasoning, and general knowledge. Some of these evaluations are crowdsourced, where users blind-rank model results, and overtime, preference data is aggregated to produce comprehensive model rankings. The two most popular general purpose evals are listed below:

1. Artificial Analysis: The Artificial Analysis website aggregates model performance on standard benchmarks and then combines them into a single score, called the Artificial Analysis Intelligence Index. This is a combination of 8 benchmarks including coding, multiple choice general knowledge, math, and others. A higher score indicates stronger performance across these diverse benchmarks.

2. LMArena: LMArena presents users with two model responses and asks them to choose their preferred answer. Rankings are then published based on user preferences. This method is robust against models being over-optimized for benchmarks, but it’s important to note that the preferences of LMArena’s user base may not align with your own evaluation criteria. For example, LMArena users often favor longer, heavily formatted answers, which may not suit applications that require concise or specifically formatted outputs. LMArena uses an Elo rating system for its leaderboard, where a higher score indicates that a model is preferred more often by users in head-to-head comparison.

While general-purpose evaluations provide useful context for comparing models, task-specific evaluations remain the gold standard for assessing quality in the context of your unique application. Combining both approaches can offer a more comprehensive understanding of model performance.

Measuring Model Cost

Comparing model costs between API-based and self-hosted models can be challenging, as they use different pricing structures. API based models charge a pay-per-token fee, while self-hosted models require an upfront investment in hardware. The size and complexity of the model will determine the amount and type of hardware needed. You can estimate these costs by reviewing the pricing of various virtual machines or GPU instances from cloud providers. This link is helpful to get a rough estimate.

Measuring Model Speed

When measuring model speed, it is not quite enough to simply send the same request to every model and measure the total response time. This is because two models will generally produce a different number of tokens to the same response. One model might be 50% faster per token, but return twice as many tokens in response. This would make this model appear slower end-to-end, despite being faster per token. Reasoning models, like GPT-5, add additional complexity to this. Reasoning models will spend some time ‘reasoning’ by producing ‘thinking tokens’ before starting their actual response. Additionally token lengths can be affected by prompting. A normally verbose model can be made to produce fewer tokens by asserting that it needs to be brief in its response. This will impact the model’s answers in your task, and this makes it hard to measure independently.

The importance of model speed, or latency, depends largely on how your application is used. For real-time, interactive applications, such as chatbots, even small increases in latency can noticeably impact user experience, making low-latency models a critical requirement. On the other hand, if your workloads are processed in batches - such as overnight data analysis - slightly higher latency may be acceptable, especially if it results in improved model quality or accuracy. Ultimately, the right balance between speed and quality should be determined by the specific needs and expectations of your end users.

Conclusion

Selecting the right model for deployment requires a careful balance between quality, cost, and speed. As demonstrated in this analysis, each factor plays a significant role and often involves trade-offs that must be weighed according to the specific needs of your application and users. Ultimately, the best model is the one that aligns most closely with your application’s requirements and goals.