Jev 1.14.0 can inspect images, answer typed visual questions, and return normalized bounding boxes, but image and box support are preview “danger zone” features rather than production guarantees. Pin the model as jev-1.14.0, add the TypeSafe-Danger-Zone-Images: true header to direct TypeSafe requests, put image bytes in a top-level assets map, and reference each asset from your state or question. For reliable grounding, ask a separate presence question beside every box question and gate automation on both presence and confidence.
What Jev 1.14.0 adds
TypeSafe AI announced Jev 1.14.0 on 14 October 2026. Jev is a typed decision model: your application supplies state and questions, and the model returns structured answers instead of an open-ended prose response. This release adds experimental image inputs, a new box question type, and an opt-in longer context window. TypeSafe describes the new capabilities as “danger zone” features and says the remaining jaggedness is too high to vouch for production use cases. Read the launch announcement at TypeSafe AI’s Jev 1.14 post.
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The practical consequence is that Jev can now participate in typed visual workflows: classify whether an object is present, inspect a product photo, or estimate the location of one described target. It is not an image-generation API, OCR pipeline with guaranteed accuracy, or a replacement for deterministic computer-vision checks. Treat every answer as a model judgment that your application must validate.
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Choose a model identifier before writing code
Use an immutable identifier whenever image or box behavior matters. The preview alias is jev-preview, while the cited stable alias remains jev-latest for the Jev 1.13.0 line. An alias can move to a different model without a code change, making visual results difficult to reproduce. Set model to jev-1.14.0 in development, tests, and production deployments that depend on this feature. The model guide documents the release and aliases at Jev Model Guide: Jev 1.14.
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| Identifier | What it represents | Use when |
|---|---|---|
jev-1.14.0 |
Specific Jev 1.14 release | You need reproducible image or box behavior and explicit rollback control. |
jev-preview |
Moving preview alias | You are experimenting and can tolerate behavior changing underneath the application. |
jev-latest |
Stable alias cited for the 1.13.0 line | You are staying on the earlier stable line and do not need Jev 1.14 image features. |
How image requests are represented
Jev keeps image data separate from logical state. Send a top-level assets map. Each entry has a key, the asset type, a base64 payload, and an image format. In your state, question instructions, or criteria, refer to the asset by that key. TypeSafe’s multimodal documentation describes this as composable asset substitution: an image can be inserted wherever a normal state value would be used. See the official Jev multimodal guide.
The following JavaScript object shows the wire shape. The variable base64Png must contain the complete base64 encoding of your PNG file; do not include a data-URL prefix such as data:image/png;base64, unless the endpoint documentation for your gateway explicitly asks for one.
const requestBody = {
model: "jev-1.14.0",
state: { photo: "shot" },
assets: {
shot: {
type: "image",
data_base64: base64Png,
format: "png"
}
},
questions: {
present: {
type: "noul",
instructions: "Is the target present in `photo`?"
}
}
};In this example, photo resolves to the asset named shot. The reference is deliberate: it lets the same image be used by multiple questions or criteria without copying the encoded bytes into each one. The official guide lists PNG, JPG, and JPEG formats.
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Direct access gives you control over the preview opt-in header and the raw asset structure. Every request containing an image or a box question must include TypeSafe-Danger-Zone-Images: true; otherwise TypeSafe rejects the request. Keep the header on the request that actually carries visual input rather than assuming a project-level setting will apply.
curl https://api.typesafe.ai/v1/jev
-H "Authorization: Bearer $TYPESAFE_API_KEY"
-H "Content-Type: application/json"
-H "TypeSafe-Danger-Zone-Images: true"
--data @request.jsonA corresponding request.json file can be generated by your application so the base64 value is never hand-edited:
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{
"model": "jev-1.14.0",
"state": { "photo": "shot" },
"assets": {
"shot": {
"type": "image",
"data_base64": "BASE64_VALUE_GENERATED_BY_YOUR_APPLICATION",
"format": "png"
}
},
"questions": {
"present": {
"type": "noul",
"instructions": "Is the target present in `photo`?"
}
}
}Replace the string with the encoded file contents at runtime. Validate the MIME type and format together: a file named .png whose bytes are actually JPEG data should be rejected by your upload validation before it reaches Jev.
Using Jev 1.14 from TypeScript
When the AI SDK route makes sense
If your application already uses the AI SDK, Vercel’s AI Gateway changelog documents Jev 1.14 under the model name typesafe-ai/jev-1.14.0. Its example sends a text state item and an image/png file part loaded with Node’s readFileSync, then asks a typed boolean question such as whether the image is predominantly red. The example is available at Vercel’s Jev 1.14 AI Gateway changelog.
