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Use Jev 1.14.0 with images by placing base64-encoded files in a top-level assets map, referring to each asset from your state or question, and sending TypeSafe-Danger-Zone-Images: true on direct TypeSafe requests. Pin jev-1.14.0 rather than the moving jev-preview alias when image or box behavior must be reproducible. Jev 1.14 is an experimental, typed decision model: it returns structured answers to questions about supplied state, not free-form prose. TypeSafe AI announced version 1.14.0 on 14 October 2026, and explicitly warns that its image and box capabilities are not ready for production guarantees.
What Jev 1.14 adds
Jev is designed for applications that provide state and ask typed questions. Instead of asking the model to write an explanation, you define questions such as a boolean presence check and receive a value that your TypeScript code can validate and route. Version 1.14.0 adds experimental image inputs, a box question type for visual localization, and an optional longer context window. TypeSafe describes these additions as “danger zone” features because reliability and interface behavior can still change. Read the launch announcement at TypeSafe AI’s Jev 1.14 announcement.
The aliases matter when you deploy:
| Identifier | Use | What to expect |
|---|---|---|
jev-1.14.0 |
Recommended production-like testing and repeatable experiments | Explicitly selects the 1.14.0 model version |
jev-preview |
Trying the current preview | Moving alias; behavior can change without an application code change |
jev-latest |
Existing 1.13 line in the cited materials | Does not select the 1.14 image and box behavior |
Do not treat a successful response as a guarantee that an object exists in an image. A Jev answer is a model judgment. For actions such as deleting a record, approving a payment, or moving a robot, require your own validation, confidence thresholds, and (where appropriate) human review.
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Image requests use two separate pieces: the binary image is described once in assets, and your typed state or questions refer to the asset by key. This makes the image composable: you can replace the asset reference without rewriting every question. The official multimodal guide documents PNG, JPG, and JPEG formats and this same reference pattern at the Jev multimodal guide.
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A minimal direct HTTP body looks like this (the value in data_base64 must contain the actual base64 text, without a data-URL prefix):
{
"model": "jev-1.14.0",
"state": {"photo": "shot"},
"assets": {
"shot": {
"type": "image",
"data_base64": "<base64 image bytes>",
"format": "png"
}
},
"questions": {
"present": {
"type": "noul",
"instructions": "Is the target present in `photo`?"
}
}
}The string shot is the asset key. The state value photo points to that key, and the question refers to the state field with photo. You may also reference an asset directly from a question’s instructions or criteria when that is clearer for your schema.
Send an image directly to TypeSafe
Direct HTTP is the clearest route when you need control over the official preview headers and raw asset structure. The request must include TypeSafe-Danger-Zone-Images: true whenever it contains an image or a box question. Without that opt-in, TypeSafe rejects the request rather than silently ignoring the image.
- Choose and pin the model. Set
modeltojev-1.14.0. Avoid relying onjev-previewfor a workflow whose outputs must be comparable over time. - Read the file and encode it. Convert the bytes to base64 on your server. Keep the original MIME-derived format separately as
png,jpg, orjpeg. - Build the asset map. Give the image a short key, set
typetoimage, and put the base64 payload indata_base64. - Reference the key from state. For example, set
state.phototoshot, then ask aboutphoto. - Set the danger-zone header. Add
TypeSafe-Danger-Zone-Images: trueto the HTTP request. - Validate the typed result. Check the returned question value and any confidence or error information before taking an external action.
A generic Node.js request skeleton is:
import { readFile } from 'node:fs/promises';
const bytes = await readFile('./photo.png');
const payload = {
model: 'jev-1.14.0',
state: { photo: 'shot' },
assets: {
shot: {
type: 'image',
data_base64: bytes.toString('base64'),
format: 'png'
}
},
questions: {
present: {
type: 'noul',
instructions: 'Is the target present in `photo`?'
}
}
};
const response = await fetch('https://api.typesafe.ai/v1/jev', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${process.env.TYPESAFE_API_KEY}`,
'TypeSafe-Danger-Zone-Images': 'true'
},
body: JSON.stringify(payload)
});
if (!response.ok) {
throw new Error(`Jev request failed: ${response.status} ${await response.text()}`);
}
const result = await response.json();The exact endpoint path and authentication setup depend on the TypeSafe client or account configuration you use; the important Jev-specific parts are the pinned model, top-level asset map, asset reference, and opt-in header. Keep the header on every image or box call, including retries.
Use Jev 1.14 from TypeScript through an AI SDK
If your application already uses an AI SDK, an AI Gateway route can carry a Jev image request without you constructing the low-level asset JSON yourself. The gateway changelog shows model identifier typesafe-ai/jev-1.14.0, a text state item, and an image/png file part populated with readFileSync. See the example at the AI Gateway Jev 1.14 changelog.
