Jev, Drex, and Laya: Three System One Decision Models Compared
A new kind of model showed up in 2026: the System One decision model. Instead of writing an
answer token by token, it reads a piece of text (the state) plus a set of typed questions, and
returns a probability for each option in a single pass. There is nothing to parse and no free-form
output to go wrong.
Three of them are worth knowing about: Jev from TypeSafe, Drex from Nace, and Laya
from ConvAI. This post compares them as of October 2026.
One protocol, three models
All three speak the same API: POST /v1/systemone with a state and a map of questions. It is
TypeSafe's API, and Jev was the first model on it; Drex and Laya both ship as wire-compatible
replacements, so switching between them is mostly a base-URL change.
They also share the three question types:
| Type | Asks | Returns |
|---|---|---|
| choice | Pick one of these named options | The pick, a confidence, and a distribution |
| noul | Is this proposition true? | P(true) |
| score | Where does this sit on an ordered scale? | Expected level and a distribution |
Typical uses: routing a ticket, classifying an email, checking an extracted field, gating an
action behind a guardrail. Code keeps the workflow, permissions, and arithmetic; the model supplies
the judgment.
Side by side
| | Jev 1.13 (TypeSafe) | Drex 2.0 (Nace) | Laya (ConvAI) |
|---|---|---|---|
| Weights | Closed | Open, under Open RAIL-M (use restrictions; not OSI open source) | Open, Apache 2.0 |
| Hosted API | Yes (api.typesafe.ai; also via OpenRouter) | Yes (console.nace.ai) | No first-party API; self-host (laya-serve). Third-party: Vercel AI Gateway (free through October 31, 2026, billed after). Plus a demo Space |
| Model | Not disclosed | Qwen3.6-35B-A3B MoE (35B total, ~3B active) | Encoders: ModernBERT-large 421M, mmBERT-base 322M |
| Hardware to self-host | n/a | Large GPU: ~70 GB bf16, ~37 GB Q8 GGUF | Small: a T4, or CPU (193–464 ms) |
| Context | 64K per request / 32K per question; 262K with the long-context header | 128K state (hosted); self-host defaults to 16K, up to 128K | 512 (English) / 1,024; up to 8K multilingual |
| Latency | 236–316 ms p50 (independently measured) | ~25 req/s per H100 (vendor) | 33–40 ms on a T4 (vendor) |
| Zero-shot quality | The reference point | Vendor reports ahead of Jev on its Decision Index (61.25 vs 57.96), slightly behind on JevBench (85.7% vs 86.6%) | Weak: 0.362 on typed-decisions, below a 0.461 majority-class baseline |
| After fine-tuning | Not offered; you steer it through state and instructions | Offered as a vendor service | 0.766 vs a third-party-published Jev 0.727 on the same benchmark |
| Many options | Up to 255; 79.2% on the full Banking77 test (77 labels) | Up to 255 | Drops past ~20 options at default settings: 0.425 on Banking77 |
| Calibration | No vendor figure; independent ECE 0.02–0.35 depending on the task | No figures published | Over-confident until temperature-fitted (0.466 → 0.081) |
| Languages | English primary; others, including CJK, handled less accurately | No list published | 100+ (45 of 51 measured languages usable), with a built-in language router |
| Price | $0.042 per 1M input tokens, output free | Hosted: $0.06 per 1M input tokens (v2.0), output free, $25 signup credit. Self-host: royalty-free for personal or research use, or organizations under $1M revenue and funding; others need a commercial license | Free (self-host) |
| Rate limits | Set per organization in requests/s and tokens/s; the numbers are in your console, not the public docs. My test: ~850 requests/min sustained (806 calls in 57 s, 4 in flight), no 429s | Free tier: 120 requests/min, 8 in flight. Paid: 600 requests/min, 16 in flight. My test: 429s on the free tier even when paced below 110 requests/min; none after adding credit (~800 calls in about 2 minutes) | None when self-hosted; your own hardware is the limit. AI Gateway limits not published |
Jev 1.14 shipped as a preview (jev-preview) on October 14; jev-latest still points to 1.13.
