An embedding model turned decision model. Qwen3-Embedding-4B with a merged LoRA, driven by the open JevEmbed toolkit, which scores Choice, Score and Noul questions from embedding similarity and returns a distribution without generating text.
Standard Thinking's decision model. Ministral 3 8B with a merged LoRA, served through stock SGLang and an open jev-adapter that exposes /v1/systemone and scores the supplied options in one forward pass with per-type temperatures.
Decides
choice, score, noul, classify
choice, score, noul, classify, route
Architecture
jevembed
standard-one
Fine-tuned from
qwen/qwen3-embedding-4b
mistralai/ministral-3-8b-instruct-2512-bf16
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights + hosted API
Hosted by
—
Standard Thinking
Input price
—
—
Decision accuracy
85.9%
71.1%
Calibration error
—
—
Valid action rate
—
—
Median latency
—
25.8 ms
p95 latency
—
41.9 ms
Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between jevembed and standard-one?
jevembed is from HIT-TMG (Lychee Team) and standard-one from Standard Thinking. jevembed has open weights you can download and run; standard-one has open weights and a hosted API. Both answer choice, score, noul and classify questions. Only standard-one answers route. standard-one reads up to 8K tokens of state, against 1K tokens for jevembed. jevembed is the smaller model, at 4.0B parameters to 8.0B.
Which is more accurate, jevembed or standard-one?
They report on different suites — jevembed 85.9% on JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging, standard-one 71.1% on typed-decisions suite, 400 cases / 2,000 decisions (maker's served run, v2) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jevembed or standard-one?
jevembed: Free (open weights). standard-one: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run jevembed or standard-one locally?
Yes, both: systemone pull hit-tmg/jevembed and systemone pull standard-thinking/standard-one download the weights.
Evaluation suite
JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging
