{"content_id":"emjtnwrqpz","slug":"jev-system-one-model-llm-comparison","locale":"en","schema_type":"TechArticle","category":"knowledge_base","category_name":"Knowledge Base","title":"Jev System Model vs LLM for Developers","summary":"Jev is a model for structured decision-making developed by TypeSafe AI. It connects choices and probabilities to code, and when adopting it in practice, its output format must be distinguished from its decision accuracy.","sponsorship_disclosure":null,"affiliate_disclosure":null,"commerce_disclosure":null,"author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["Jev returns predefined answers and probabilities instead of free-form prose.","Choice and Score include confidence, but Noul has no separate confidence field.","The input price announced on September 15, 2026, is $0.042 per million tokens.","The published speed comparison reflects results from a specific task and test environment.","For Korean-language work, use real cases to validate accuracy and the criteria for switching to review."],"content_markdown":"Jev is a model developed by TypeSafe for software decision-making. It returns selected values and probabilities instead of explanatory text. It is designed for classification and task routing. Even when the output format is correct, the decision may be wrong.\n\nPricing and speed figures are based on TypeSafe's announcement on September 15, 2026.\n\n## What Jev and System One Model Mean\n\nJev is an AI that produces decision results that code can use directly. Its official product name is Jev. TypeSafe calls this family System One Model.\n\nThe input contains the `state`, which is the subject of the decision, and `questions`. The `state` contains the information needed for the decision, such as a customer inquiry or work record. Questions specify the allowed answers and evaluation criteria.\n\nThe name System One comes from fast, intuitive thinking. It is the company's term for describing the product's role. It can also understand natural language input. Its concepts and input scope are available in [TypeSafe's System One documentation](https://docs.typesafe.ai/concepts/system-one).\n\n## How Is It Different from an LLM?\n\nThe key difference is the range of answers it can output. An LLM generates unrestricted content such as explanatory text and code. Jev makes decisions within the range of answers defined by the developer.\n\n| Comparison | Typical LLM use | Jev use |\n| --- | --- | --- |\n| Main result | Explanations, summaries, generated code | Selected values, evaluation scores, probabilities |\n| Range of answers | Free-form generation or structured output | Range predefined in the question |\n| Software integration | Interpret and validate based on the response format | Connect return values to conditions and classifications |\n| Explanation generation | Can write reasons for a decision as sentences | Does not support free-form explanatory text generation |\n| Suitable role | Conversation, writing, solving complex problems | Classification, routing selection, evaluation by criteria |\n\nAn LLM can also be configured to answer in a structured format. JSON output itself is therefore not unique to Jev. Vercel's documentation also explains how it connects to structured LLMs. However, the same response format does not mean the same evaluation behavior. This distinction is explained in [Vercel's overview of Jev](https://vercel.com/i/what-is-jev).\n\nIn software, the output must follow the required specification before the next task can run. For example, a department field needs an allowed department name. If explanatory text is mixed in, a separate interpretation step is required. Jev is designed to return these decision results directly.\n\n## Choice·Score·Noul Guide by Condition\n\nChoose a type based on the form of the answer to the question. Jev's basic question types are Choice, Score, and Noul. Independent questions about the same state can be grouped into one call. The configuration method is described in the [TypeSafe Introduction](https://docs.typesafe.ai/introduction).\n\n| Decision needed | Question type | Returned information | Example use |\n| --- | --- | --- | --- |\n| Select from predefined options | Choice | Selected value, probability for each option, confidence | Route an inquiry to the responsible department |\n| Evaluate against ordered criteria | Score | Score, probability for each level, confidence | Evaluate incident severity |\n| Determine whether a proposition is true | Noul | A 0-1 value representing the probability of yes | Determine whether a refund request is included |\n\nA Score number indicates a position within the configured evaluation levels. It does not always mean a percentage. Noul is a type that returns the probability of yes. The [Score documentation](https://docs.typesafe.ai/primitives/score) and [Noul documentation](https://docs.typesafe.ai/primitives/noul) provide the interpretation criteria for each type.\n\nSet the processing order for questions that depend on one another. If a later question needs an earlier answer, you must connect them in code. Questions within the same call independently evaluate the shared state.\n\n## How Should the Numbers in the Restaurant Service Example Be Read?\n\nThe numbers in the restaurant service demonstration should not be viewed as the accuracy of the product as a whole. The following values appear in the introduction video's description. The original API request and measurement conditions could not be verified.