# Models

Jev is TypeSafe's flagship model and the first [System One model](/concepts/system-one). Every model on this page is served by the same endpoint, `POST /v1/systemone`. The request's `model` field selects which one handles the call; see the [API reference](/api) for the full request shape.

## Current models

| Jev 1.13                    | `jev-1.13.0`                                                                              |
| :-------------------------- | :---------------------------------------------------------------------------------------- |
| Price (per Btok / per Mtok) | \$42 / \$0.042                                                                            |
| Rate limits                 | 250,000 tokens per second / 1,200 requests per minute                                     |
| Context length              | 64k tokens per request; 32k tokens for `state` plus the longest question                  |
| Input                       | Text only. String, JSON object, or array of text values. No image, audio, or video input. |

* **Price:** Charged per input token. Output tokens are free. A Btok is a billion tokens and an Mtok is a million tokens.
* **Rate limits:** Measured in tokens per second and requests per minute. A request over either limit returns `429 Too Many Requests`. Our [client SDKs](/sdk) retry with backoff by default and honor the `retry-after` header when the response carries one. If you call the HTTP API directly, see [Handling rate limits](/api#handling-rate-limits).
* **Context length:** Jev ingests the `state` once and evaluates every question against it in parallel. The 64k budget covers the `state` plus all questions combined; the 32k budget applies to the `state` plus the single longest question. See [Speculative fan-out](/patterns/fan-out) for packing many questions into one request, and [Jev 1.13 jaggedness](/model-jaggedness/jev-1.13) for how accuracy shifts as the state grows.
* **Input:** Jev evaluates natural-language text. Pre-process non-text inputs (images, audio, video, binaries) into text or structured fields before sending them as `state`. See [State](/concepts/state) for supported shapes.

## Aliases

An alias is a model name that resolves to a versioned model ID. Send it in the `model` field like any other name.

| Alias         | Points to    | Meaning                                                                                                                       |
| :------------ | :----------- | :---------------------------------------------------------------------------------------------------------------------------- |
| `jev-latest`  | `jev-1.13.0` | The most recent stable, official release. The default in our client SDKs, and the name the examples in these docs use.        |
| `jev-preview` | `jev-1.13.0` | The most recent release, whether or not it is an official one. Moves ahead of `jev-latest` when a preview build is available. |

An alias moves when a new release ships, so the answers behind it can change without a change on your side. The response's `model` field reports the versioned ID that answered, so you can log which model produced each result. If you have tuned confidence thresholds against a specific version, pin that version's ID instead of the alias and move to the new one on your own schedule.

## Customizing Jev

Jev is not fine-tuned or LoRA-adapted with customer data. It is trained with [RLCD](/introduction/machine-learning-primer) to return calibrated decisions, and the same weights serve every account. You shape its answers to your domain through the request rather than through per-account weights:

* Put your proprietary content, records, and reference material in the `state` field. See [State](/concepts/state).
* Encode your domain rules and boundary cases in the `instructions` and `criteria` of each question. See [How to build with TypeSafe](/concepts/how-to-build-with-system-one) and [Advanced: structure](/primitives/advanced).
* Decompose broad judgments into atomic questions and combine the outputs in code. See [Composite scoring](/patterns/composite-scoring) and the [AutoResearch cookbook](/cookbooks/autoresearch_feature_discovery) for training a downstream classical model on Jev's probabilities.

## Language support

Jev accepts natural-language text. English is the primary training language and where accuracy is currently best. Other languages, including CJK scripts, are handled but not equally well; test on your own content before relying on Jev for a non-English workload, and pay close attention to [Confidence](/confidence) when routing.

## Data handling

Jev is not trained on customer requests or responses. See [Legal](/legal) for the Data Processing Agreement, the Privacy Policy, and details on zero data retention (ZDR) for enterprise customers.

## Listing models

`GET /v1/models` returns the names your account can send in the `model` field, with a description and release date for each. It currently lists the aliases. Versioned IDs such as `jev-1.13.0` are accepted by the `model` field whether or not they appear in the list.

<CodeGroup>
  ```bash cURL theme={null}
  curl https://api.typesafe.ai/v1/models \
    -H "Authorization: Bearer $TYPESAFE_API_KEY"
  ```

```python Python theme={null}
  from typesafe_sdk import TypeSafeClient

with TypeSafeClient() as client:
      for model in client.models.list().models:
          print(model.name, model.release_date, model.description)
  ```

```typescript JavaScript theme={null}
  import { TypeSafeClient } from "@typesafe-ai/sdk";

const client = new TypeSafeClient();
  const models = await client.models.list();
  for (const model of models) {
    console.log(model.name, model.release_date, model.description);
  }
  ```
</CodeGroup>

See the [Python](/sdk/python/api/clients/sync#typesafe_sdk.Models.list) and [JavaScript](/sdk/javascript/api/interfaces/Models) SDK references for the full method signatures.