TypeSafe AI released Jev 1.14 with image support, a Box type, and a 262k context window.
TypeSafe AI announced Jev 1.14. The release title says the version adds image support, a Box type, and a 262,000-token context window. The supplied page text did not include model size, benchmark scores, licensing, weights availability, or pricing.
Jev 1.14 adds images, a Box type, and 262k context.
The provided text includes no size, benchmark, license, or weight details.
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TypeSafe AI has released Jev 1.14, a new version of its System One decision model. It is live now under the
jev-preview
alias. It adds 3 experimental ‘danger zone’ features: images, a
box
type and longer context. For developers, Jev 1.14 pushes a typed decision model toward vision tasks like inspection and localization. TypeSafe shares a note that none of the 3 features are ready for production yet.
TL;DR: Jev 1.14
Size: Parameters not disclosed. Context is 64k tokens per request and 32k per question by default, or 262k with an opt-in header.
Runs on: TypeSafe’s hosted API only (
POST /v1/systemone
). No open weights. Hardware and quantized variants not disclosed.
Performance: Small gains everywhere, largest in multilingual support (TypeSafe’s claim, no public scores).
Best: In TypeSafe’s Box test, 4 edge-check questions caught 30 of 36 cut-off crops.
Worst: Images and Box are not production-ready, and Box has no “not found” answer.
What is Jev 1.14?
Jev is a System One model from TypeSafe AI that returns typed decisions instead of generated text. You send a
PNG or JPEG still images only, up to 16 MiB per file
256 to 1568 pixels per side
32 MiB per request body
Images bill as input tokens at the normal price, with no separate charge
TypeSafe warns that all models, Jev included, perform worse on images than on pure text. It recommends the simplest possible questions.
What is the new Box type?
Box is a new question type that returns 1 bounding box per question. The answer gives
top
,
bottom
,
left
and
right
edges as fractions from 0 to 1. It also returns a confidence score. Box needs
jev-1.14.0
or
jev-preview
, plus the images header.
Box always returns a box. A question about an absent object still returns 4 edges. TypeSafe suggests pairing it with a Noul question that checks whether the target exists.
TypeSafe also shared a small test on 36 painting crops, each cutting off part of the target. 4 per-edge Noul questions caught 30 cuts. A single “is all of it inside” question caught 26. Drawing the box as an outline and asking about it caught 20.
How long is Jev’s context window now?
By default, both models keep 64k tokens per request and 32k per question. Sending
TypeSafe-Danger-Zone-Long-Context: true
raises both limits to 262k tokens, per the Models docs. TypeSafe states that Jev always supported longer context internally. It capped context on purpose because Jev, like all models, is less reliable on long inputs. The dev team still recommends short contexts.
What do the benchmarks show?
TypeSafe published internal intelligence charts in the release post. They compare Jev only against open-weight models. The post does not include a numeric score table. TypeSafe says the measurement is likely biased toward its own models. It tells developers to favor their own evals.
Pricing, availability and migration
Pricing is unchanged at $42 per billion input tokens, or $0.042 per million. Output tokens are free, per the Models docs.
Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.