\n\n| Presented value | Meaning in the description | What to check when interpreting it |\n| --- | --- | --- |\n| 0.09 on a 0-1 scale | Evaluation of whether the customer's behavior was justified | Check whether it is a score or the probability of yes |\n| Option suitability of 94% | Value assigned to an approach explaining the reason for payment | Check the presented options and question criteria |\n| Confidence of 92% | Confidence indicator for the decision | Do not interpret it as the actual accuracy rate |\n| Processing time of less than 0.1 seconds | Response time presented in the demonstration | Check whether network time was included |\n\nThis example illustrates the output format. The numbers alone cannot resolve a real-world dispute. In particular, changing the available options changes the question itself.\n\n## Common Mistakes When Confusing Probability and Confidence\n\nProbability and confidence are different values. Choice probabilities are the values assigned to each option. Confidence summarizes how concentrated that probability distribution is.\n\nTherefore, confidence of 92% should not be interpreted as an accuracy rate of 92%. Actual accuracy must be measured using cases with verified correct answers. Noul does not have a separate `confidence` field. This distinction is stated in the [TypeSafe Confidence documentation](https://docs.typesafe.ai/confidence).\n\n- **Confusing format with accuracy:** It may select an allowed department but still choose the wrong one.\n- **Confusing score with probability:** A Score value may represent a position within the evaluation levels.\n- **Confusing confidence with accuracy:** High confidence alone does not guarantee that an individual answer is correct.\n- **Missing options:** If the required answer is not among the options, an appropriate decision becomes difficult.\n\nCalibration is the concept of collecting multiple predictions and evaluating the relationship between probabilities and outcomes. It is not a process that guarantees the correctness of a single answer. Evaluate compliance with the output specification separately from semantic accuracy.\n\n## Conditions for Comparing Public Pricing and Speed\n\nPublic pricing is calculated based on input tokens. A token is a unit the model uses to process text. The September 15, 2026 announcement stated that output tokens were free.\n\n| Item | Public information | Scope |\n| --- | --- | --- |\n| Input price | $0.042 per 1 million tokens | TypeSafe pricing at the time of the announcement |\n| Output price | Free | TypeSafe pricing at the time of the announcement |\n| Response time | 70-500 milliseconds | Measurement range announced by the company |\n| Measurement environment | Primarily laptops in the western United States | Evaluation environment stated in the company announcement |\n\nThese figures do not guarantee performance for every request. The company's evaluation includes comparisons that use predictions from external models as a reference. This cannot be treated as equivalent to accuracy in real work. The measurement conditions are published in the [TypeSafe launch announcement](https://typesafe.ai/blog/introducing-system-one-models-and-jev).\n\n### Example Calculation Using the Public Rate\n\nThe base usage fee for 1 billion input tokens is $42. The formula is `1,000,000,000 ÷ 1,000,000 × 0.042`. This is an example conversion based on the public rate.\n\nThe price of each decision varies with the input length. Retries and subsequent model calls also affect the actual cost. Check the [TypeSafe Models documentation](https://docs.typesafe.ai/models) for the latest rates.\n\n## The Range of Uses Demonstrated by Doom\n\nThe Doom demonstration is an example of selecting actions from structured game state. It is not a demonstration of directly understanding game screen images. TypeSafe explains that it used state data in text form. This condition is described in the [Doom section of the launch announcement](https://typesafe.ai/blog/introducing-system-one-models-and-jev).\n\nWhen applying it to work, points that require repeated decisions are potential candidates. The following table provides design examples based on its features. Performance in each task must be validated separately.\n\n| Work condition | Possible role | Additional processing needed |\n| --- | --- | --- |\n| Classify inquiries into predefined departments | Select the responsible route with Choice | Review route for ambiguous inquiries |\n| Evaluate documents against common criteria | Return scores for each criterion with Score | Clear definition of evaluation criteria |\n| Check whether a specific request is included | Evaluate a proposition with Noul | Code that determines whether to take action |\n| Write explanatory text for users | Connect to a generative model | Pass along source evidence and approved decisions |\n\n## Limitations in Korean-Language Work and Number Processing\n\nKorean-language work requires separate validation. TypeSafe states that English is the primary training language. It explains that performance is not the same in other languages. Language support conditions are available in the [Models documentation](https://docs.typesafe.ai/models).\n\nReturning numbers does not mean that calculations are accurate. This is stated in the limitations documentation reviewed on September 17, 2026. The documentation recommends handling calculations in code.\n\n\u003e Jev is not a calculator.\n\nThis statement comes directly from [Jev 1.13 jaggedness](https://docs.typesafe.ai/model-jaggedness/jev-1.13).\n\n| Known limitation | Development response |\n| --- | --- |\n| Exact quantity calculations | Count items in code |\n| Date and time comparisons | Convert extracted values to date types before comparing them |\n| Long, irrelevant input | Select only the content needed for the decision |\n| Complex negative sentences and indirect conditions | Split them into clear, short questions |\n| Ambiguous or conflicting evaluation criteria | Align the meanings of questions and options |\n\nInclude actual workplace expressions in Korean validation data. Honorifics and omitted subjects should also be tested. You can compare the model's answers against cases with confirmed outcomes.