Every few months, a groundbreaking new model is unveiled by leading AI companies like OpenAI, Anthropic, Google, and Meta. Each model comes with its own set of strengths and weaknesses, as well as trade-offs in accuracy, cost, and speed.
For developers, researchers, and businesses, this presents a moving target: How do you choose the right model for your specific needs? How often should you upgrade?
The limitations of traditional benchmarks
While AI benchmarks offer valuable insights into model performance across a wide variety of tasks, they only measure one dimension at a time. Real world applications require you to simultaneously balance cost, latency, accuracy, reliability, and tone of voice (among many others).
While benchmarks are a great starting place, the only way to identify the best model for your specific application, is to rigorously compare the performance of your top model candidates against one another.
How do you know which model is “best” for you?
Whether you’re using a chatbot for one off tasks, a prompt-based LLM-powered workflow in production, or looking to fine-tune your own model, selecting the right starting model is one of the most important first steps of any AI application.
Choosing the right model is all about tradeoffs:
Cost Efficiency. Models like Claude 3 Haiku and GPT-4o mini are 12-17 times cheaper than their more powerful counterparts. For many real-world applications, the cost and latency savings outweigh the differences in output quality.
Speed. Faster models mean reduced latency for your users and more efficient workflows for your business.
Accuracy. When precision is paramount, you need to know which model truly excels and is most reliable.
Routinely evaluating different models for every new use case can be a slow and tedious process, especially if it is done manually. But what if there was a shortcut?
Using LLMs-as-a-judge
LLMs now meet or exceed human performance on many tasks. They excel at catching errors, fact-checking, and verifying correct formatting – areas where humans often struggle. They’re also particularly powerful for evaluating how well a prompt/instruction pair was followed. In short, they make pretty good judges.
LLMs-as-a-judge aren’t perfect, but they’re blazing fast and easy to scale. They’re also incredibly adaptable, even without fine-tuning, as long as you provide them with a thoughtful set of evaluation criteria and examples of high-quality answers/outputs.
For the purpose of evaluating model performance, LLMs-as-a-judge provide a perfect starting place. They allow you to quickly compare hundreds or thousands of model/prompt pairs and provide nuanced explanations for their judgments, offering valuable insights into model performance.
Model selection made easy
To help make model selection easier at Aligned, we have been using an internal tool to let us quickly compare the performance of a wide range of models against our most important prompts.
Today, we’re making this tool free and publicly available to all! This is the first in a suite of tools to help you get better performance from your models.
To get started, you can sign up here.
Compare up to 5 different state-of-the-art models at a time.
Evaluate them using up to 3 of your own custom prompts.
Define your own evaluation criteria for the LLM judges.
Get detailed reporting with 100+ judgements on 30 pairwise combinations of model outputs, including detailed descriptions of why one model was preferred over another.
How it works
Model Evaluation: Each selected model processes your custom prompts.
Head-to-Head Comparisons: For each prompt, we compare model outputs in all possible pairwise combinations
LLM Judges: Four top-tier language models (GPT, Claude, Gemini, and LLaMA) evaluate the outputs based on your provided criteria.
Comprehensive Report: Within a minute, you receive a detailed report on model performance with explanations.
The report first summarizes the average win rate and elo score for each model.
Next, is a win/loss table to see the overall and head-to-head performance of each model.
There is a summary from the LLMs-as-a-judge on the reasons they preferred one model over another.
Finally there is a drilldown section for each prompt where you can review the outputs of each model, and see the head-to-head results as well as the reasoning behind each choice made by an LLM-as-a-judge.
Looking ahead: Integrating AI tools with human experts
Model selection is a critical first step in AI development, but we know it’s only one small piece of the puzzle. Follow along with us as we continue to release new tools to get better performance from your models.
At Aligned, we're committed to supporting you through every stage of the AI development process. From our free few-shot example service to our expert-driven data collection for pre-training, fine-tuning, and evaluation, we're here to elevate your AI projects
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