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import { generateObject } from "ai";
import { z } from "zod";
const imageBytes = readFileSync("./photo.png");
const result = await generateObject({
model: "typesafe-ai/jev-1.14.0",
schema: z.object({
predominantlyRed: z.boolean()
}),
messages: [
{
role: "user",
content: [
{ type: "text", text: "Evaluate the attached image." },
{
type: "file",
data: imageBytes,
mediaType: "image/png"
}
]
}
]
});
console.log(result.object.predominantlyRed);The gateway example demonstrates SDK ergonomics, not a replacement for the direct protocol. The AI SDK provider decides how file parts become a Jev request, while direct TypeSafe calls expose the canonical assets map and danger-zone header. Keep the model identifier explicit in either path, and confirm how your provider reports usage, errors, and request-size limits before deploying.
Ask a presence question before trusting a box
A box question locates one described target. Jev returns four normalized coordinates—top, bottom, left, and right—as fractions from the image’s top-left corner, plus a confidence value. A box answer always exists; there is no built-in “not found” result. The multimodal guide states this explicitly: “A box answer always contains a box — there is no ‘not found’.”
Because a box is returned even when the target is absent, pair each box question with a separate noul (boolean-style) presence question. Only use coordinates when the presence answer is positive and confidence clears a threshold chosen for your application. One box question should describe one target; do not ask a single box question to locate several unrelated objects.
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const requestBody = {
model: "jev-1.14.0",
state: { photo: "shot" },
assets: {
shot: {
type: "image",
data_base64: base64Jpeg,
format: "jpeg"
}
},
questions: {
present: {
type: "noul",
instructions: "Is a red safety helmet visible in `photo`?"
},
helmetBox: {
type: "box",
instructions: "Locate the red safety helmet in `photo`."
}
}
};Suppose the image is 1,600 pixels wide by 1,000 pixels high and the returned box is left: 0.20, right: 0.50, top: 0.10, bottom: 0.60. Convert it to pixel coordinates as follows:
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left: Math.round(0.20 * 1600),
right: Math.round(0.50 * 1600),
top: Math.round(0.10 * 1000),
bottom: Math.round(0.60 * 1000)
};
// { left: 320, right: 800, top: 100, bottom: 600 }Store the original normalized values as well as converted pixels. Pixel coordinates depend on the exact image dimensions and become invalid if a downstream service resizes or crops the image. Keep the image dimensions, model version, presence result, confidence, and any decision threshold in the same log record.
Improve grounding with crops and simple questions
The Jev Model Guide recommends cropping when a target is small inside a large image. A crop increases the target’s share of the pixels and reduces competing visual detail. Preserve the crop’s offset and scale so a box measured in crop coordinates can be mapped back to the original image.
Break complicated visual judgments into several typed questions. For example, ask whether a package is present, whether its label is facing the camera, and whether a warning color is visible as separate booleans instead of one compound instruction. This makes each answer easier to validate and lets your application require a particular combination before taking action.
- Use one
boxquestion for one described target. - Use a presence question for every target that can be absent.
- Crop first when the target occupies only a small region.
- Prefer several narrow typed questions to one ambiguous visual paragraph.
- Route low-confidence or contradictory answers to human review.
Preview limits, image size, and long context
The limits published for Jev 1.14 vary by service, so treat them as preview-era guidance rather than a universal contract. The Jev Model Guide playground reports up to four PNG or JPEG images per request, dimensions of roughly 256 to 1,568 pixels per side, and about 4 MB per image on that service. The independent multimodal guide reports a whole-request cap near 32 MiB. Validate the actual endpoint you deploy instead of assuming the playground limits apply unchanged.
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| Constraint | Published guidance | How to design around it |
|---|---|---|
| Images per request | Up to four PNG/JPEG images on the Jev Model Guide playground | Reject excess files before upload or split the work into separate calls. |
| Image dimensions | Roughly 256–1,568 pixels per side on the playground | Resize oversized images while preserving the target; avoid shrinking small targets below a useful size. |
| Per-image size | About 4 MB on the playground | Check encoded and decoded sizes before constructing the request. |
| Whole request | Near 32 MiB in the multimodal guide | Account for base64 expansion, state, questions, and all assets together. |
Jev 1.14 also offers an opt-in long-context mode with an input budget of approximately 262,000 tokens. TypeSafe’s launch post and the playground both warn that longer contexts are less reliable. Send TypeSafe-Danger-Zone-Long-Context: true only when trimming the input is impractical. A larger budget does not make visual answers more accurate; it only permits more input.
curl https://api.typesafe.ai/v1/jev
-H "Authorization: Bearer $TYPESAFE_API_KEY"
-H "Content-Type: application/json"
-H "TypeSafe-Danger-Zone-Images: true"
-H "TypeSafe-Danger-Zone-Long-Context: true"
--data @long-request.jsonBilling and image token usage
The Jev Model Guide lists a reference rate of $0.042 per 1 million input tokens, with output free, and says images count as input tokens. The multimodal guide likewise describes image usage as ordinary input-token metering; exact image tokenization is performed by the model and reconciled after the call. Treat $0.042 as a current published reference, not a guaranteed long-term price. Record the usage returned by each response so your cost reports reflect the provider’s final accounting.