\n\n## Comparing API and SDK Access Routes\n\nGateway routes have also been confirmed in addition to direct access through TypeSafe. The launch announcement described initial access through a waitlist. Check each service to confirm whether it is available for your account.\n\n| Route | Confirmed offering | Documentation to check |\n| --- | --- | --- |\n| TypeSafe | Console, API, client SDK | Quick start |\n| Vercel AI Gateway | Connection for Jev evaluation requests | Evaluation |\n| OpenRouter | Jev 1.13 model listing | Jev 1.13 model page |\n\nTypeSafe's [Quick start](https://docs.typesafe.ai/introduction/quickstart) explains the direct call procedure. The basic steps for this route are as follows.\n\n1. Check your account's access permissions in the console.\n2. Test the decision subject and questions in the Playground.\n3. Generate an API key in the dashboard.\n4. Send a request through the SDK or HTTP API.\n5. Connect the return values and probabilities to your code's processing rules.\n\nVercel's [Evaluation documentation](https://vercel.com/docs/ai-gateway/modalities/evaluation) includes integration examples. Yes or no questions are represented as Boolean through this route. The model is also available on [OpenRouter's Jev 1.13 page](https://openrouter.ai/typesafe/jev-1.13/). Refer to the documentation for each route for its request format and limits.\n\n## Design Criteria for Keeping Decision Records\n\nTo validate automation results, record the supporting input as well. If you save only the selected value, it is difficult to identify the cause of a wrong decision. The following items are design recommendations based on the official interface.\n\n| Recorded item | Problem it can reveal |\n| --- | --- |\n| Original text used for the decision and its timestamp | Outdated or missing information |\n| Version of the questions and options | Differences in results caused by changes in criteria |\n| Actual responding model ID | Effects of model changes |\n| Answer and probability distribution | Reason for handling ambiguous cases |\n| Action executed by the code | Mismatch between the decision and execution |\n| Human-verified result | Actual errors and correction history |\n\nA model name alias may change to point to a new version. A validated version can be fixed using its model ID. This operating method is described in TypeSafe's [Models documentation](https://docs.typesafe.ai/models).\n\nSet review escalation criteria using actual cases. Evaluate both the cost of misclassification and the burden of human review. There is no universally correct confidence threshold. For validation methods, refer to the guidance in the [Confidence documentation](https://docs.typesafe.ai/confidence).","content_html":"\u003cp\u003eJev is a model developed by TypeSafe for software decision-making. It returns selected values and probabilities instead of explanatory text. It is designed for classification and task routing. Even when the output format is correct, the decision may be wrong.\u003c/p\u003e\n\u003cp\u003ePricing and speed figures are based on TypeSafe's announcement on September 15, 2026.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-jev-and-system-one-model-mean\" class=\"anchor\" id=\"what-jev-and-system-one-model-mean\"\u003e\u003c/a\u003eWhat Jev and System One Model Mean\u003c/h2\u003e\n\u003cp\u003eJev is an AI that produces decision results that code can use directly. Its official product name is Jev. TypeSafe calls this family System One Model.\u003c/p\u003e\n\u003cp\u003eThe input contains the \u003ccode\u003estate\u003c/code\u003e, which is the subject of the decision, and \u003ccode\u003equestions\u003c/code\u003e. The \u003ccode\u003estate\u003c/code\u003e contains the information needed for the decision, such as a customer inquiry or work record. Questions specify the allowed answers and evaluation criteria.\u003c/p\u003e\n\u003cp\u003eThe name System One comes from fast, intuitive thinking. It is the company's term for describing the product's role. It can also understand natural language input. Its concepts and input scope are available in \u003ca href=\"https://docs.typesafe.ai/concepts/system-one\"\u003eTypeSafe's System One documentation\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-is-it-different-from-an-llm\" class=\"anchor\" id=\"how-is-it-different-from-an-llm\"\u003e\u003c/a\u003eHow Is It Different from an LLM?\u003c/h2\u003e\n\u003cp\u003eThe key difference is the range of answers it can output. An LLM generates unrestricted content such as explanatory text and code. Jev makes decisions within the range of answers defined by the developer.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComparison\u003c/th\u003e\n\u003cth\u003eTypical LLM use\u003c/th\u003e\n\u003cth\u003eJev use\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison\"\u003eMain result\u003c/td\u003e\n\u003ctd data-label=\"Typical LLM use\"\u003eExplanations, summaries, generated code\u003c/td\u003e\n\u003ctd data-label=\"Jev use\"\u003eSelected values, evaluation scores, probabilities\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison\"\u003eRange of answers\u003c/td\u003e\n\u003ctd data-label=\"Typical LLM use\"\u003eFree-form generation or structured output\u003c/td\u003e\n\u003ctd data-label=\"Jev use\"\u003eRange predefined in the question\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison\"\u003eSoftware integration\u003c/td\u003e\n\u003ctd data-label=\"Typical LLM use\"\u003eInterpret and validate based on the response format\u003c/td\u003e\n\u003ctd data-label=\"Jev use\"\u003eConnect return values to conditions and classifications\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison\"\u003eExplanation generation\u003c/td\u003e\n\u003ctd data-label=\"Typical LLM use\"\u003eCan write reasons for a decision as sentences\u003c/td\u003e\n\u003ctd data-label=\"Jev use\"\u003eDoes