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.
A Gemma 4 12B fine-tune for typed decisions that also chats and reads images. Ships as GGUF for llama.cpp, holds a 64K context on a 16 GB GPU, and serves /v1/systemone next to /v1/chat/completions.
Decides
choice, classify, route
choice, score, noul, classify, route
Architecture
tev
winnow
Fine-tuned from
qwen/qwen3.5-4b
google/gemma-4-12b-it
License
Unspecified — weights licence being finalised
apache-2.0
Availability
Open weights + hosted API
Open weights
Hosted by
Together AI
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Input price
$0.042/MTok
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Decision accuracy
88.0%
85.7%
Calibration error
—
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Valid action rate
—
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Median latency
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143 ms
p95 latency
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Evaluation suite
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 tev1 and winnow?
tev1 is from Together AI and winnow from EldanRing. tev1 has open weights and a hosted API; winnow has open weights you can download and run. Both answer choice, classify and route questions. Only winnow answers score and noul. tev1 is the smaller model, at 4.0B parameters to 12B. tev1 is licensed other; winnow, apache-2.0.
Which is more accurate, tev1 or winnow?
They report on different suites — tev1 88.0% on Together development set (reused, not held out), winnow 85.7% on JevBench public subset (231 items), Q8_0 — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, tev1 or winnow?
tev1: $0.042 / $0 per 1M, or free to self-host. winnow: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run tev1 or winnow locally?
Yes, both: systemone pull together-ai/tev1 and systemone pull eldanring/winnow download the weights.
