2025 LLM Testing Guide: Cluster F*ck, Unclustered.

Here's the TL;DR: Use whatever AI tool works for you. The rest of this article is for the curious minds who want to understand what all these benchmark scores actually mean and how you can get that extra 1%er over your competition.

It seems every day brings new headlines about AI models breaking records on tests you've never heard of. MMLU, MT-Bench, TruthfulQA - it's becoming a confusing cluster f*ck. Let's cut through the noise and understand what actually matters for your business.

Understanding the Benchmarks 📊

A quick note on tokens: Think of them as pieces of words. 'Marketing' is one word but about 2 tokens, while 'internationalisation' is about 6 tokens. An average tweet (sorry, X) is around 55 tokens, a typical email 200-300 tokens. When you see '$0.002 per token,' multiply that by your expected content volume to understand real costs (or use a token estimator just a quick google away).

Beyond the Numbers: Marketing Reality Check 👀

Different Business Types, Different Needs 🪡

Each business type needs to evaluate AI tools through their own lens:

Enterprise Reality 🏢

• Performance requirements trump cost concerns
• Security and compliance non-negotiable
• Integration capabilities critical
• Environmental impact considered but not primary

Small Business Approach 🏪

• Cost-efficiency essential
• Quick deployment needed
• Flexible usage patterns
• ROI focus primary

Green Brand Priorities (if this is important for your brand promises) 🍃

• Environmental impact key consideration
• Balance of performance/efficiency
• Brand alignment crucial
• Long-term sustainability focus

Independent Testing Sources (If You Really Want to Nerd Out 🤓)

If you're still curious about unbiased benchmark comparisons, there are a few relatively neutral sources:

• Hugging Face's Open LLM Leaderboard: Community-driven testing
  • Side note, Hugging Face is great for many other things. A much needed resource.
• Chatbot Arena: Blind testing format
  • This is cool. I've been surprised a few times at which model gave what i actually wanted from a prompt.
• Stanford CRFM's HELM: Academic evaluation framework

At the current pace of the industry, these could also be outdated by the time you read this article. Your best test will always be how well a model performs on your actual marketing tasks.

A Note on How These Tests Work 📝

AI is grading other AI on these tests. Yes, really. While there is human involvement in creating and validating these benchmarks, a lot of the scoring is automated - it's robots judging robots.

In the AI arms race, these companies will always find data to support their claims. It's like a group of running shoe companies each creating their own race tracks, then claiming they've made the fastest shoe - but each one measured "fastest" differently and on their own turf.

This is partly why you shouldn't get too caught up in the benchmark race. By the time you've finished researching which model scored highest on which test, there'll be new models, new tests, and new scores to consider.

Remember our TL;DR from the start? "Use whatever AI tool works for you." Still holds true, however for the smart marketers, understanding this noise and knowing which benchmark to actually care about depending on the stage of your business and bank account can yield returns.

Ready to Make AI Work for Your Business? 🫡

At BRAIVE, we help organisations cut through the noise and find AI solutions that deliver real results. Let's focus on what actually matters for your business.