Jev just landed and the agent stack moved in three days. TypeSafe AI released Jev on September 15. It is built for the work agents actually do most of the time: choose the next tool, score a risk, decide whether to retry, or answer yes or no. Latency is under half a second. Show more
API & Models
Model alias
A name like jev-latest that resolves to a versioned model ID and can move when a new release ships.
- Category
- API & Models
- Also known as
- —
- Related terms
- 5
- Directory entries
- 1
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
Aliases are sent in the model field like any other name. jev-latest points to the most recent stable release and jev-preview to the most recent release whether or not it is official; both currently resolve to jev-1.13.0.
Because an alias can move without a change on your side, the docs recommend pinning a versioned ID when confidence thresholds were tuned against a specific build, and moving on your own schedule.
Tagsapimodels
Related terms
Definitions that connect to this one.
jev-latestThe default model alias in the SDKs and docs: the most recent stable official Jev release, currently jev-1.13.0.API & Modelsjev-previewThe alias for the most recent Jev release whether or not it is official; currently identical to jev-latest.API & Modelsjev-1.13.0The current versioned Jev build: $42 per billion input tokens, a 64k context, and 250,000 tokens per second.API & Modelsmodel fieldThe required request field that selects which model handles the call; accepts versioned IDs and aliases.API & ModelsThresholdThe value your code compares against a score, a Noul probability, or a confidence to turn an answer into an action.Answers & Confidence
From the directory
The model card for jev-1.13.0: $42 per billion input tokens with free output, a 64k context, 250k tokens per second, and how to list the models your account can call.
Sites & GuidesDocs#official#docs#models
Official
From the community
Posts from builders shipping with Jev right now.
How the agent stack moved in Jev's first three days
Jev plays Doom at ~10 calls/sec for ~$7/hour
We love how this doomo doomonstrates real-time intelligence and what can be doone with code + AI! ~10 calls/sec = ~$7/hour
Jev plays Super Mario Bros.
got @typesafeai’s new model Jev to play Super Mario Bros. fast inference + structured outputs makes it surprisingly good for real time use cases. I'm excited to see what can be done with these new models!
LLMs generate answers, Jev makes decisions
this is the easiest way to understand Jev: LLMs generate answers. Jev makes decisions. that sounds like a small difference, but it actually changes the entire use case. say you give a normal LLM this: “here’s a user, their account history, payment behavior, support chats, Show more
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
Arbitrary classification as a primitive
Jev is cool not because it re-invented classification, but because it makes ARBITRARY classification into a type-safe programmable primitive. A general purpose zero shot decision model whose native interface is RUNTIME-DEFINED typed decisions, optimized for that exact interface
If you called Yann LeCun an idiot for saying we need to move beyond LLMs and build something new, you are banned from using Jev.
How Jev makes agents faster and cheaper
Jev solved local harness/model routing I use a combination of Claude Code, Codex and Opencode as my local agentic stack and routing to other harnesses was always enforced in the system prompt/rules With a deterministic hook that Claude Code can decide before delegation, Jev Show more