Benchmarks and fairness

0 section. Convai states Jev figures come from third-party published measurements without replicating them in-house. Laya figures come from their Router predict path on identical question sets. Sample sizes, prompts, and label spaces differ per row.

md in the Laya repo before citing a single accuracy delta.

Laya and Laya-MLX benchmarks typically time model forward passes (sometimes plus tokenization and calibration) on a warm process. Hosted Jev latency includes client SDK, DNS, TLS, API gateway, batching, and geographic distance.

A 33 ms T4 forward pass is not comparable to a 250 ms p50 API trace from another author's blog. When evaluating agents, measure wall clock from your orchestrator.

Convai notes Laya typed-decisions can beat Jev on argmax accuracy while Jev leads on soft accuracy against teacher distributions. Expected calibration error improves after Laya temperature fitting. Compare ECE only when both sides use equivalent post-processing on your holdout.

/learn/jev-vs-llm-classification explains why thresholds need probabilities, not just top-1 labels.

Banking77-style benchmarks show Jev ahead when dozens of labels compete for a fixed option-token budget. Laya documents mitigations (raise head_max_len, hierarchical choice).

If your product has 50+ simultaneous options, reproduce the benchmark with your schema before switching vendors.

Blog posts such as aiidelist.com's Laya-MLX explainer and retailer-hosted AI hubs (for example ZimaSpace's Laya overview) summarize public repos. They are not peer review. Use them for install pointers and vocabulary, then verify against GitHub READMEs and HF cards.

jev.aitools.fyi does not run independent head-to-head evals between Laya and Jev. We do not host weights or TypeSafe keys. Any showcase clip is anecdotal social proof, not a benchmark suite.