kev
tiny Jev-like family of decision models built on top of Qwen3.5 you can train and run on your own
The Lens
By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.
Updated Sep 2026
Kev answers typed questions about a block of text and returns probabilities your code can branch on. Route this ticket to billing or shipping. Is the customer angry. How urgent is this. No prose, one forward pass, one answer per question. Three sizes (0.8B, 4B, 9B) on Qwen3.5 bases, Apache-2.0, weights and training code included. The API copies TypeSafe's System One contract, so their Python SDK points at a local server with a URL change.
Install is uv sync and one serve command. The 4B wants about 9 GB of GPU memory in bf16, the 9B about 19 GB, and both fit a 32 GB Mac. Five questions take tens of milliseconds on an H100, roughly 780 ms for the 4B on an M5, two seconds for the 9B. On Apple Silicon the older Qwen3 builds are still the quicker option.
Solo developers and small teams doing ticket routing, moderation, or intent classification can run the 4B on hardware they already own. Be honest about why, though: hosted Jev costs $42 per billion input tokens, which is cheaper than any GPU you would rent to replace it. Run Kev for data residency, offline operation, or a fine-tune on your own labels. Not to save money.
The catch is what Kev does not know. On one 900-ticket support suite the 4B loses badly to hosted Jev at spotting an angry customer, and on MMLU-Pro it scores 0.490 against Jev's 0.840, because the base model sets that ceiling. The 9B closes the tone gap to roughly a tie and the knowledge gap not at all. Narrow, well-specified decisions are the job. The server also binds to localhost with no authentication.
Free vs Self-Hosted vs Paid
fully freeFree: everything. Apache-2.0. All three checkpoints are on Hugging Face and mirrored as GitHub release tarballs with SHA-256 checksums. Training code, the frozen eval suites, the research log, and a local playground ship in the repo. No account, no API key, no rate limit. A hosted demo of the 4B and 0.8B runs on Hugging Face Spaces if you want to try it before installing anything.
Self-hosted: you pay in compute, not license. Serving the 4B in bf16 needs roughly 9 GB of GPU memory, the 9B roughly 19 GB, and both run on a 32 GB Mac. Training your own adapter is cheaper than expected: 56 minutes on a single H100 for the 4B, 91 minutes for the 9B, and a 9-minute delta fine-tune for the 4B when you start from a released checkpoint. Budget engineering time for what the project leaves out: the server ships with no authentication and binds to localhost, so a proxy, keys, and deployment are on you.
Paid: hosted Jev, and it is cheap. Kev has no commercial tier. The paid comparison is TypeSafe's hosted Jev, the model Kev is built to imitate, and TypeSafe puts a headline rate on its homepage: $42 per billion input tokens, marketed as 238x lower than Claude Fable 5.1. There is no pricing page and no tier table, so anything beyond that number means contacting sales. Do the arithmetic before you self-host. At that rate 100 million input tokens a month runs about $4, which no rented GPU is going to beat. Self-hosting Kev buys you data residency, offline operation, and a model you can fine-tune. It does not buy you a lower bill.
Free and Apache-2.0 with the weights included. But hosted Jev publishes $42 per billion input tokens, so self-host for data residency or a custom fine-tune, not to save money.
What to do by team size
- Solo
- free; the 4B runs on hardware you already own
- Small team
- free, but hosted Jev costs less than a GPU; self-host for privacy
- Medium team
- free; worth it for a fine-tune on your own labels
- Large team
- free, but benchmark against hosted Jev before committing
Get tools like this every Wednesday
One featured tool, three on the radar. No fluff.
A low score is not a verdict on quality. Young and niche tools start low by design. How we calculate scores
Trust Signals
License: Apache License 2.0
Use freely. Patent grant included.
Commercial use: ✓ Yes
About
- Owner
- Jared Palmer (User)
- Stars
- 2,763