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import { readFileSync } from 'node:fs';
import { generateObject } from 'ai';
import { z } from 'zod';
const image = readFileSync('./photo.png');
const { object } = await generateObject({
model: 'typesafe-ai/jev-1.14.0',
schema: z.object({
predominantlyRed: z.boolean()
}),
messages: [{
role: 'user',
content: [
{ type: 'text', text: 'Is the image predominantly red?' },
{ type: 'file', data: image, mediaType: 'image/png' }
]
}]
});
console.log(object.predominantlyRed);Use this route when the rest of your stack already standardizes on the AI SDK. Use direct TypeSafe HTTP when you need to set and audit the canonical danger-zone header yourself, inspect the raw asset references, or keep the request format identical to TypeSafe’s documentation. In either route, keep the Jev model identifier explicit so a gateway or SDK default cannot move your application to another model.
Ask visual presence questions safely
A presence question should be small and typed. Ask one observable condition rather than requesting a paragraph of visual interpretation. For example, “Is the image predominantly red?” or “Is the target present in photo?” maps naturally to a boolean or noul result. If your business rule needs several conditions, create separate questions and combine them in application code. This lets you log which judgment failed and avoids hiding multiple uncertain decisions inside one prompt.
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- Use a specific noun for the target and include the state field or asset reference the question should inspect.
- Define what “present” means in your application: fully visible, partially visible, or any recognizable portion.
- Reject unexpected types and missing answers before passing a result to downstream code.
- For irreversible operations, require an application-level confidence threshold or a second verification step.
Get a bounding box with the box question type
A box question asks Jev to locate one described target. The answer contains top, bottom, left, and right values as fractions from 0 to 1, measured relative to the image’s top-left corner, plus a confidence value. Multiply the normalized coordinates by the rendered image width and height to draw a pixel rectangle.
"questions": {
"found": {
"type": "noul",
"instructions": "Is a red bicycle present in `photo`?"
},
"bicycleBox": {
"type": "box",
"instructions": "Locate the red bicycle in `photo`."
}
}Jev always returns a box answer for a box question; there is no built-in “not found” result. That behavior makes a box unsafe as a presence test. Pair every box with a boolean or noul presence question and use the coordinates only when the presence answer is affirmative and the confidence clears your own threshold.
- Ask one box question for one target. If an image contains three cars, create three separately named questions only if your application can distinguish them.
- Inspect the presence result first. Stop if it says the target is absent.
- Check the box confidence against a threshold chosen from your validation data; Jev does not choose that threshold for you.
- Check coordinate order and range in your code (
0 <= left <= right <= 1and the equivalent vertical relationship). - Convert normalized values to pixels after accounting for the displayed image’s actual dimensions.
The Jev Model Guide recommends cropping when a target is small in a large image. Crop and re-run the question when the object occupies too few pixels for a reliable judgment. One box question should describe one target rather than asking Jev to return a list of unrelated regions. Details and preview guidance are available at the Jev 1.14 model guide.
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Image, request-size, and context limits
Preview limits vary by service, so validate them against the endpoint you deploy rather than hard-coding playground observations as universal guarantees.
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|---|---|---|
| Images per request | Up to four PNG/JPEG images on the Jev Model Guide playground | Keep a lower application limit if your endpoint or gateway has stricter rules |
| Image dimensions | Roughly 256–1568 pixels per side on the playground | Resize or reject images outside that range before encoding |
| Per-image size | About 4 MB on the playground | Check the encoded and decoded size before upload |
| Whole request | Approximately 32 MiB in the independent multimodal guide | Count JSON, base64 expansion, state, and all assets together |
| Long context | Approximately 262,000 input tokens with opt-in | Use only when trimming is impractical; longer contexts are less reliable |
Base64 increases payload size compared with the original binary, and JSON adds additional bytes. Validate the original dimensions, MIME type, decoded bytes, encoded request size, and number of assets before sending. A request that fits a local file-size check can still exceed a gateway’s whole-request limit after encoding.
Opt in to long context only when necessary
The long-context feature raises the input budget to approximately 262k tokens when you send TypeSafe-Danger-Zone-Long-Context: true. Both TypeSafe’s launch post and the playground warn that longer contexts are less reliable. First remove redundant state, shorten instructions, and split a large decision into several typed calls. Add the header only when those changes would discard information the question genuinely needs. Record the header in your request logs so a later result can be interpreted with the correct context setting.
Understand billing for images
The Jev Model Guide lists a reference rate of $0.042 per 1 million input tokens with output free, and says image usage is metered as ordinary input tokens. The multimodal guide notes that exact image tokenization is model-side and reconciled after the call. Therefore, do not estimate an image’s cost from pixel count alone or assume every encoder produces the same token count. Treat $0.042 per 1 million input tokens as a current published reference, not a permanent price, and use the usage data returned by your account for chargeback and alerts.