not support free-form explanatory text generation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Comparison\"\u003eSuitable role\u003c/td\u003e\n\u003ctd data-label=\"Typical LLM use\"\u003eConversation, writing, solving complex problems\u003c/td\u003e\n\u003ctd data-label=\"Jev use\"\u003eClassification, routing selection, evaluation by criteria\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eAn LLM can also be configured to answer in a structured format. JSON output itself is therefore not unique to Jev. Vercel's documentation also explains how it connects to structured LLMs. However, the same response format does not mean the same evaluation behavior. This distinction is explained in \u003ca href=\"https://vercel.com/i/what-is-jev\"\u003eVercel's overview of Jev\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn software, the output must follow the required specification before the next task can run. For example, a department field needs an allowed department name. If explanatory text is mixed in, a separate interpretation step is required. Jev is designed to return these decision results directly.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#choicescorenoul-guide-by-condition\" class=\"anchor\" id=\"choicescorenoul-guide-by-condition\"\u003e\u003c/a\u003eChoice·Score·Noul Guide by Condition\u003c/h2\u003e\n\u003cp\u003eChoose a type based on the form of the answer to the question. Jev's basic question types are Choice, Score, and Noul. Independent questions about the same state can be grouped into one call. The configuration method is described in the \u003ca href=\"https://docs.typesafe.ai/introduction\"\u003eTypeSafe Introduction\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDecision needed\u003c/th\u003e\n\u003cth\u003eQuestion type\u003c/th\u003e\n\u003cth\u003eReturned information\u003c/th\u003e\n\u003cth\u003eExample use\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision needed\"\u003eSelect from predefined options\u003c/td\u003e\n\u003ctd data-label=\"Question type\"\u003eChoice\u003c/td\u003e\n\u003ctd data-label=\"Returned information\"\u003eSelected value, probability for each option, confidence\u003c/td\u003e\n\u003ctd data-label=\"Example use\"\u003eRoute an inquiry to the responsible department\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision needed\"\u003eEvaluate against ordered criteria\u003c/td\u003e\n\u003ctd data-label=\"Question type\"\u003eScore\u003c/td\u003e\n\u003ctd data-label=\"Returned information\"\u003eScore, probability for each level, confidence\u003c/td\u003e\n\u003ctd data-label=\"Example use\"\u003eEvaluate incident severity\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Decision needed\"\u003eDetermine whether a proposition is true\u003c/td\u003e\n\u003ctd data-label=\"Question type\"\u003eNoul\u003c/td\u003e\n\u003ctd data-label=\"Returned information\"\u003eA 0-1 value representing the probability of yes\u003c/td\u003e\n\u003ctd data-label=\"Example use\"\u003eDetermine whether a refund request is included\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eA Score number indicates a position within the configured evaluation levels. It does not always mean a percentage. Noul is a type that returns the probability of yes. The \u003ca href=\"https://docs.typesafe.ai/primitives/score\"\u003eScore documentation\u003c/a\u003e and \u003ca href=\"https://docs.typesafe.ai/primitives/noul\"\u003eNoul documentation\u003c/a\u003e provide the interpretation criteria for each type.\u003c/p\u003e\n\u003cp\u003eSet the processing order for questions that depend on one another. If a later question needs an earlier answer, you must connect them in code. Questions within the same call independently evaluate the shared state.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-should-the-numbers-in-the-restaurant-service-example-be-read\" class=\"anchor\" id=\"how-should-the-numbers-in-the-restaurant-service-example-be-read\"\u003e\u003c/a\u003eHow Should the Numbers in the Restaurant Service Example Be Read?\u003c/h2\u003e\n\u003cp\u003eThe numbers in the restaurant service demonstration should not be viewed as the accuracy of the product as a whole. The following values appear in the introduction video's description. The original API request and measurement conditions could not be verified.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePresented value\u003c/th\u003e\n\u003cth\u003eMeaning in the description\u003c/th\u003e\n\u003cth\u003eWhat to check when interpreting it\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Presented value\"\u003e0.09 on a 0-1 scale\u003c/td\u003e\n\u003ctd data-label=\"Meaning in the description\"\u003eEvaluation of whether the customer's behavior was justified\u003c/td\u003e\n\u003ctd data-label=\"What to check when interpreting it\"\u003eCheck whether it is a score or the probability of yes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Presented value\"\u003eOption suitability of 94%\u003c/td\u003e\n\u003ctd data-label=\"Meaning in the description\"\u003eValue assigned to an approach explaining the reason for payment\u003c/td\u003e\n\u003ctd data-label=\"What to check when interpreting it\"\u003eCheck the presented options and question criteria\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Presented value\"\u003eConfidence of 92%\u003c/td\u003e\n\u003ctd data-label=\"Meaning in the description\"\u003eConfidence indicator for the decision\u003c/td\u003e\n\u003ctd data-label=\"What to check when interpreting it\"\u003eDo not interpret it as the actual accuracy rate\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Presented value\"\u003eProcessing time of less than 0.1 seconds\u003c/td\u003e\n\u003ctd data-label=\"Meaning in the description\"\u003eResponse time presented in the demonstration\u003c/td\u003e\n\u003ctd data-label=\"What to check when interpreting it\"\u003eCheck whether network time was included\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThis example illustrates the output format. The numbers alone cannot resolve a real-world dispute. In particular, changing the available options changes the question itself.