For the past few years, the tech world has been obsessed with AI that can talk to us. We have all seen Large Language Models (LLMs) write emails, summarize reports and debug code.
But as businesses try to put AI into their actual software pipelines, a hard reality is setting in: conversational AI is often too slow and too unpredictable for automated decision-making.
Enter a new wave of "System One" AI: JEV and CLMs (Contrastive Language Models). If you are a founder, developer or technology leader, understanding the difference between these three architectures matters for the software you build next. Here is a plain breakdown.
1. LLM (Large Language Model): The "System Two" Talker
Think of an LLM, such as ChatGPT or Claude, as a person carefully writing an essay.
- How it works: It generates answers sequentially, predicting one word at a time from the words before it.
- The problem: Because it works word by word, it is slow and computationally expensive. If your software only needs a "Yes" or "No" (for example, "Is this transaction fraudulent?"), an LLM may reply "Based on the data provided, the answer is Yes." Software cannot read that directly. It needs the data.
- Best for: Chatbots, creative writing, document summarization and brainstorming.
2. JEV: The "System One" Decision Maker
JEV, recently introduced by TypeSafe AI, is a shift in approach. It does not generate text. It generates decisions.
- How it works: JEV behaves like human System One thinking: fast, intuitive, snap judgments. Instead of generating language, it looks at a situation and evaluates several questions in a single parallel pass. It returns clean, structured data, such as JSON, with a confidence score attached to each answer.
- The impact: It is very fast. JEV can return complex routing decisions in milliseconds, reported at up to 200x faster and 400x cheaper than traditional LLMs.
- Best for: Content moderation, routing customer support tickets, classifying data and dependable software automation.
3. CLM (Contrastive Language Model): The Action Taker
CLMs, developed by researchers at Stanford and NVIDIA, take the speed of System One models and apply it to AI agents.
- How it works: Like JEV, a CLM generates no text. It uses contrastive learning. It looks at a computer screen (the "state") and a list of things it can do (the "actions", such as clicking a button or scrolling), then uses a mathematical space to match the right action to the current state.
- The impact: Because it does not have to generate words, it is very efficient. CLMs are reported to match the accuracy of larger models on agentic tasks while running up to 9x faster than even JEV.
- Best for: Autonomous AI agents, coding assistants, robotics and tools that work with computer interfaces in real time.
The Bottom Line
We are moving from an era of AI generation to an era of AI action.
- If you need to generate human-like text, use an LLM.
- If your software needs to categorize thousands of data points instantly, use JEV.
- If you are building an AI agent that navigates software and clicks buttons on its own, use a CLM.
The speed and cost figures above are those reported by the developers of these models. Results depend on your task, data and hardware, so test on your own cases before committing.
Frequently Asked Questions
What is the difference between an LLM, JEV and a CLM?
An LLM generates text one word at a time and suits chat, writing and summarization. JEV returns structured decisions with confidence scores in a single pass and suits classification and routing. A CLM generates no text and matches the current state to the best available action, which suits autonomous agents.
When should I not use an LLM?
When your software needs a label, a route or a next action rather than prose. In those cases a decision or action model can be faster and cheaper, and its structured output needs no parsing.
What does JEV output?
Structured data, such as JSON, with a confidence score for each answer, produced by evaluating several questions in one parallel pass.
What is a Contrastive Language Model (CLM)?
A model from researchers at Stanford and NVIDIA that uses contrastive learning to match a state, such as a screen, to the correct action from a list of possible actions, without generating text.
Can Liberin help choose between these models?
Yes. We audit which tasks need generation, decisions or actions, benchmark the options on your data and integrate the result into your systems. See our Decision and Action Models service.