Direct TypeSafe access or an AI Gateway?
| Decision point | Direct TypeSafe request | AI Gateway with an AI SDK |
|---|---|---|
| Model selection | Set model: "jev-1.14.0" in the request body |
Set typesafe-ai/jev-1.14.0 in the SDK call |
| Image representation | Explicit assets map with base64 data and format |
SDK file part, such as an image/png buffer |
| Danger-zone header | You control TypeSafe-Danger-Zone-Images: true directly |
Confirm how your gateway forwards or exposes the opt-in behavior |
| SDK ergonomics | More plumbing, but raw request and response visibility | Convenient if your application already uses the AI SDK |
| Request-size behavior | Validate TypeSafe’s endpoint limits and base64 expansion yourself | Validate both gateway and upstream limits |
| Observability | Log HTTP status, model, usage, and raw typed answers | Combine gateway telemetry with Jev usage and answer logs |
| Fallbacks | Implement another model or a non-AI path in your service | Use gateway routing only if its documented fallback meets your requirements |
Neither route removes the experimental status of Jev 1.14 image and box features. The route is an integration choice, not a reliability guarantee.
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Production-readiness checklist
- Pin the version: use
jev-1.14.0and record it with every result. - Opt in explicitly: send
TypeSafe-Danger-Zone-Images: truefor all direct image or box requests. - Validate uploads: check MIME type, dimensions, decoded size, image count, and complete request size before encoding.
- Separate detection from localization: pair every box question with a presence question and gate coordinates on presence plus confidence.
- Prefer simple decisions: decompose complicated visual judgments into independently typed questions.
- Crop deliberately: give small targets more pixels instead of sending a huge image that makes the target hard to resolve.
- Log enough to reproduce: model version, request identifier, asset metadata (not sensitive image contents), question names, confidence, usage, latency, and failure status.
- Protect sensitive images: avoid logging base64 payloads, apply retention controls, and restrict access to stored request artifacts.
- Review before irreversible actions: treat outputs as model judgments and provide a human or deterministic verification path for high-impact decisions.
- Monitor preview drift: run a fixed image fixture set after SDK, gateway, or model changes and compare typed answers and coordinate distributions.
Troubleshoot common failures
Request rejected even though the JSON is valid
Confirm that the request includes TypeSafe-Danger-Zone-Images: true. The opt-in is required for image and box requests; a syntactically correct body is still rejected without it. If you are using a gateway, inspect the outgoing request or gateway documentation to verify that the required capability is enabled upstream.
Jev says a target is absent but the box looks plausible
This is an expected risk because a box answer always exists. Do not use the rectangle by itself. Require an affirmative presence answer and a confidence value above your application threshold, then consider cropping and asking again at a larger effective scale.
The image is refused for size or format
Check that the format is PNG, JPG, or JPEG, that dimensions are within the service’s preview range, and that the decoded file is near or below the published per-image limit. Reduce resolution or JPEG quality, remove unnecessary images, and recalculate the complete post-base64 request size. Gateway limits may be lower than the playground’s.
Results change between runs
Pin jev-1.14.0, retain a stable question definition, and avoid the moving jev-preview alias for comparisons. Log usage and confidence so you can distinguish model variation from a changed image, prompt, or context budget. Preview features can still be jagged even with a pinned version.
Long-context calls are less dependable
Remove stale state and split the task before enabling long context. If the larger budget is unavoidable, send TypeSafe-Danger-Zone-Long-Context: true, mark the result as using that mode in telemetry, and apply stricter review rules to low-confidence answers.
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A practical request flow
For a service that classifies uploaded photographs and optionally draws a region, the following order keeps uncertainty visible:
- Receive the upload and validate its MIME type, dimensions, decoded bytes, and security policy.
- Resize or crop the file if it exceeds the endpoint’s preview limits; preserve the transformed dimensions for coordinate conversion.
- Encode the final bytes and place them under one key in
assets. - Send a presence question and, only when a region is needed, one box question for the same target.
- Reject malformed typed answers, out-of-range normalized coordinates, absent targets, and confidence below your threshold.
- Convert an accepted box to display pixels and draw it on the exact image variant Jev inspected.
- Store the pinned model, question names, confidence, usage, and decision outcome for auditing without retaining the raw image longer than necessary.
- Route uncertain or high-impact cases to a deterministic check or human reviewer instead of silently proceeding.
This flow uses Jev for structured visual judgments while leaving validation, authorization, privacy, and irreversible business decisions under your application’s control.
Frequently Asked Questions
What header enables Jev image input?
Direct requests containing an image or a box question must include TypeSafe-Danger-Zone-Images: true. Without that opt-in, TypeSafe rejects the request.
Can Jev return a bounding box?
Yes. Jev 1.14’s box question returns normalized top, bottom, left, and right coordinates plus confidence. It always returns a box, so pair it with a presence question before using the coordinates.
Is Jev 1.14 production-ready?
TypeSafe labels image and box support experimental danger-zone features and says it cannot vouch for them for production use cases. Pin the model, validate every answer, log confidence and failures, and require review for high-impact actions.
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