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#common-mistakes-when-confusing-probability-and-confidence\" class=\"anchor\" id=\"common-mistakes-when-confusing-probability-and-confidence\"\u003e\u003c/a\u003eCommon Mistakes When Confusing Probability and Confidence\u003c/h2\u003e\n\u003cp\u003eProbability and confidence are different values. Choice probabilities are the values assigned to each option. Confidence summarizes how concentrated that probability distribution is.\u003c/p\u003e\n\u003cp\u003eTherefore, confidence of 92% should not be interpreted as an accuracy rate of 92%. Actual accuracy must be measured using cases with verified correct answers. Noul does not have a separate \u003ccode\u003econfidence\u003c/code\u003e field. This distinction is stated in the \u003ca href=\"https://docs.typesafe.ai/confidence\"\u003eTypeSafe Confidence documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eConfusing format with accuracy:\u003c/strong\u003e It may select an allowed department but still choose the wrong one.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConfusing score with probability:\u003c/strong\u003e A Score value may represent a position within the evaluation levels.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConfusing confidence with accuracy:\u003c/strong\u003e High confidence alone does not guarantee that an individual answer is correct.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMissing options:\u003c/strong\u003e If the required answer is not among the options, an appropriate decision becomes difficult.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCalibration is the concept of collecting multiple predictions and evaluating the relationship between probabilities and outcomes. It is not a process that guarantees the correctness of a single answer. Evaluate compliance with the output specification separately from semantic accuracy.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#conditions-for-comparing-public-pricing-and-speed\" class=\"anchor\" id=\"conditions-for-comparing-public-pricing-and-speed\"\u003e\u003c/a\u003eConditions for Comparing Public Pricing and Speed\u003c/h2\u003e\n\u003cp\u003ePublic pricing is calculated based on input tokens. A token is a unit the model uses to process text. The September 15, 2026 announcement stated that output tokens were free.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eItem\u003c/th\u003e\n\u003cth\u003ePublic information\u003c/th\u003e\n\u003cth\u003eScope\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eInput price\u003c/td\u003e\n\u003ctd data-label=\"Public information\"\u003e$0.042 per 1 million tokens\u003c/td\u003e\n\u003ctd data-label=\"Scope\"\u003eTypeSafe pricing at the time of the announcement\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eOutput price\u003c/td\u003e\n\u003ctd data-label=\"Public information\"\u003eFree\u003c/td\u003e\n\u003ctd data-label=\"Scope\"\u003eTypeSafe pricing at the time of the announcement\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eResponse time\u003c/td\u003e\n\u003ctd data-label=\"Public information\"\u003e70-500 milliseconds\u003c/td\u003e\n\u003ctd data-label=\"Scope\"\u003eMeasurement range announced by the company\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eMeasurement environment\u003c/td\u003e\n\u003ctd data-label=\"Public information\"\u003ePrimarily laptops in the western United States\u003c/td\u003e\n\u003ctd data-label=\"Scope\"\u003eEvaluation environment stated in the company announcement\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThese figures do not guarantee performance for every request. The company's evaluation includes comparisons that use predictions from external models as a reference. This cannot be treated as equivalent to accuracy in real work. The measurement conditions are published in the \u003ca href=\"https://typesafe.ai/blog/introducing-system-one-models-and-jev\"\u003eTypeSafe launch announcement\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#example-calculation-using-the-public-rate\" class=\"anchor\" id=\"example-calculation-using-the-public-rate\"\u003e\u003c/a\u003eExample Calculation Using the Public Rate\u003c/h3\u003e\n\u003cp\u003eThe base usage fee for 1 billion input tokens is $42. The formula is \u003ccode\u003e1,000,000,000 ÷ 1,000,000 × 0.042\u003c/code\u003e. This is an example conversion based on the public rate.\u003c/p\u003e\n\u003cp\u003eThe price of each decision varies with the input length. Retries and subsequent model calls also affect the actual cost. Check the \u003ca href=\"https://docs.typesafe.ai/models\"\u003eTypeSafe Models documentation\u003c/a\u003e for the latest rates.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#the-range-of-uses-demonstrated-by-doom\" class=\"anchor\" id=\"the-range-of-uses-demonstrated-by-doom\"\u003e\u003c/a\u003eThe Range of Uses Demonstrated by Doom\u003c/h2\u003e\n\u003cp\u003eThe Doom demonstration is an example of selecting actions from structured game state. It is not a demonstration of directly understanding game screen images. TypeSafe explains that it used state data in text form. This condition is described in the \u003ca href=\"https://typesafe.ai/blog/introducing-system-one-models-and-jev\"\u003eDoom section of the launch announcement\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eWhen applying it to work, points that require repeated decisions are potential candidates. The following table provides design examples based on its features. Performance in each task must be validated separately.