laya vs kronos
Two of the top projects in Open models, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
laya
Single-pass multilingual classifier for typed choice, score and yes/no decisions
kronos
Pretrained decoder-only models that forecast financial candlestick (K-line) series
Verdict
laya ranks higher, 67 to 47. Of the 4 score parts we measure for both, laya leads on 3, kronos on 1.
- Adoptionkronos leads by 58 points, 81 to 23: a wide gap.
- Freshnesslaya leads by 35 points, 100 to 65: a wide gap.
- Maintenancelaya leads by 81 points, 91 to 10: a wide gap.
- Agent-readylaya leads by 30 points, 30 to 0: a wide gap.
Pick laya if you are
Engineers needing fast multilingual triage, routing and moderation classifiers
- One forward pass: 33 ms per question, 7.2 ms/question batched, measured on a T4
- No generation, so no output parsing; answers are typed choice, score or probability
Pick kronos if you are
Quant researchers and ML engineers forecasting candlestick time series
- Open weights at 4.1M, 24.7M and 102.3M params on Hugging Face
- Few-line predict and predict_batch API returning pandas DataFrames
| What we compare | laya | kronos |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 23, niche | 81, widely used |
| Freshness | 100, active | 65, recent |
| Maintenance | 91, healthy | 10, weak |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 32.9k | 40.8k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Apr 2026 |
| Last release | Oct 2026 | None published |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
laya
Laya answers typed questions (choice, score, noul) about a text, email, ticket or JSON state in one forward pass of an encoder model, with no text generation. A Router picks between three checkpoints (ModernBERT-large English, mmBERT-base multilingual for 100+ languages, and a typed-decisions fine-tune). It ships as a Python package with a CLI, optional HTTP and MCP servers, and LangChain, LlamaIndex and CrewAI integrations.
Who it is for: Engineers needing fast multilingual triage, routing and moderation classifiers
Strengths
- One forward pass: 33 ms per question, 7.2 ms/question batched, measured on a T4
- No generation, so no output parsing; answers are typed choice, score or probability
- Router selects English or multilingual checkpoint per request; 100+ languages covered
- Fine-tuning notebooks provided; typed-decisions checkpoint scores 0.766 vs 0.362 for base English
Weaknesses
- Zero-shot accuracy is low on domain tasks; fine-tuning is needed for good results
- Multilingual checkpoint ships with a 1,024-token limit; beyond about 4,000 tokens accuracy varies
- Only classifies fixed question types; cannot generate or explain text
- Checkpoints download from Hugging Face on first use, so offline setups need preparation
- GPU optional
- Docker + Compose
- Needs Hugging Face Hub (checkpoint download), PyTorch, transformers
- Models: ModernBERT-large (421M), mmBERT-base (322M), ONNX Runtime
kronos
Kronos is a family of decoder-only Transformers pretrained on OHLCV candlestick data from over 45 exchanges. A tokenizer quantizes K-lines into hierarchical discrete tokens, and the model forecasts future bars through a KronosPredictor class with temperature and top-p sampling. Weights for mini, small and base are on Hugging Face, and the repo includes a Qlib-based fine-tuning and backtest pipeline.
Who it is for: Quant researchers and ML engineers forecasting candlestick time series
Strengths
- Open weights at 4.1M, 24.7M and 102.3M params on Hugging Face
- Few-line predict and predict_batch API returning pandas DataFrames
- Includes tokenizer and predictor fine-tuning scripts with a Qlib backtest example
- MIT license; paper accepted at AAAI 2026
Weaknesses
- Kronos-large (499.2M) weights are not released
- Context limited to 512 tokens, except 2048 for Kronos-mini
- Batch prediction requires identical lookback and pred_len across all series
- Fine-tuning example is a demo, not a production trading system; no releases tagged
- GPU optional
- Needs PyTorch, Hugging Face Hub, pyqlib (fine-tuning only), Comet.ml (optional)
- Models: Kronos-mini, Kronos-small, Kronos-base, Hugging Face
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