The AI industry has a measurement problem. As a recent critique of LMArena put it: “There’s a fundamental misalignment between what we’re measuring and what we want: models that are truthful, reliable, and safe.”
We agree. And today, we’re doing something about it.
Your Tasks. Your Data. Real Answers.
Neurometric runs the only “system 2” leaderboard. We don’t evaluate one shot model prompts but rather, multi-stage or multi-turn agentic workloads. If you are familiar with “system 1” and “system 2” thinking, most leaderboards look at system 1. We look at system 2. Today we are announcing the beta version of our data upload toolkit for our Leaderboard. You can now upload your own prompts, your own evaluation criteria, and your own AI workloads—and test them against every combination of model and test-time compute configuration we support. You can see how your workloads perform against various “system 2” configurations.
No more guessing whether GPT-4 or Claude or Llama will perform better on your specific use case. No more trusting aggregate benchmarks that hide massive variance across task types. No more optimizing for a leaderboard that rewards emoji usage over accuracy.
You get answers that matter for your business. On your tasks. For your specific needs.
The Dirty Secret of Benchmarks
Here’s what we’ve learned from months of rigorous testing: when a model “wins” a benchmark, it doesn’t win at every task within that benchmark. Performance is jagged. A model that excels at reasoning may stumble on retrieval. A model optimized for code may falter on nuanced customer support responses.
Aggregate scores hide this variance. They tell you which model is best on average, across tasks you probably don’t care about, evaluated by people who aren’t checking for correctness.
Our leaderboard shows you which model—and which test-time compute configuration—wins on the tasks you actually run in production.
Ensembles Beat Single Models
Our research demonstrates something the industry hasn’t fully absorbed yet: for thinking and reasoning tasks, an intelligent combinations of models with different test-time compute configurations consistently outperform any single model on overall benchmark performance.
The implications are significant. If you’re routing 100% of your inference traffic to a single frontier model, you’re likely overpaying for some tasks and underperforming on others. The right architecture isn’t picking a winner—it’s building a system that routes each task to its optimal configuration.
That’s what Neurometric’s orchestration layer does. And now you can see the evidence on your own data.
What This Means for You
Upload a sample of your production prompts. Define what “good” looks like for your use case. Our leaderboard will show you exactly which model and test-time configuration delivers the best results—and by how much.
No gamified labor from uncontrolled volunteers. No rewards for verbosity and aggressive formatting. Just rigorous, task-specific evaluation on the workloads that matter to your business.
The AI industry spent years optimizing for what feels right. It’s time to optimize for what is right.
Your data. Your tasks. Real performance.
Click here to request access.
This finally shifts focus from abstract benchmarks to real-world performance. A game-changer for building cost-effective, task-optimal AI systems.