Laya benchmarks
The official Laya README publishes detailed numbers, and a growing set of independent repositories now test those claims. The overall picture: Laya is much faster when run locally and free to self-host, while Jev is more accurate out of the box on most shared tests.
228 projects
Laya-Showcase
zamax14
Demonstrates and benchmarks multilingual Laya alongside Jev and GPT models
decision-systems-bakeoff
actuallyrizzn
Runs a reproducible benchmark comparing Flybrain, Jev, and Laya on four public tests
flappy-laya-openvino-cpu
rupeshs
Runs Laya inference with OpenVINO to control a Flappy Bird game on CPU
laya-multilingual-FP8
Weidows
Provides an FP8 per-row quantization of the multilingual Laya checkpoint with multilingual evaluation results
LayaSnakeGame
AmirTahaMim
A Pygame Snake game compares human play, a greedy baseline, and local Laya decisions
laya-snake
zxrneu
Builds an autonomous Snake game agent using Laya decisions and BFS pathfinding
JevCoreML
GodModeAI2025
Provides Core ML decision models, a Swift package, an HTTP server, and a demo app
laya-as-judge
rbrus
Uses Laya-MLX to evaluate models and agents with typed-decision judgments
laya-vs-jev
zaferayan
Benchmarks multilingual Laya against hosted Jev across 900 cases, three tasks, and six languages
stuntdouble
ReallyArtificial
Proxies Jev requests to local decision models and compares their answers on recorded traffic
laya-cuda
Alexw1111
Provides a lightweight CUDA inference library for Laya
laya-cpu-benchmark
taiheqi718-art
Measures steady-state CPU latency for the multilingual Laya model
laya-ternary-lite
xixi3548942758-design
Quantizes Laya to ternary weights for smaller, lower-memory inference
laya-thalamus
lcbkmm
Middleware uses Laya for agent tool routing with confidence-based LLM fallback and evaluation
laya-typed-decisions
akshatbindal
Fine-tunes Laya on the typed-decisions benchmark and compares results with Jev
laya-ov-int8
rupeshs
An int8 OpenVINO export of Laya for CPU inference
decision-model-playground
tedliou
A local web playground comparing Laya and Jev for recommending articles
laya-tetris-finetuning
hama-jp
Fine-tunes Laya for real-time Tetris placement decisions and documents the experiments
jev-vs-open-decision-models
elcronos
Benchmarks Jev against Laya and PrismNLI on zero-shot text classification tasks
sokudan
hiroki-abe-58
Builds a Japanese typed-decision model and benchmarks it against Laya
laya
xosi
Runs Laya typed-decision inference locally on Apple Silicon with MLX
jev-vs-laya
DDnim
Benchmarks Jev and Laya on typed decisions for reviewing SQL statements
laya-jev-arena
dante01yoon
Compares local Laya and hosted Jev models in games and measurement tools
chinese-workflow-decision-bench
Adkid-Zephyr
Benchmarks Jev and Laya on synthetic Feishu message classification scenarios
Official numbers
The core README and its BENCHMARKS.md report latency on a Tesla T4 (32.8 ms for one question with laya-multilingual, 39.5 ms with laya) and accuracy across 51 languages. Its Jev figures are third-party published, not measured by the Laya authors.
Independent head-to-heads
- sysone-bench: byte-identical inputs across 751 states. Jev leads on triage, guardrails, moderation, Banking77 and multilingual intent. Laya leads on AG News and MNLI.
- jev-laya-benchmark: 1,470 synthetic items. Jev 92.9% vs Laya 65.3% of judgments correct. For a single question, Laya was 42 ms locally on an M3 Pro and Jev was 136 ms. Jev was faster above 3 to 4 questions per request.
- laya-jev-lab: 40 Chinese support tickets. Jev 78% at 588 ms, Laya 57% at 7.6 ms on an M4 Max. A cascade that escalates below 0.60 confidence matched Jev's accuracy at 1.8x its speed.
- JevBench: a composite board. In v1.4.2, Laya (English, CPU) ranks 41st with a score of 30.25 and Jev 1.13.0 ranks 2nd with 63.29.
- laya-mcp eval: key accuracy 32.5% for Laya vs 98.8% for Jev, with median latency of 26 ms vs 138 ms.
CPU latency
laya-cpu-benchmark measured laya-multilingual in steady state on an Intel i9-14900HX. One question took 50 ms for a 71-token state, 307 ms for 261 tokens, and 1,315 ms for 927 tokens. Batching gave little gain on CPU. Its conclusion: a realistic CPU call is 0.3 to 2 seconds, not 33 ms.
Run your own
git clone https://github.com/glukicov/laya_router && cd laya_router
uv sync --all-extras
uv run laya-router eval run --backend laya --device mps
Or use jevbench (dhruvmehra) to compare Jev, Laya, LLMs and BERT models on public datasets.
Reading these numbers
Most of these suites are small (40 to 1,500 items), synthetic or in one domain, and their authors say so. Latency depends on hardware, state length and question count. The core README's own advice: treat Laya as a fast base to specialise, and fine-tune for accuracy.
More ways to use Laya