Which one when
- Jev if you want it to work zero-shot today with the most mature SDKs and docs, and a
hosted-only service is fine.
- Drex if you want both doors open: a hosted API priced close to Jev today, and open weights you
can move in-house later if data has to stay in your own environment (check the license if your
organization is above its $1M thresholds). It also has the largest default context (128K).
Self-hosting it takes a large GPU.
- Laya if the task is narrow and high-volume, latency matters, and you have labeled examples to
fine-tune on. It is the only one that runs comfortably on CPU or at the edge, and the only one
that publishes multilingual results.
In one line: Jev is the hosted default, Drex offers the same idea both hosted and as open weights,
and Laya is a very small model you train and run yourself.
Read the benchmarks carefully
Almost none of these numbers are directly comparable:
- Drex's figures are self-reported by Nace.
- Laya's comparisons against Jev use Jev numbers published by third parties; the model card
says they were never measured by the Laya team. The widely quoted "beats Jev" result is for a
checkpoint fine-tuned on that benchmark's own training split; the base checkpoint scores below
majority-class guessing on it.
- Jev's latency, Banking77 and calibration figures come from two independent benchmark repos;
TypeSafe itself publishes no accuracy or calibration numbers.
Because the three share a protocol, the honest way to choose is cheap: take 50–100 real decisions
from your own workload, run all three, and compare. Each model also has documented weak spots worth
testing for: injected text shifting Jev's probabilities, Laya's score questions and its noul
following option labels instead of the state.
Sources
- Jev models, context and pricing and
API reference
- Drex, its pricing,
limits and rate limits and
repo
- Laya model card and
laya-typed-decisions, and
Laya on Vercel AI Gateway
- jev-benchmarks and
decision-model-benchmark
Mentions
@jaydrogers I’ve built this so my devops agent can provision me on my pve machines whatever I need. I’ve been using it for a year or so, but it is still very messy one person project nibzard/agentlab
Jev, Drex, and Laya: Three System One Decision Models Compared A new kind of model showed up in 2026: the System One decision model. Instead of writing an answer token by token, it reads a piece of text (the state) plus a set of typed questions, and returns a probability for each option in a single pass. There is nothing to parse and no free-form output to go wrong. Three of them are worth knowing about: Jev from TypeSafe, Drex from Nace, and Laya from ConvAI. This post compares them as of October 2026. One protocol, three models All three speak the same API: POST /v1/systemone with a state and a map of questions. It is TypeSafe's API, and Jev was the first model on it; Drex and Laya both ship as wire-compatible replacements, so switching between them is mostly a base-URL change. They also share the three question types: | Type | Asks | Returns | |---|---|---| | choice | Pick one of these named options | The pick, a confidence, and a distribution | | noul | Is this proposition true? | P(true) | | score | Where does this sit on an ordered scale? | Expected level and a distribution | Typical uses: routing a ticket, classifying an email, checking an extracted field, gating an action behind a guardrail. Code keeps the workflow, permissions, and arithmetic; the model supplies the judgment. Side by side | | Jev 1.13 (TypeSafe) | Drex 2.0 (Nace) | Laya (ConvAI) | |---|---|---|---| | Weights | Closed | Open, under Open RAIL-M (use restrictions; not OSI open source) | Open, Apache 2.0 | | Hosted API | Yes (api.typesafe.ai; also via OpenRouter) | Yes (console.nace.ai) | No first-party API; self-host (laya-serve). Third-party: Vercel AI Gateway (free through October 31, 2026, billed after). Plus a demo Space | | Model | Not disclosed | Qwen3.6-35B-A3B MoE (35B total, ~3B active) | Encoders: ModernBERT-large 421M, mmBERT-base 322M | | Hardware to self-host | n/a | Large GPU: ~70 GB bf16, ~37 GB Q8 GGUF | Small: a T4, or CPU (193–464 ms) | | Context | 64K per request / 32K per question; 262K with the long-context header | 128K state (hosted); self-host defaults to 16K, up to 128K | 512 (English) / 1,024; up to 8K multilingual | | Latency | 236–316 ms p50 (independently measured) | ~25 req/s per H100 (vendor) | 33–40 ms on a T4 (vendor) | | Zero-shot quality | The reference point | Vendor reports ahead of Jev on its Decision Index (61.25 vs 57.96), slightly behind on JevBench (85.7% vs 86.6%) | Weak: 0.362 on typed-decisions, below a 0.461 majority-class baseline | | After fine-tuning | Not offered; you steer it through state and instructions | Offered as a vendor service | 0.766 vs a third-party-published Jev 0.727 on the same benchmark | | Many options | Up to 255; 79.2% on the full Banking77 test (77 labels) | Up to 255 | Drops past ~20 options at default settings: 0.425 on Banking77 | | Calibration | No vendor figure; independent ECE 0.02–0.35 depending on the task | No figures published | Over-confident until temperature-fitted (0.466 → 0.081) | | Languages | English primary; others, including CJK, handled less accurately | No list published | 100+ (45 of 51 measured languages usable), with a built-in language router | | Price | $0.042 per 1M input tokens, output free | Hosted: $0.06 per 1M input tokens (v2.0), output free, $25 signup credit. Self-host: royalty-free for personal or research use, or organizations under $1M revenue and funding; others need a commercial license | Free (self-host) | | Rate limits | Set per organization in requests/s and tokens/s; the numbers are in your console, not the public docs. My test: ~850 requests/min sustained (806 calls in 57 s, 4 in flight), no 429s | Free tier: 120 requests/min, 8 in flight. Paid: 600 requests/min, 16 in flight. My test: 429s on the free tier even when paced below 110 requests/min; none after adding credit (~800 calls in about 2 minutes) | None when self-hosted; your own hardware is the limit. AI Gateway limits not published | Jev 1.14 shipped as a preview (jev-preview) on October 14; jev-latest still points to 1.13. Which one when - Jev if you want it to work zero-shot today with the most mature SDKs and docs, and a hosted-only service is fine. - Drex if you want both doors open: a hosted API priced close to Jev today, and open weights you can move in-house later if data has to stay in your own environment (check the license if your organization is above its $1M thresholds). It also has the largest default context (128K). Self-hosting it takes a large GPU. - Laya if the task is narrow and high-volume, latency matters, and you have labeled examples to fine-tune on. It is the only one that runs comfortably on CPU or at the edge, and the only one that publishes multilingual results. In one line: Jev is the hosted default, Drex offers the same idea both hosted and as open weights, and Laya is a very small model you train and run yourself. Read the benchmarks carefully Almost none of these numbers are directly comparable: - Drex's figures are self-reported by Nace. - Laya's comparisons against Jev use Jev numbers published by third parties; the model card says they were never measured by the Laya team. The widely quoted "beats Jev" result is for a checkpoint fine-tuned on that benchmark's own training split; the base checkpoint scores below majority-class guessing on it. - Jev's latency, Banking77 and calibration figures come from two independent benchmark repos; TypeSafe itself publishes no accuracy or calibration numbers. Because the three share a protocol, the honest way to choose is cheap: take 50–100 real decisions from your own workload, run all three, and compare. Each model also has documented weak spots worth testing for: injected text shifting Jev's probabilities, Laya's score questions and its noul following option labels instead of the state. Sources - Jev models, context and pricing and API reference - Drex, its pricing, limits and rate limits and repo - Laya model card and laya-typed-decisions, and Laya on Vercel AI Gateway - jev-benchmarks and decision-model-benchmark
@ManuAF6 I still read them manually, but I have a plan to integrate checks and automate it with nibzard/measuretwice
everything is just a tad nicer, who needs factories when you can have atelier nibzard/looper t.co
here is the pr agent did on its own agentlab#1
am I now behind if I still drop ideas into a loop inside vm and get back to it after few days? nibzard/looper t.co