The Metrics of
Functional Truth.

We provide rigorous, highly-customized evaluation suites. Moving beyond public leaderboard vanity metrics to deterministic, functional correctness for specialized models.

REASONING CORE

Custom MMLU-Pro

Our specialized evaluation sets focus on 10-choice questions and complex chain-of-thought requirements, drastically reducing the "guessing" factor found in standard public benchmarks.

5k+
CUSTOM EVALUATION SETS
SCIENCE

GPQA Verification

Graduate-level science evaluations curated and verified by our in-house network of PhD experts.

100%
SME VERIFIED
CODING

SWE-bench

2.5k

Verified Fix Traces

INTEGRITY

Clean-Index

0%

Contamination Rate

The Acadify
Delta.

We eliminate "Model Contamination" by utilizing completely private, non-public data variants, ensuring a model's score reflects genuine reasoning, not pre-training memorization.

Impact of Specialized Tuning

Average performance delta when comparing base open-source models (7B-13B parameter class) to models fine-tuned using our proprietary reasoning datasets.

Base Model Average (13B) ~48%
Acadify SFT Tuned (13B) ~64%
0% FALSE POSITIVES
100% DETERMINISTIC

Benchmark FAQ

Deep diving into our bespoke evaluation protocols and data integrity measures.

We use strictly private, non-public variants of standard benchmarks to eliminate any chance of memorization. We also enforce highly rigorous, deterministic grading criteria (e.g., verifying code via an interpreter rather than regex matching).

Our targeted SWE-bench evaluates models on highly-curated, real-world GitHub issues. The model is required to navigate the repository structure, diagnose the bug, and generate a fix that successfully passes our isolated, containerized unit tests.