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eWork condition\u003c/th\u003e\n\u003cth\u003ePossible role\u003c/th\u003e\n\u003cth\u003eAdditional processing needed\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Work condition\"\u003eClassify inquiries into predefined departments\u003c/td\u003e\n\u003ctd data-label=\"Possible role\"\u003eSelect the responsible route with Choice\u003c/td\u003e\n\u003ctd data-label=\"Additional processing needed\"\u003eReview route for ambiguous inquiries\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Work condition\"\u003eEvaluate documents against common criteria\u003c/td\u003e\n\u003ctd data-label=\"Possible role\"\u003eReturn scores for each criterion with Score\u003c/td\u003e\n\u003ctd data-label=\"Additional processing needed\"\u003eClear definition of evaluation criteria\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Work condition\"\u003eCheck whether a specific request is included\u003c/td\u003e\n\u003ctd data-label=\"Possible role\"\u003eEvaluate a proposition with Noul\u003c/td\u003e\n\u003ctd data-label=\"Additional processing needed\"\u003eCode that determines whether to take action\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Work condition\"\u003eWrite explanatory text for users\u003c/td\u003e\n\u003ctd data-label=\"Possible role\"\u003eConnect to a generative model\u003c/td\u003e\n\u003ctd data-label=\"Additional processing needed\"\u003ePass along source evidence and approved decisions\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca href=\"#limitations-in-korean-language-work-and-number-processing\" class=\"anchor\" id=\"limitations-in-korean-language-work-and-number-processing\"\u003e\u003c/a\u003eLimitations in Korean-Language Work and Number Processing\u003c/h2\u003e\n\u003cp\u003eKorean-language work requires separate validation. TypeSafe states that English is the primary training language. It explains that performance is not the same in other languages. Language support conditions are available in the \u003ca href=\"https://docs.typesafe.ai/models\"\u003eModels documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eReturning numbers does not mean that calculations are accurate. This is stated in the limitations documentation reviewed on September 17, 2026. The documentation recommends handling calculations in code.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eJev is not a calculator.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp\u003eThis statement comes directly from \u003ca href=\"https://docs.typesafe.ai/model-jaggedness/jev-1.13\"\u003eJev 1.13 jaggedness\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eKnown limitation\u003c/th\u003e\n\u003cth\u003eDevelopment response\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Known limitation\"\u003eExact quantity calculations\u003c/td\u003e\n\u003ctd data-label=\"Development response\"\u003eCount items in code\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Known limitation\"\u003eDate and time comparisons\u003c/td\u003e\n\u003ctd data-label=\"Development response\"\u003eConvert extracted values to date types before comparing them\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Known limitation\"\u003eLong, irrelevant input\u003c/td\u003e\n\u003ctd data-label=\"Development response\"\u003eSelect only the content needed for the decision\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Known limitation\"\u003eComplex negative sentences and indirect conditions\u003c/td\u003e\n\u003ctd data-label=\"Development response\"\u003eSplit them into clear, short questions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Known limitation\"\u003eAmbiguous or conflicting evaluation criteria\u003c/td\u003e\n\u003ctd data-label=\"Development response\"\u003eAlign the meanings of questions and options\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eInclude actual workplace expressions in Korean validation data. Honorifics and omitted subjects should also be tested. You can compare the model's answers against cases with confirmed outcomes.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#comparing-api-and-sdk-access-routes\" class=\"anchor\" id=\"comparing-api-and-sdk-access-routes\"\u003e\u003c/a\u003eComparing API and SDK Access Routes\u003c/h2\u003e\n\u003cp\u003eGateway routes have also been confirmed in addition to direct access through TypeSafe. The launch announcement described initial access through a waitlist. Check each service to confirm whether it is available for your account.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eRoute\u003c/th\u003e\n\u003cth\u003eConfirmed offering\u003c/th\u003e\n\u003cth\u003eDocumentation to check\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Route\"\u003eTypeSafe\u003c/td\u003e\n\u003ctd data-label=\"Confirmed offering\"\u003eConsole, API, client SDK\u003c/td\u003e\n\u003ctd data-label=\"Documentation to check\"\u003eQuick start\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Route\"\u003eVercel AI Gateway\u003c/td\u003e\n\u003ctd data-label=\"Confirmed offering\"\u003eConnection for Jev evaluation requests\u003c/td\u003e\n\u003ctd data-label=\"Documentation to check\"\u003eEvaluation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Route\"\u003eOpenRouter\u003c/td\u003e\n\u003ctd data-label=\"Confirmed offering\"\u003eJev 1.13 model listing\u003c/td\u003e\n\u003ctd data-label=\"Documentation to check\"\u003eJev 1.13 model page\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eTypeSafe's \u003ca href=\"https://docs.typesafe.ai/introduction/quickstart\"\u003eQuick start\u003c/a\u003e explains the direct call procedure. The basic steps for this route are as follows.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eCheck your account's access permissions in the console.\u003c/li\u003e\n\u003cli\u003eTest the decision subject and questions in the Playground.