Jev and open alternatives¶

What Jev is¶

Jev (TypeSafe, typesafe/jev, served through OpenRouter) turns a model into a typed decision engine: send a text (the state) and questions of type noul (yes/no), choice or score, get typed answers with probabilities, with no text generation. SelfJev copies the interface, not the model: selfjev serve answers Jev's requests at Jev's paths (/v1/systemone, /api/alpha/decisions, /v1/decisions), so Jev clients work by changing the base URL and the key (API, how it works).

What TypeSafe discloses (public web, read 2026-09-23):

• "Transformer-based, but it is not a large language model", with a "new model architecture, parallel sampler": all outputs come from one pass.
• Questions run "in parallel and in isolation against the same state". That is the shape of our shared-prefix tree.
• Limits: 64K tokens per request; 32K for the state plus the longest question. Rate limit 250K tokens/s.
• Training: "Reinforcement Learning for Calibrated Decisions (RLCD)", no details. Their benchmark's reference answer is the average of GPT-6 Astra and Fable 5.1.
• Not disclosed: size, base model, data. They "cannot prove the price is unsubsidized".

Sources: typesafe.ai/blog/introducing-system-one-models-and-jev, docs.typesafe.ai/models.md, marktechpost.com/2026/09/19/typesafe-ai-releases-jev/.

What we measured about it:

Our reading (2026-09-25 update: what Jev's behaviour implies and what to build next): the gap is mostly training, not architecture. 4B → 8B, a bigger adapter and stock → tree each moved the dev benchmark by ≤ 1.3 points, while 10K verified target-task questions moved eval2 by +5.5. Our default model, selfjev-4b, is now 1.4 points behind on eval2 (95.8), level on eval_llm (93.1, p = 0.52) and ahead on the dev benchmark (83.8). Jev's speed is mostly the hardware: on one H100 our Qwen3 4B tree is faster than Jev inside the machine in every cell but 4,096 tokens × 16 questions (2026-09-26, speed); Jev's per-token cost is still ≈ 6× lower than ours, so a smaller model or more GPUs per request, and the end-to-end difference is network distance. selfjev-4b's own serving engine is not timed on a GPU yet.

Open "Jev-like" models (survey of Hugging Face model cards, 2026-09-24)¶

Nothing was downloaded or run for the survey (Eikos-4B was scored later, below); claims are each model's own, on its own benchmark, and not comparable with ours.

• Their claims: AutoJev-27B 84.6 vs Jev 82.8; kev-4b beats Jev in distribution but trails out of domain (0.817 vs 0.857); kev-0.8b trails everywhere; decider-35b-a3b JevBench hard 0.676, decider-4b 0.541.
• The same pattern as ours: sub-1B trails, 4B and up is where the quality is.
• "JevBench" public items exist and several cards report on them: the only shared yardstick. Scoring our tree on it is an open idea.

Laya (reviewed at commit 970dc8c)¶

• A bidirectional encoder (ModernBERT-large 421M, or mmBERT-base 322M multilingual), one sequence per question with every option and the state; a new 2-layer transformer head scores each option's [MASK]. Trained with a GRPO-style policy gradient on proper-scoring-rule rewards, which it calls RLCD.
• Documented limits: the state is truncated to ≈ 320 tokens by default (≈ 768 multilingual, up to 8K opt-in), options share a 192–256-token budget, base checkpoints near chance zero-shot on its own set, over-confident before temperature fitting (ECE 0.466 → 0.081).
• Its Jev latency figure (236–276 ms p50) is slower than we measured (143–178 ms).
• Verdict: the faster and multilingual option (≈ 33 ms on a T4), not the more accurate one on long, trap-heavy texts. Our closest measured analogue, jina 0.6B listwise, scores 73.3 on eval2. Scoring Laya on eval2 is open (no API cost; a small GPU box, never the laptop).

Eikos-4B (caiovicentino1/Eikos-4B, MIT)¶

• A full fine-tune of Qwen3.5-4B distilled from GLM-5.3-Flash on finance and rules data; options as letters, softmax over the letter logits.
• vLLM ≥ 0.30 with --enable-prefix-caching --mamba-cache-mode all shares the state across questions (the card reports that vLLM 0.11 lost 3–6 points on batched long shared documents).
• Own-harness claims: JevBench public hard 72.1 (Jev 73.0 on the official leaderboard), ECE 0.033.
• Evidence that the Qwen3.5 base works for typed decisions. Zero-shot through our option mapping it scores 92.8 on eval2 (reports/eikos_4b/). Qwen3.5-4B has been the base of our best models since 2026-09-25, selfjev-4b included.

Qwen3.5 and Qwen3.8 as bases¶

• Qwen3.8 has no small dense model: 27B (48 linear-attention + 16 full layers), a 180B MoE and a 2.4T MoE.
• Qwen3.5 has 0.8/2/4/9/27B; the 4B has 32 layers (24 Gated DeltaNet + 8 full attention) and 262K positions. A tree mask cannot branch its linear-attention layers in one pass. First a forked native cache did it (challengers); now our Qwen3.5 tree (src/selfjev/engine/tree.py) runs those layers level by level from copied states, in training and serving (tree scorer).