The biggest model isn't always the best one for your use case. We run structured evaluations to find the model that actually performs on your specific task — then tell you honestly what we found.
Published benchmarks measure general capability. Your use case is specific. The model that tops the leaderboard for coding might underperform a 7B specialist model on your classification task, at a tenth of the cost and latency. You won't know without testing.
Smaller, task-specific models regularly outperform 30B+ generalist models on focused tasks. We've seen it repeatedly in our own benchmarking work.
The cost difference between local inference with a capable smaller model and cloud API calls to a frontier model can be significant — with comparable results on the right task.
AI vendors publish the benchmarks that make their models look best. Nobody publishes the tasks where they underperform cheaper alternatives. Independent evaluation fills that gap.
When existing benchmarking tools didn't serve our needs, we built Squirmify — a structured framework for task-specific model evaluation.
Standard AI benchmarks are designed to compare models in generalised ways. They're useful for initial selection but poor at predicting performance on specific production tasks. When we were evaluating models for Guardian (our crisis detection system), we needed to know which model performed best on our exact task — not which one scored highest on MMLU.
Squirmify runs structured evaluations across a defined task set, measures performance across multiple dimensions (accuracy, consistency, latency, failure modes), and produces comparative results that actually predict production behaviour. We discovered during this work that an 8B model consistently outperformed 30B+ models on specific classification tasks — a finding that would have been invisible in any standard benchmark.
The framework is now part of how we approach every model selection decision, for ourselves and for clients.
We design evaluation suites around your actual use case — your data, your output requirements, your edge cases. The test set reflects production reality, not academic benchmarks.
We run candidates across your evaluation suite and compare results across accuracy, consistency, latency, and failure modes. Local models, cloud APIs, specialist fine-tunes — we test what's relevant to your situation.
Performance results alongside real cost modelling. At your expected inference volume, what does each option actually cost? Where does the cost-performance curve break in your favour?
We have no vendor relationships and no incentive to recommend a particular model. We tell you what the evaluation found, including when the answer is "the smaller, cheaper model is fine for this."
Tell us what you're trying to do. We'll scope an evaluation that gives you a real answer before you commit to an architecture.
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