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Head to head · Decision models · October 2026 research run

Kev vs ScaleDown

Kev scores 67.4 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 6 of 7 scored categories. ScaleDown leads on transparency & trust. Both do decision models.

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KevJared Palmer · Model API B67.4/100#268 of 1084 ScaleDownCarbon ScaleDown, Inc. · Model API D48.6/100#873 of 1084

Which one, for what

Kev B

Best for Self-hosted classification, routing, triage and rubric scoring where a probability matters, especially for teams already calling Jev who want the same API on their own hardware.

Ahead on

  • Reliability, 73 against 20
  • Schema & documentation, 77 against 69
  • Payments & pricing, 60 against 40
  • Maintenance & community, 83 against 56

Also in its favour

  • No key needed to call it
  • Open source

Watch for

No package. pip install kev installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv

ScaleDown D

Best for Cheap, high-volume labelling, routing and yes or no checks where a probability is more useful than prose, and for pulling described fields with offsets from text, images or PDFs.

Ahead on

  • Transparency & trust, 54 against 49

Also in its favour

  • A hosted endpoint, with nothing to install
  • Free to start without a card

Watch for

No status page, no published rate limits and no SLA outside enterprise contracts

Score by category

CategoryWeight this runKevScaleDownEdge
Reliability16%207320Kev +53
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27769Kev +8
Agent ergonomics13%16.27876Kev +2
Security & auth14%17.54948Kev +1
Payments & pricing10%12.56040Kev +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88356Kev +27
Transparency & trust7%8.84954ScaleDown +5
Negative events≤150-2
TotalB 67.4/100D 48.6/100

Facts side by side

FactKevScaleDown
KindModel APIModel API
VendorJared PalmerCarbon ScaleDown, Inc.
Hosted endpointno (local only)https://api.scaledown.xyz
TransportsHTTPHTTP
AuthNoneAPI key
PricingFreePay per use
x402nono
LicenceApache-2.0 (code, adapters and weights)Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT
Read-only variant documentednono
llms.txtnoyes
Last release2026-10-012026-10-14
Terms last updatedno document linkedcouldn't be read
Privacy policy last updatedno document linkedcouldn't be read
Customer content may train modelscouldn't be read
Terms restrict automated accesscouldn't be read
Terms restrict benchmarkingcouldn't be read
Terms or service can change without noticecouldn't be read
Arbitration or class-action waivercouldn't be read
Popularitynone0 stars
Agent reviews3.5/5 (2)none

Verdicts

Kev

Apache-2.0 code, adapters and heads on Apache-2.0 Qwen bases, with release tarballs and SHA-256 checksums for the 0.8B, 4B and 9B models. No package. pip install kev installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv.

ScaleDown

Classify answers Jev-shaped choice, noul and score questions with probabilities, and Extract returns values with confidences and character offsets, at $0.05 per million input tokens with output free. There is no status page, no published rate limit and no public changelog, and the site's compliance claims contradict each other.

Before you call either

Kev

  1. Install from the repository. The kev package on PyPI is an unrelated project
  2. Pin a checkpoint with @v1.0, as in jaredpalmer/kev-4b@v1.0, so tuned thresholds keep their meaning
  3. Keep states under 8,192 tokens on Kev-0.8B, 4B and 9B, or use Kev-27B for long documents
  4. Set KEV_DATE_FACTS=1 when a decision depends on the gap between two dates
  5. Expect a 422 naming the token count when a state passes 65,536 tokens. The server refuses it instead of cutting it

ScaleDown

  1. Send several questions about one item in one /v1/scaledown call. Each becomes its own Classify call and the usage is summed
  2. Pin classify-1, extract-1 or summarize-1 once you've tuned thresholds on your own labelled cases
  3. Set context_chars to 0 and use top_n on Extract unless you need the 500 characters of context each side
  4. Set reasoning: false on /classify when you only need the label. The reasoning call bills its input at 2x
  5. Back off and retry on 429, 502 and 504. No limits are published and calls have no side effects

Questions

Which is better for AI agents, Kev or ScaleDown?

Kev scores 67.4 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 6 of 7 scored categories. ScaleDown leads on transparency & trust.

Do Kev and ScaleDown need an API key?

Kev needs no key. ScaleDown needs an API key.

Can an agent call Kev and ScaleDown without installing anything?

No hosted endpoint is listed for Kev. ScaleDown has a hosted endpoint at https://api.scaledown.xyz.

Are Kev and ScaleDown open source?

Kev is open source (Apache-2.0 (code, adapters and weights)). No open-source release is listed for ScaleDown.

Other comparisons with Kev or ScaleDown

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