\u003c/li\u003e\n\u003cli\u003eGenerate an API key in the dashboard.\u003c/li\u003e\n\u003cli\u003eSend a request through the SDK or HTTP API.\u003c/li\u003e\n\u003cli\u003eConnect the return values and probabilities to your code's processing rules.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eVercel's \u003ca href=\"https://vercel.com/docs/ai-gateway/modalities/evaluation\"\u003eEvaluation documentation\u003c/a\u003e includes integration examples. Yes or no questions are represented as Boolean through this route. The model is also available on \u003ca href=\"https://openrouter.ai/typesafe/jev-1.13/\"\u003eOpenRouter's Jev 1.13 page\u003c/a\u003e. Refer to the documentation for each route for its request format and limits.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#design-criteria-for-keeping-decision-records\" class=\"anchor\" id=\"design-criteria-for-keeping-decision-records\"\u003e\u003c/a\u003eDesign Criteria for Keeping Decision Records\u003c/h2\u003e\n\u003cp\u003eTo validate automation results, record the supporting input as well. If you save only the selected value, it is difficult to identify the cause of a wrong decision. The following items are design recommendations based on the official interface.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eRecorded item\u003c/th\u003e\n\u003cth\u003eProblem it can reveal\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Recorded item\"\u003eOriginal text used for the decision and its timestamp\u003c/td\u003e\n\u003ctd data-label=\"Problem it can reveal\"\u003eOutdated or missing information\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Recorded item\"\u003eVersion of the questions and options\u003c/td\u003e\n\u003ctd data-label=\"Problem it can reveal\"\u003eDifferences in results caused by changes in criteria\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Recorded item\"\u003eActual responding model ID\u003c/td\u003e\n\u003ctd data-label=\"Problem it can reveal\"\u003eEffects of model changes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Recorded item\"\u003eAnswer and probability distribution\u003c/td\u003e\n\u003ctd data-label=\"Problem it can reveal\"\u003eReason for handling ambiguous cases\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Recorded item\"\u003eAction executed by the code\u003c/td\u003e\n\u003ctd data-label=\"Problem it can reveal\"\u003eMismatch between the decision and execution\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Recorded item\"\u003eHuman-verified result\u003c/td\u003e\n\u003ctd data-label=\"Problem it can reveal\"\u003eActual errors and correction history\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eA model name alias may change to point to a new version. A validated version can be fixed using its model ID. This operating method is described in TypeSafe's \u003ca href=\"https://docs.typesafe.ai/models\"\u003eModels documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSet review escalation criteria using actual cases. Evaluate both the cost of misclassification and the burden of human review. There is no universally correct confidence threshold. For validation methods, refer to the guidance in the \u003ca href=\"https://docs.typesafe.ai/confidence\"\u003eConfidence documentation\u003c/a\u003e.\u003c/p\u003e\n","tags":["Generative AI","AI Agents","AI Development","Decision Making","Development Tools"],"faqs":[{"question":"What kind of AI is Jev?","answer":"It is a model developed by TypeSafe for making software decisions. It returns predefined answers and probabilities."},{"question":"What does System One Model mean?","answer":"It is TypeSafe's name for a fast decision-making model with a defined scope. The name comes from the concept of fast, intuitive thinking."},{"question":"Does Jev replace all conversational LLMs?","answer":"Jev does not generate free-form explanations. If conversation and writing are needed, it can be used together with a generative model."},{"question":"How is it different from an LLM's JSON output?","answer":"LLMs can also support structured output. Jev is a model designed to make decisions within a predefined set of answers. Even with the same response format, their evaluation behavior may differ."},{"question":"How do you distinguish between Choice and Score?","answer":"It is Choice when selecting one option from an unordered set. It is Score when evaluating an ordered scale, such as from low to high."},{"question":"Does a low Noul value mean low confidence?","answer":"Noul represents the probability of yes. A value close to 0 means the result was judged toward no. There is no separate confidence field."},{"question":"Does 92% confidence mean 92% accuracy?","answer":"It cannot be interpreted that way. Confidence is a summary of the probability distribution of the answers. Actual accuracy must be measured using cases where the correct answers are known."},{"question":"How much does Jev cost?","answer":"The pricing announced on September 15, 2026, was $0.042 per 1 million input tokens. At the time, output tokens were stated to be free. Check the TypeSafe Models documentation for the latest rates."},{"question":"Does it always respond within 0.1 seconds?","answer":"This has not been confirmed as a guarantee that applies to every request. The range stated in the company's launch announcement is 70-500 milliseconds. Measure actual latency with your input and connection environment."},{"question":"Did the Doom demo analyze the game screen directly?","answer":"TypeSafe explains that it used a text-based game state. It was not a demo that used the image itself as input."},{"question":"Can I enter Korean?","answer":"It can also process non-English input such as Korean. However, the official documentation states that accuracy is best in English. Validate it separately for Korean-language business use cases."