About tensor.news

tensor.news is the evidence layer for AI model claims and benchmarks. We answer one question the leaderboards skip: which scores are trustworthy, comparable, and worth acting on.

Why we exist

A benchmark score is a function of the model, the harness, and the protocol, not of the model alone. Run the same model through a different scaffold and the number moves. Most coverage reports a single figure without saying whether the benchmark still separates models, whether the score was independently reproduced, or whether the test set could have leaked into training. We make those distinctions the product.

What we do

• Benchmark Integrity Index. We grade every benchmark from 0 to 100 on discrimination, saturation, contamination risk, harness comparability, and age, so you know which evaluations still mean something.
• Model Claim Ledger. Every model-by-benchmark score is tagged by evidence status: independently reproduced, self-reported, contradicted, or unverified. We keep independent evaluations and vendor self-reports separate rather than averaging them.

Standards

• Provenance. Every fact traces to a source record: which evaluator ran the score, when it was measured, and where it was published.
• Honesty about what a number is. We frame every score as task performance under a disclosed harness, never as deployed, real-world, or agentic capability.
• Independence. Where a benchmark's integrity is low or its harness is mixed, we say so next to the score rather than leaving it to be read as a verdict.

Sources

Frontier benchmark scores are drawn from independent evaluations, currently anchored on Epoch AI's capabilities data, alongside vendor technical reports (labeled as self-reported). Read the full grading method on the methodology page.

Contact

Have feedback, a correction, or a tip? Reach us at hello@tensor.news.