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Jev

jev-latestllms.txt
TypeSafe
Jev is a structured decision model launched by TypeSafe and is also their first "System One" model. System One models can make structured decisions for software quickly, returning typed selection results rather than freely generated text. They are suitable for routing, classification, and other decision nodes in applications—where fast, predictable answers are more important than generating natural language content.

Pricing

  • Input Tokens: $0.0462 /M tokens
  • Output Tokens: $0 /M tokens

Invoermodaliteiten

  • Text
  • Vision

Context length

  • 64K tokens

Capabilities

  • Thinking
  • Streaming
  • Tool calling
  • Web search
  • URL context
  • Code interpreter
  • Computer use
  • File search
  • Memory tool
  • Structured outputs
  • Structured decision
  • Citations
  • Prompt caching
  • Background mode
  • Server-side sessions

Aanbieders

TypeSafe jev-latest
Pricing$0.0462$0
PrecisionⓘNot Disclosed
Latency-
Throughput-
Uptime
100.00% uptime 2 days ago
100.00% uptime yesterday
100.00% uptime today

Performance for jev-latest

Uptime is het percentage geslaagde verzoeken in de afgelopen 72 uur. AIHubMix monitort elke provider continu en probeert automatisch de op één na beste provider wanneer een provider een fout retourneert of te traag reageert; Latency is de totale retourtijd van een verzoek (lager is beter); Throughput is hoe snel het model schrijft (tokens per seconde; hoger is beter).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
import requests

# Structured decision: POST /v1/systemone — every question is evaluated in parallel and in
# isolation against the same state, and comes back typed. No text generation, nothing to parse.
# The answers{} map is keyed by your own question names; each answer holds its value under a
# key named after its type:
#   noul   -> { type, noul }                                      probability of "yes", 0..1
#   choice -> { type, choice, probabilities, confidence }          choice is one of your criteria keys
#   score  -> { type, score, legend, probabilities, confidence }   legend maps level index -> description
# usage carries input_tokens / output_tokens.
url = "https://aihubmix.com/v1/systemone"
headers = {
    "Authorization": "Bearer " + os.environ["AIHUBMIX_API_KEY"],
    "Content-Type": "application/json",
}
payload = {
      "model": "jev-latest",
      "state": "Hi, I have been trying to connect my Stripe account for 3 days and it keeps failing. I am losing sales. Please help ASAP.",
      "questions": {
        "department": {
          "type": "choice",
          "instructions": "Which team should handle this",
          "criteria": {
            "billing": "Payment or subscription issues",
            "technical": "Bugs or integration problems",
            "sales": "Pricing or account questions"
          }
        },
        "frustration": {
          "type": "score",
          "instructions": "How frustrated the customer appears",
          "criteria": [
            "Calm, just stating facts",
            "Frustrated but civil",
            "Very angry, strong language"
          ]
        },
        "is_urgent": {
          "type": "noul",
          "instructions": "The message conveys urgency or time-sensitivity"
        }
      }
    }

response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()

data = response.json()
for name, answer in data["answers"].items():
    kind = answer["type"]
    print(name, kind, answer[kind], answer.get("confidence"))
print("usage:", data.get("usage"))

Frequently asked questions

What is Jev?

Jev is a structured decision model launched by TypeSafe and is also their first "System One" model. System One models can make structured decisions for software quickly, returning typed selection results rather than freely generated text. They are suitable for routing, classification, and other decision nodes in applications—where fast, predictable answers are more important than generating natural language content.