Evaluate AI Models for Your Needs

January 24, 2026

The AI landscape in 2025 is overwhelming. There are many models, each with their own benchmark scores. GPT-5.2, Claude Opus 4.5, Gemini 3, DeepSeek, Llama, Qwen... the list goes on. It's easy to fall into the trap of just picking whatever model is performing best at XXX benchmark currently.

However, benchmarks only tell part of the story.

The Benchmark Problem

Benchmarks continue to saturate, but they fail to completely capture the real-world efficacy of models. Benchmark-maxxing models will not retain users long-term. This is a critical insight that many people overlook.

A model might crush MMLU or score impressively on HumanEval. Most, generative AI models excel at tasks like IMO problems but still struggle with complex games such as Chess. They often fail to reliably solve logic tasks even when provably correct solutions exist, limiting their effectiveness in high-stakes settings where precision is critical.

What does this mean for you? A model that looks great on paper may fall flat when it comes to your use case.

Different Models Excel at Different Things

Different AI use cases require different model sizes and capabilities, which is why open models are so useful. Claude Opus 4.1 was the best performing model in the set, excelling in particular on coding ability, and GPT-5 excelled more on specialized knowledge. This means you need to actually test models against your specific use case.

Coding

Let's look at coding as an example. Coding has become the breakout use case in departmental AI. Investment is concentrated where the impact is most immediate: product and engineering teams now account for the vast majority of spend. Anthropic has now had an almost unparalleled 18 months atop the LLM leaderboards for coding, starting with the release of Claude Sonnet 3.5 in June 2024. When Google released Gemini 3 Pro in mid-November 2025, its own model card showed it leading most major evaluations except SWE-bench Verified, where it still trailed Claude Sonnet 4.5. Just a week later, Anthropic widened the gap again with Claude Opus 4.5. If coding is your primary use case, this matters. But if you're doing something completely different, like drafting legal documents or analyzing medical images, those coding benchmarks are meaningless.

AI in Specilialized Domains

When you're using highly specific domain knowledge, LLMs are trained on widely available data and suited to deal with everyday information. But they may not be as accurate for highly technical or niche tasks, like finding out what animal is pooping in your backyard. This is the main idea. Sometimes the right answer isn't the newest model. It's a specialized system built for your domain.

However, privacy should remain at the forefront of your concerns. You must exercise caution when feeding proprietary, sensitive, or confidential information into LLMs. There is the potential for data leaks. While it is possible to build your own private models, that requires skills that may not be available to you. In these situations, you might want to try evaluating existing models instead.

Here's my practical advice for evaluating models:

1. Start With Your Use Case What are you actually trying to accomplish? Be specific. "I want to use AI for my business" is not specific enough. "I need to review my code changes based on the file diffs" is much better.
2. Create Your Own Test Set Build a collection of real examples (human-made golden data) from your domain. Feed them to multiple models. Compare the outputs. This is infinitely more valuable than any benchmark score.
3. Consider the Full Picture Consider your specific use case, budget, and technical requirements. Evaluate each model's strengths in areas crucial to your project, such as language processing, data analysis, or specialized domain knowledge. Cost matters. Speed matters. A "worse" model that's faster and cheaper might actually be better for your production environment.
4. Test Edge Cases Where do models break? Push them to the limits of your use case. The difference between models often shows up at the margins.

The Bottom Line

Evaluation remains important as benchmarks are imperfect. Good judgment about when and how to use these systems is still essential. The model you choose matters but not as much as how well it fits your needs.

Run real tests. Gather real data. Make informed decisions.

Remember, the "best" model is the one that actually solves your problem.