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Use Jev 1.14 with its normal budgets first: 64K tokens per request and 32K tokens for state plus one question. Trim irrelevant state, summarize older turns, send only decision-relevant images, and enable the experimental long-context setting only when removing context would damage the answer. Jev charges $0.042 per million input tokens and nothing for output, so every unnecessary text token or repeated image reference increases the bill. Jev 1.14.0 is a preview released on 14 October 2026; pin jev-1.14.0 for repeatable tests rather than relying on the moving jev-preview alias.
What Jev 1.14 changes
Jev 1.14.0 is a preview release dated 14 October 2026. The release adds a vision path for still images, the experimental box question type, and a higher experimental context ceiling. TypeSafe describes it as “small, subtle improvements to everything,” with the largest expected gain in multilingual support. That is a vendor characterization, not an independently measured benchmark; no verified percentage improvement is established.
Pin the model while testing
Use jev-1.14.0 when you need reproducible behavior. The jev-preview alias points to the preview line and can change as new preview builds replace it. Record the exact model ID, headers, context mode, image dimensions, and token usage with each test so a later alias change cannot silently alter your comparison.
Context budgets and what they mean for cost
| Mode | Request budget | State plus one question | Operational trade-off |
|---|---|---|---|
| Default | 64K tokens | 32K tokens | Lower latency and the recommended starting point |
| Experimental long context | Approximately 262K tokens | Approximately 262K tokens | Can preserve more information, but reported guidance says it may be slower and less reliable |
The long-context option raises both ceilings to about 262K tokens. It does not lower the per-token price. A larger state, a longer question, or more attached images simply creates more billable input. Treat the larger window as an exception for information that genuinely cannot be summarized or retrieved in smaller pieces.
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How Jev billing works
Jev input costs $0.042 per million tokens, according to TypeSafe/jev documentation for 2026. Output is free. The same input rate applies with or without long context, so the economic decision is about how many input tokens you send, not about a different long-context tariff.
| Input in one request | Calculation | Input charge |
|---|---|---|
| 10,000 tokens | 10,000 ÷ 1,000,000 × $0.042 | $0.00042 |
| 100,000 tokens | 100,000 ÷ 1,000,000 × $0.042 | $0.0042 |
| 1,000,000 tokens | 1,000,000 ÷ 1,000,000 × $0.042 | $0.042 |
These examples show the arithmetic for a single request at the documented rate. Your actual bill depends on the aggregate input tokens sent across requests. Image data is represented in the request and billed as input too; sending the same image reference again can therefore repeat its token cost.
A cost-first Jev 1.14 workflow
- Pin the preview build. Set the model to
jev-1.14.0for a test or production pilot. Store the model ID in logs instead of logging only a friendly alias. - Establish a default-mode baseline. Run the workload with the 64K request and 32K state-plus-question budgets. Measure input tokens, latency, task accuracy or confidence, and failure rate before changing context settings.
- Remove stale state. Delete unrelated turns, duplicate instructions, and old documents. Summarize history into a compact record containing decisions, constraints, unresolved questions, and citations needed for the current answer.
- Ask only the questions needed for the decision. A broad batch of speculative questions consumes input even when the application uses only one answer. Split unrelated tasks into separate, focused requests when that reduces state.
- Use retrieval or targeted excerpts before expanding the window. Select the passages that can change the answer. Keep surrounding context when it is needed to interpret a clause, table, or image, but do not send an entire archive by default.
- Enable long context conditionally. Use the documented experimental long-context setting only when summarizing or retrieval would remove information required for a correct answer. Mark those requests in telemetry so their latency and reliability can be compared with the baseline.
- Prepare images before upload. Reject unsupported files, resize oversized stills, crop irrelevant areas, and remove duplicate references. Keep the original only when fine detail or wider context changes the decision.
- Track input usage and outcomes. Log input-token count, whether long context was enabled, image count and dimensions, latency, retries, confidence, and a task-specific correctness signal. Optimize against the combined cost and quality result rather than token count alone.
When the 262K setting is justified
Long context is useful when the answer depends on many mutually related items that cannot be safely compressed: for example, a large specification in which a definition in one chapter changes an exception in another. It is a poor default for short prompts, routine classification, or conversations whose old turns can be summarized without losing constraints.
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| Question to ask | Stay with default budgets when… | Consider long context when… |
|---|---|---|
| Does every retained token affect the answer? | Most history is repetitive, resolved, or unrelated. | Removing sections would hide dependencies needed for a correct decision. |
| Can you summarize safely? | A structured summary preserves requirements and exceptions. | A summary would lose exact wording, cross-references, or evidence. |
| What is the operational impact? | Interactive latency and predictable behavior matter most. | The workload tolerates slower responses and you have measured acceptable reliability. |
| Can you reproduce a result? | A pinned model and compact state are stable. | You can record the larger payload, setting, and preview version for every run. |
Reported guidance characterizes long-context requests as potentially slower and less reliable. Do not assume that a larger window automatically improves reasoning. Compare the same real tasks in both modes, using the same success criteria, before changing a default.
