An open System One model from Interfaze AI. A rank-32 LoRA on Qwen3.5-4B reads yes/no, choice and score answers from the logits of one forward pass, returns calibrated probabilities and serves the /v1/systemone API.
Together AI's Jev-style classifier. A LoRA fine-tune of Qwen3.5-4B that reads a state, a question and 2 to 24 options and returns one option letter. Served on Together's platform; recipe published.
Decides
choice, score, noul, classify, route
choice, classify, route
Architecture
lev
tev
Fine-tuned from
qwen/qwen3.5-4b
qwen/qwen3.5-4b
License
apache-2.0
Unspecified — weights licence being finalised
Availability
Open weights
Open weights + hosted API
Hosted by
—
Together AI
Input price
—
$0.042/MTok
Decision accuracy
68.9%
88.0%
Calibration error
0.115
—
Valid action rate
—
—
Median latency
69 ms
—
p95 latency
—
—
Evaluation suite
S1Bench (13 public subsets, macro)
Together development set (reused, not held out)
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 lev and tev1?
lev is from Interfaze AI and tev1 from Together AI. lev has open weights you can download and run; tev1 has open weights and a hosted API. Both answer choice, classify and route questions. Only lev answers score and noul. lev is licensed apache-2.0; tev1, other.
Which is more accurate, lev or tev1?
They report on different suites — lev 68.9% on S1Bench (13 public subsets, macro), tev1 88.0% on Together development set (reused, not held out) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, lev or tev1?
lev: Free (open weights). tev1: $0.042 / $0 per 1M, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run lev or tev1 locally?
Yes, both: systemone pull interfaze-ai/lev and systemone pull together-ai/tev1 download the weights.
