The Best AI Model is the One You Use

Nearly every time I talk to someone about AI engineering, the same question comes up: "Which model is the best?"

It is a logical question, but it has a frustrating answer. The reality of the situation is that everything is changing so fast and so often that "best" is a moving target. Every major AI company is competing with each other and upping the table stakes so frequently that the top spot is usually held by whoever released their update most recently.

Generally speaking, recency from a reputable company like OpenAI, Anthropic, or Google is the dominant factor in model performance. While there are smaller projects and companies that stay behind or release less impressive models, they are the exception. If you see news articles covering a new model release, it is likely coming from a company large enough to produce something worth your time.

The Photography Lesson

There is a classic adage in photography: "The best camera is the one you have with you."

The same logic applies to AI tools. The best tool right now is the one you actually use and that provides value for your specific workflow.

There are a hundred things you could try with AI today that would be disappointing or frustrating because the current tooling is still evolving. The most important skill you can build is learning how to identify what these models are good at today and using them for those specific tasks. If you spend your time chasing the marginal gains of a benchmark, you will miss the compounding gains of actually integrating these tools into your life.

How to Choose for Your Needs

Once you move past the "best" hype, you can start to determine what actually matters for your project.

• Speed: If you need near-instant responses for a chat interface or a simple automation, look at smaller, specialized models. They are often faster and cheaper while remaining plenty smart for narrow tasks.
• Research: If you need the model to find facts or synthesize current events, use a tool that permits the model to browse the internet or search for sources directly.
• Consistency: If you are building a production system that needs to follow a strict format, the flagship models are generally your best bet.

If you have access to an interface that allows you to adjust the model temperature, you can take control of that consistency yourself. If you need predictable results, drop that temperature down. It reduces the randomness of the output and makes the model easier to test.

Focus on Learning, Not Benchmarks

Right now, learning how to work with these tools is significantly more important than finding a "perfect" one that will be obsolete in three months. Figure out which tools work for you, help you accomplish your goals, and avoid the friction that leads to burnout.

The models will continue to get better. Your job is to be ready to use them when they do.