Fast, Cheap Agent Decisions

Jev has only been available to the public for about a week (unveiled September 15th, 2026), but it’s already created a ton of excitement and discussion within the AI community. TypeSafe made Jev available through limited, early access at the same time they disclosed their $40 million seed round. Let’s strip away the hype and talk about what Jev is, and what it isn’t. When our agentic code submits a prompt to an LLM, it can include a description of available tools, context, and conversation history. The typical pattern is to stream text into a model and we get the answer streamed back out. If we want more structured replies, we can give these LLMs a schema and tell them that their output must conform to that schema. This structured output can be anything from data queried from a customer’s account to a set of product recommendations or an itinerary for a vacation based on weather forecasts and traveler preferences. Sometimes this output schema is extremely focused, like asking specific questions. Is there PII exposed anywhere in this text? Is the sentiment in the text negative or positive? Does the supplied context refer to a tech support issue or is it an account query? Narrowing down the potential output of an LLM to these extremely focused questions dramatically increases accuracy and reduces hallucinations. However, using a general-purpose LLM to answer these questions is extremely inefficient. While you could technically use a jumbo jet at runway taxi speed to commute from home to the office, it’s slow, inefficient, and costly. Overkill has a cost and many of us are using all-purpose LLMs when we could be using something more focused and efficient. This is where Jev comes in. It’s a special kind of model with its own interface style. It doesn’t stream text bi-directionally like LLMs. Instead, you ask it questions based on some state and it gives you answers and their probability scores. Performance and cost figures TypeSafe published for Jev TypeSafe published these figures with the Jev launch on September 15, 2026. All of them come from TypeSafe’s own evaluations and demos. Claim Jev Comparison, as TypeSafe states it End-to-end response time 70 ms to 500 ms 3 s to 329 s for frontier models Speed on System One queries 40x to 200x faster The same level of frontier intelligence Workflow evaluation, time per task 0.114 s 8.566 s for LLMs, 193.6x slower Workflow evaluation, cost per task $0.000081 $0.013880 for LLMs, 444.6x more expensive Accuracy, average of four workflows 67.8% at $0.0004 per workflow GPT-5.6 Terra 67.9% at $0.0304; Claude Sonnet 5 67.8% at $0.1174 Input token price $0.042 per million ($42 per billion) $0.20 to $10 per million Input price against Claude Fable 5.1 238x lower $10 per million Output token price Free About 5x the input price Hallucinations and type errors Zero, guaranteed by the output schema LLMs hallucinate and make type errors Confidence A calibrated probability with every answer Overconfident and inconsistent, even when prompted for confidence Doom demo 10 queries per second for about $7 per hour None given TypeSafe states that the 193.6x and 444.6x figures are on the higher end of real-world gains. The zero-hallucination figure follows from the output schema and was not measured. The LLM error rates TypeSafe compares against come from OpenRouter traffic. Sources: TypeSafe launch post, typesafe.ai, evals.typesafe.ai.

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