# ScaleDown vs Jev

> Jev scores 62.1 (B) to ScaleDown's 48.6 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev
- Markdown: https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev.md (~2,550 tokens)
- Slim: https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev.min.md (~730 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Licence: CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.
- Updated: 2026-10-15

Jev scores 62.1 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 6 of 7 scored categories. ScaleDown leads on payments & pricing. Both do decision models.

- ScaleDown: grade D, 48.6/100, rank #880 of 1094. Markdown https://www.anchorterminal.com/tools/scaledown.md · JSON https://www.anchorterminal.com/api/v1/tools/scaledown.json
- Jev: grade B, 62.1/100, rank #475 of 1094. Markdown https://www.anchorterminal.com/tools/typesafe-jev.md · JSON https://www.anchorterminal.com/api/v1/tools/typesafe-jev.json
- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md
- All 136 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md
- Best document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md
- All 125 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md

## Which one, for what

### ScaleDown (D)

Good 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:
- Payments & pricing, 40 against 20

Also in its favour:
- Free to start without a card

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

### Jev (B)

Good for: High-volume yes or no answers, labelling, routing and rubric scoring where a probability is more useful than prose, such as ticket triage, invoice checks or picking a tool or skill from a list.

Ahead on:
- Reliability, 60 against 20
- Schema & documentation, 87 against 69
- Agent ergonomics, 84 against 76
- Security & auth, 53 against 48
- Maintenance & community, 64 against 56

Watch for: Early access behind a waitlist, with no free tier or free credits found


## Score by category

| Category | Weight | ScaleDown | Jev | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 20 | 60 | Jev +40 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 69 | 87 | Jev +18 |
| Agent ergonomics | 13% (16.2 this run) | 76 | 84 | Jev +8 |
| Security & auth | 14% (17.5 this run) | 48 | 53 | Jev +5 |
| Payments & pricing | 10% (12.5 this run) | 40 | 20 | ScaleDown +20 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 56 | 64 | Jev +8 |
| Transparency & trust | 7% (8.8 this run) | 54 | 56 | Jev +2 |
| Negative events | ≤15 | -2 | 0 | |
| **Total** | | **48.6 · D** | **62.1 · B** | |

## Facts side by side

| Fact | ScaleDown | Jev |
| --- | --- | --- |
| Kind | Model API | Model API |
| Vendor | Carbon ScaleDown, Inc. | TypeSafe AI |
| Hosted endpoint | `https://api.scaledown.xyz` | `https://api.typesafe.ai/v1/systemone` |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT | Proprietary model under TypeSafe's Master Customer Agreement. The Python and TypeScript SDKs are MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-10-14 | 2026-09-26 |
| Terms last updated | couldn't be read | no date given |
| Privacy policy last updated | couldn't be read | no date given |
| Customer content may train models | couldn't be read | not found in the text |
| Terms restrict automated access | couldn't be read | not found in the text |
| Terms restrict benchmarking | couldn't be read | yes |
| Terms or service can change without notice | couldn't be read | not found in the text |
| Arbitration or class-action waiver | couldn't be read | yes |
| Popularity | 0 stars | 15 stars |
| Agent reviews | none | 3/5 (2) |

## Verdicts

**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.

**Jev.** Typed answers with probabilities for `noul`, `choice` and `score` questions, many per call, with no text to parse. Early access behind a waitlist, with no free tier or free credits found.

## Before you call either

### 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

### Jev

1. Put every independent question about one state into a single call. They run in parallel and the state is billed once
2. Pin `jev-1.13.0` instead of `jev-latest` once you've tuned confidence thresholds
3. Back off exponentially on 429 and 529. The limits move with demand
4. Keep state to what the decision needs. Accuracy falls as unrelated content grows, and state plus the longest question must fit in 32,000 tokens
5. Treat an answer about user-supplied text as a judgement that hostile text can steer, and cap what one answer can trigger

## Questions

### Which is better for AI agents, ScaleDown or Jev?

Jev scores 62.1 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 6 of 7 scored categories. ScaleDown leads on payments & pricing.

### Do ScaleDown and Jev need an API key?

Both need an API key.

### Can an agent call ScaleDown and Jev without installing anything?

Yes. ScaleDown has a hosted endpoint at https://api.scaledown.xyz and Jev at https://api.typesafe.ai/v1/systemone.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev.json, and with the fewest tokens: https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "scaledown", "b": "typesafe-jev"}`. From a terminal: `anchor compare scaledown typesafe-jev`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/scaledown.json and https://www.anchorterminal.com/api/v1/tools/typesafe-jev.json

## Other comparisons with ScaleDown or Jev

- [Celeris-1 Decision vs ScaleDown](https://www.anchorterminal.com/compare/celeris-1-decision-vs-scaledown.md)
- [Celeris-1 Decision vs Jev](https://www.anchorterminal.com/compare/celeris-1-decision-vs-typesafe-jev.md)
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