Jev
Structured decision model for programmatic automation with calibrated probabilities
Context window
32K
Input / 1M tokens
$0.042
Output / 1M tokens
Free
Provider
TypeSafe AI
Data verified 2026-09-16
Jev is TypeSafe's first System One Model designed for fast, structured decisions inside software systems. Rather than generating text token-by-token, it evaluates typed questions in parallel against input state and returns constrained outputs (choices, scores, or yes/no probabilities) with calibrated confidence scores. Optimized for automation workflows requiring high-volume, repeated semantic decisions.
Capability index
Relative estimates (0-100) to place this model against its peers, grounded in published benchmarks.
How to access it
Early access via waitlist at typesafe.ai. Access through Python/TypeScript SDKs or HTTP API with API key.
Strengths
- ✓70-500ms latency (20-200x faster than LLMs)
- ✓Extremely low cost ($0.042 per million input tokens)
- ✓Returns typed, schema-constrained outputs eliminating hallucinations
- ✓Calibrated confidence scores for autonomous decision-making
- ✓Parallel evaluation of multiple questions from single input
Best for developers who...
When to choose it (and when not to)
Reach for Jev when...
- →When you need fast, repeated structured decisions at scale
- →For decisions that should run in software background, not chat
- →When you want reliable probability calibration over raw speed
- →For cost-sensitive high-volume automation
Look elsewhere if...
- ✕When you need text generation or open-ended explanation
- ✕For tasks requiring image/audio input
- ✕When you need a general-purpose chat interface
- ✕For reasoning tasks beyond bounded classification
How to use it
- ›Define bounded question types upfront (Choice, Score, Noul/yes-no)
- ›Decompose complex reasoning into separate judgment questions
- ›Send only necessary context to keep costs low and quality high
- ›Set confidence thresholds based on your specific use-case cost tolerance
Quickstart
Pythonfrom typesafe import TypeSafe
client = TypeSafe(api_key='...')
response = client.decisions(
state='customer ticket...',
questions=[
{'type': 'Choice', 'name': 'action', 'options': ['approve', 'escalate', 'deny']},
{'type': 'Noul', 'name': 'needs_human_review'}
]
)
Returns typed outputs with probability distributions and confidence scores per decision.
API model id: jev-latest
Benchmarks
| Benchmark | Score | Notes |
|---|---|---|
| System One Decision Tasks (TypeSafe internal) | Frontier-level intelligence on classification/routing tasks | Vendor-reported on security alert triage, support escalation, invoice validation, vendor decision tasks. Independent testing limited to accuracy sampling. |
Source: TypeSafe AI Launch Materials
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