Image input settings and limits
Image input in Jev 1.14 is experimental. Requests must include the header TypeSafe-Danger-Zone-Images: true. The reported accepted formats are PNG and JPEG still images. Each image may be up to 16 MiB, each side must be between 256 and 1568 pixels, and the complete request body is limited to about 32 MiB.
| Validation | Required check | Why it matters |
|---|---|---|
| MIME type | Allow only PNG or JPEG stills. | Other formats are outside the documented image path. |
| Dimensions | Keep both sides within 256–1568 pixels. | Out-of-range dimensions can be rejected or require preprocessing. |
| Decoded file size | Keep each image at or below 16 MiB. | Compressed size alone can hide a larger decoded bitmap. |
| Encoded request size | Keep the whole body near or below 32 MiB. | Several individually valid files can exceed the aggregate limit. |
| State plus assets | Count image references and accompanying text together. | Images and text both consume input budget. |
Resize and crop without destroying evidence
Resize a large screenshot to the smallest dimensions that preserve the text, control, or object relevant to the question. Crop menus, blank margins, and unrelated panels when they cannot change the answer. Keep enough surrounding context to identify the target and its relationship to nearby elements. For a document image, crop to the relevant page or region rather than uploading every page.
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Do not resend identical images
Applications that retry or ask several questions should avoid attaching the same image repeatedly unless the API workflow requires it. Repeated references can repeat image input tokens and therefore increase spend. If several questions concern one still, design the request flow to reuse a stored representation where supported, or consolidate genuinely related questions while staying within the default budget.
Recommended image gate
- Inspect the declared MIME type and verify the file decodes as PNG or JPEG.
- Read the actual pixel dimensions and reject anything outside 256–1568 pixels per side.
- Check decoded size against the 16 MiB per-image limit.
- Estimate the final encoded request, including text state and all assets, against the approximately 32 MiB body limit.
- Deduplicate files and references, then crop or resize only when the removed detail cannot affect the question.
- Send the required danger-zone header and record that it was used.
Using the experimental box question type
The new box question type returns one bounding box for a target in a still image. It is appropriate for locating one control, field, or object, such as identifying where a button appears in a screenshot.
A box answer does not prove that the target exists. Ask an independent presence question when absence is possible, and treat a “no target” result separately from coordinates. If you need several regions, issue separate questions and validate each result rather than expecting one box response to enumerate them.
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Because Box is a preview capability, validate coordinates against your own images, layouts, and failure cases before using them to drive an automated action. It is not a substitute for a production detector without workload-specific testing.
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Jev 1.14.0, image input, Box, and long context are presented as preview or danger-zone functionality. A cautious deployment can pilot text-only behavior, but image-dependent or long-context paths need explicit safeguards.
- Pin
jev-1.14.0rather than the moving preview alias. - Record every experimental header and context mode with the request.
- Keep a text-only fallback for requests that exceed image limits, fail validation, or return unusable coordinates.
- Set latency and error budgets that account for slower long-context requests.
- Test multilingual prompts separately; the release guidance identifies multilingual support as the likely largest improvement, but supplies no independent percentage benchmark.
- Re-run acceptance tests when the preview model or its documented limits change.
Troubleshooting common cost and request failures
The request is rejected before inference
Check the image MIME type, dimensions, per-image size, and aggregate body size. A request can fail even when every file is individually valid if the combined text and image payload is too large. Remove duplicates, crop irrelevant regions, and retry in default mode with fewer assets.
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Input usage is unexpectedly high
Inspect serialized state for repeated conversation turns, full documents attached to every question, and duplicate image references. Compare the logged token count before and after summarization. Output tokens are free, so focus the optimization on input state and attachments.
Long-context results are slower or inconsistent
Return to the default budgets and run the same cases with a compact, explicit summary. If the larger mode is necessary, reduce unrelated state and keep it behind a workload-specific feature flag. Record confidence and correctness, not latency alone, because a cheaper response that misses a required exception is not an improvement.
The box is in the wrong place
Verify that the image is within the documented dimensions and that the target is visible at sufficient resolution. Ask a separate presence question, inspect the returned coordinates against the original image, and route uncertain results to a text or human-review fallback. Do not trigger an irreversible action from coordinates that have not passed your own validation.
Practical decision rule
Start every Jev 1.14 integration with the pinned preview model, default context budgets, validated PNG or JPEG inputs, and detailed token and outcome logging. Increase context only when a measured task loses necessary information after trimming; add images only when they change the decision; and treat Box output as an experimentally validated signal rather than proof of detection. This sequence controls cost without hiding the reliability limits of the preview features.
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