Image dimensions, compression, and the number of assets affect the amount of input sent. Do not estimate spend from file bytes alone: base64 size and model-side image tokenization are separate concerns. Set application-level quotas and alert on unexpected usage before enabling unrestricted uploads.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Direct TypeSafe access versus an AI Gateway
Both routes can expose Jev 1.14, but they optimize for different operational needs. Direct access is clearer when you need the official preview headers and raw asset semantics. An AI Gateway is convenient for teams already standardized on the AI SDK and its provider abstraction.
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|---|---|---|
| Model pinning | Set model: "jev-1.14.0" in the request. |
Use the documented provider identifier typesafe-ai/jev-1.14.0. |
| Image opt-in | You explicitly send TypeSafe-Danger-Zone-Images: true. |
The provider maps SDK file parts to its backend; verify how it forwards preview requirements. |
| Request shape | Canonical top-level assets map and asset references. |
SDK message content with a file part such as image/png. |
| SDK ergonomics | More HTTP and validation code, with maximum protocol visibility. | Convenient integration when your app already uses the AI SDK. |
| Request-size behavior | Validate TypeSafe’s endpoint limits directly. | Account for both gateway and upstream limits. |
| Observability and billing | Inspect TypeSafe response usage and errors directly. | Track gateway telemetry as well as upstream usage and reconciliation. |
| Fallbacks | Implement retries, alternate models, or human review in your own service. | Use gateway/provider controls where available, but test that preview headers and structured answers survive fallback. |
Production-readiness checklist
Jev 1.14 image and box features should be treated as guarded preview dependencies. Before allowing an answer to trigger a consequential operation, implement all of the following:
- Pin the release. Use
jev-1.14.0, not a moving alias, in configuration and test fixtures. - Opt in explicitly. Send
TypeSafe-Danger-Zone-Images: truefor every direct image or box request. Add the long-context header only for requests that truly need it. - Validate uploads. Check MIME type, decoded dimensions, encoded size, total request size, and the number of assets before calling Jev.
- Pair box with presence. Require a positive presence answer and a confidence value above your documented threshold before using coordinates.
- Normalize coordinates safely. Preserve source dimensions, reject values outside the expected 0–1 range, and convert to pixels only after confirming which image the box refers to.
- Design for uncertainty. Send low-confidence, contradictory, or malformed results to a review queue rather than an irreversible action.
- Log enough to reproduce. Record model identifier, request and asset metadata, response confidence, usage, latency, and failure category. Avoid retaining sensitive image bytes unless your data policy permits it.
- Test drift. Keep a representative image set and rerun it whenever you change the pinned model, gateway, preprocessing, or question wording.
Troubleshooting common failures
Request rejected when an image is present
Check that the direct request includes the exact header TypeSafe-Danger-Zone-Images: true. Confirm that your HTTP client did not drop custom headers during a redirect or proxy hop. If you are using an SDK gateway, inspect provider documentation and request logs to verify that the gateway supports Jev 1.14 image parts.
“Unsupported format” or size errors
Decode the file, identify its actual MIME type, and send only the documented PNG, JPG, or JPEG formats. Measure both each image and the complete request; base64 encoding increases transport size, and several individually acceptable images can still exceed a whole-request cap. Resize or recompress, then retry with the same pinned model.
Boxes appear even when the object is absent
This is expected behavior, not a missing error flag. Jev always returns a box for a box question. Add the separate presence question, enforce a confidence threshold, and discard coordinates when presence is false or confidence is insufficient.
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Coordinates are inaccurate for a tiny object
Crop around the relevant region, ask one box question for the target, and retain the crop’s offset so you can map the normalized result back to the original image. If the target remains ambiguous, route the case for review instead of lowering the threshold until every image passes.
Long-context requests are inconsistent
Trim irrelevant state and images first. The approximately 262k-token mode is an opt-in capacity increase, not an accuracy setting, and the launch materials warn that longer contexts are less reliable. Use it only when a smaller request cannot represent the task.
Example decision flow for a visual action
A safe application can keep Jev’s model judgment separate from its business action:
- Receive the upload and verify format, dimensions, and size.
- Generate a request with
model: "jev-1.14.0", one asset key, a presence question, and one box question. - Send the request with the image danger-zone header.
- Validate the response schema and confirm that normalized coordinates are within 0–1.
- Require presence to be true and confidence to meet the configured threshold.
- Convert coordinates using the original dimensions, or map them from a crop using its offset and scale.
- Apply the business rule only after these checks; otherwise queue the image for review.
- Log the model version, question identifiers, confidence, usage, and final disposition.
This separation lets you change thresholds, preprocessing, or review policy without pretending that a typed answer is a deterministic guarantee.
Is Jev 1.14 production-ready?
Not for unconditional, high-consequence automation. TypeSafe’s 14 October 2026 announcement explicitly warns that image and related features remain too jagged for a production-use guarantee. You can still use Jev 1.14 in a controlled application when the model is pinned, inputs are validated, visual answers are cross-checked, failures are observable, and a human or deterministic system can stop an unsafe action. Start with low-risk classification or review-assistance workflows, measure your own error rates, and promote only the cases that meet your documented threshold.
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