},{"question":"Can it be used for numerical calculations and date comparisons?","answer":"The Jev 1.13 limitations document explains that it has weaknesses in precise calculations. Comparing the order of dates is also a known limitation. It recommends handling calculations and comparisons in code."},{"question":"Is it also available through Vercel and OpenRouter?","answer":"Vercel provides instructions on how to connect through AI Gateway's evaluation feature. OpenRouter also has a Jev 1.13 model page. Check the terms of use and request format for each route."},{"question":"Can the model change while in operation?","answer":"An alias that points to the latest version may move to a new model. You can pin the verified model ID. Also record the actual model ID included in the response."}],"sources":[{"url":"https://typesafe.ai/blog/introducing-system-one-models-and-jev","title":"TypeSafe AI, Introducing System One Models \u0026 Jev, September 15, 2026","type":"source"},{"url":"https://docs.typesafe.ai/concepts/system-one","title":"TypeSafe AI Documentation, System One","type":"source"},{"url":"https://docs.typesafe.ai/introduction","title":"TypeSafe AI Documentation, Introduction","type":"source"},{"url":"https://docs.typesafe.ai/primitives/score","title":"TypeSafe AI Documentation, Score","type":"source"},{"url":"https://docs.typesafe.ai/primitives/noul","title":"TypeSafe AI Documentation, Noul","type":"source"},{"url":"https://docs.typesafe.ai/confidence","title":"TypeSafe AI Documentation, Confidence","type":"source"},{"url":"https://docs.typesafe.ai/models","title":"TypeSafe AI Documentation, Models","type":"data_point"},{"url":"https://docs.typesafe.ai/model-jaggedness/jev-1.13","title":"TypeSafe AI Documentation, Jev 1.13 jaggedness, reviewed September 17, 2026","type":"source"},{"url":"https://docs.typesafe.ai/introduction/quickstart","title":"TypeSafe AI Documentation, Quick start","type":"source"},{"url":"https://vercel.com/i/what-is-jev","title":"Vercel, What is Jev, TypeSafe AI's System One model?, September 18, 2026","type":"source"},{"url":"https://vercel.com/docs/ai-gateway/modalities/evaluation","title":"Vercel AI Gateway Documentation, Evaluation","type":"source"},{"url":"https://openrouter.ai/typesafe/jev-1.13/","title":"OpenRouter, TypeSafe: Jev 1.13","type":"source"}],"images":[{"id":1451,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MjEwNTYsInB1ciI6ImJsb2JfaWQifX0=--e6b978aedcb8b8e82ef1491cad6918fb7c29680e/ai-92117be8.webp","is_representative":true,"generation_method":"ai_photo","license":"ai_generated","mime_type":"image/webp","width":1536,"height":1024,"translations":{"ko":{"alt":"식당 카운터에서 태블릿의 차트와 데이터를 살펴보는 여성","caption":"한 여성이 태블릿과 인쇄된 자료를 비교하며 데이터를 검토하고 있다.","description":null},"en":{"alt":"Woman reviewing charts and data on a tablet at a restaurant counter","caption":"A woman compares information on a tablet with a printed report.","description":null},"ja":{"alt":"飲食店のカウンターでタブレットのグラフとデータを確認する女性","caption":"女性がタブレットの情報と印刷資料を見比べている。","description":null},"es":{"alt":"Mujer revisando gráficos y datos en una tableta en el mostrador de un restaurante","caption":"Una mujer compara la información de una tableta con un informe impreso.","description":null},"id":{"alt":"Perempuan meninjau grafik dan data di tablet pada meja restoran","caption":"Seorang perempuan membandingkan informasi di tablet dengan laporan cetak.","description":null},"pt":{"alt":"Mulher analisando gráficos e dados em um tablet no balcão de um restaurante","caption":"Uma mulher compara informações no tablet com um relatório impresso.","description":null},"zh-hant":{"alt":"女子在餐廳櫃檯查看平板上的圖表與資料","caption":"一名女子正比對平板上的資訊與紙本報表。","description":null},"de":{"alt":"Frau prüft Diagramme und Daten auf einem Tablet an einem Restauranttresen","caption":"Eine Frau vergleicht Informationen auf einem Tablet mit einem gedruckten Bericht.","description":null}}},{"id":1452,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MjEwNjMsInB1ciI6ImJsb2JfaWQifX0=--595ff001fb75deadb34c8a35159d13264fa08279/ai-2475489e.webp","is_representative":false,"generation_method":"ai_semi","license":"ai_generated","mime_type":"image/webp","width":1536,"height":1024,"translations":{"ko":{"alt":"카페에서 태블릿의 차트와 문서를 살펴보는 여성, 옆에 시스템 흐름 아이콘이 표시됨","caption":"태블릿의 데이터를 검토하는 장면 위로 시스템 구성과 사용자 흐름을 나타내는 아이콘이 겹쳐 보인다.","description":null},"en":{"alt":"Woman reviewing charts on a tablet in a café with overlaid system workflow icons","caption":"System components and a user flow are visualized beside a woman analyzing data on a tablet.","description":null},"ja":{"alt":"カフェでタブレットのグラフを確認する女性と、重ねて表示されたシステムフローのアイコン","caption":"タブレットでデータを分析する女性の横に、システム構成とユーザーフローが示されている。","description":null},"es":{"alt":"Mujer revisando gráficos en una tableta en una cafetería con iconos de flujo del sistema","caption":"Los iconos junto a la mujer representan componentes del sistema y un flujo dirigido al usuario.","description":null},"id":{"alt":"Perempuan meninjau grafik di tablet dalam kafe dengan ikon alur sistem di sampingnya","caption":"Ikon di samping perempuan tersebut menggambarkan komponen sistem dan alur menuju pengguna.","description":null},"pt":{"alt":"Mulher analisando gráficos em um tablet em um café com ícones de fluxo do sistema","caption":"Os ícones ao lado da mulher representam componentes do sistema e um fluxo direcionado ao usuário.","description":null},"zh-hant":{"alt":"女子在咖啡廳查看平板上的圖表，旁邊疊加系統流程圖示","caption":"女子分析平板資料時，畫面以圖示呈現系統元件與使用者流程。","description":null},"de":{"alt":"Frau prüft Diagramme auf einem Tablet in einem Café, daneben Symbole für einen Systemablauf","caption":"Die Symbole neben der Frau veranschaulichen Systemkomponenten und einen Ablauf zum Nutzer.","description":null}}}],"published_at":"2026-09-22T04:13:16+09:00","updated_at":"2026-09-22T04:13:16+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant","de"],"url":"https://injoys.com/en/articles/jev-system-one-model-llm-comparison"}