On this site
Jev 1.13.0
TypeSafe · System One decision model
Typed decisions with calibrated probabilities, 70 to 500 ms per call, $0.042 per million input tokens.
Jev vs a generative model · independent measurements · updated October 7, 2026
Jev vs ChatGPT
One answers in prose and reasons for minutes; the other answers in probabilities in half a second.
ChatGPT is a chat product; the models behind it, GPT-6 Astra and GPT-5.6 Terra, are generative models that write, reason, use tools and operate computers. Jev does none of that. It reads a state, answers the typed questions you send and returns a probability for each option, with no text at all. People compare the two because the classification, routing and scoring prompts teams send to GPT are exactly what Jev was built for. This page quotes the published prices and the measurements that exist, then draws the line between them.
vs
Compared with
ChatGPT
OpenAI · Generative models: GPT-6 Astra, GPT-5.6 Terra
Text, code, structured outputs, function calling and computer use from a 1,050,000-token context, at $10 input and $50 output per million tokens.
- JevChoice, score or yes/no probability; no textWhat comes backChatGPTGenerated text; structured outputs and function calling
- Jev$0.042 input, output freeList price per million tokensChatGPTGPT-6 Astra: $10 input, $50 output, $1 cached read
- Jev70 to 500 ms per call, vendor-reportedLatencyChatGPTSeconds per reply; about 40 minutes per OSWorld 2.0 agent task
- Jev64k tokens; 32k on some gateway listingsContext windowChatGPT1,050,000 tokens, 128,000 max output
- Jev68% accuracy, $0.0004 per case, 0.4 sTypeSafe’s four-workflow evalChatGPTGPT-5.6 Terra: 68%, $0.03 per case, 10 s
- JevNot applicable: no reasoning, no textReasoning benchmarksChatGPTGPT-6 Astra: 96.0% GPQA Diamond, 97.6% FrontierMath Tier 4
- JevNoExplains its answerChatGPTYes
- JevNoChats, writes, codes, browsesChatGPTYes
What has been measured
Jev and ChatGPT share no benchmark, so every number here is one party’s own test, quoted with its setup and its caveats.
Price and latency: Jev vs GPT-6 Astra
DataCamp compared the two on published list prices and vendor-reported latency, with two example workloads: a decision workload of 10 million input tokens and 1 million output tokens on Jev, and generation workloads of 1 million input tokens with 250,000 or 4 million output tokens on Astra.
| Jev | ChatGPT | |
|---|---|---|
| Input price per million tokens | $0.042 | $10; $1.00 cached read, $12.50 cache write |
| Output price per million tokens | Free | $50 |
| Example workload | $0.42 for 10M in / 1M out | $22.50 for 1M in / 250K out; $210 for 1M in / 4M out |
| Latency | 70 to 500 ms per call | Minutes on agent tasks; about 40 minutes per OSWorld 2.0 task |
| Context | 32,000 tokens on the OpenRouter and Vercel listings | 1,050,000 tokens; 128,000 max output |
Per token Jev is 238 times cheaper on input and bills nothing for output. The comparison is between different jobs: Astra’s price buys reasoning, generation and computer use, which Jev does not do at any price. DataCamp’s line is that Jev is for classification, routing, scoring and guardrails at volume, and Astra for multi-step tasks, text and code.
Source: DataCamp — Jev vs GPT-6 Astra: speed, cost and when to use each, September 21, 2026.
Agreement with a frontier reference
TypeSafe’s own four-workflow evaluation, reported by DataCamp and Arize, scores each model by agreement with a reference built from GPT-6 Astra and Claude Fable 5.1, and by cost and time per case. It is the vendor’s evaluation, not an independent one.
| Jev | ChatGPT | |
|---|---|---|
| Agreement with the reference | 67.8% (reported as 68%) | GPT-5.6 Terra: 68% |
| Cost per case | $0.0004 | $0.03 |
| Time per case | 0.4 s | 10 s |
On its own evaluation TypeSafe shows Jev level with GPT-5.6 Terra at 75 times lower cost and 25 times the speed, and five points behind Claude Opus 5, which scored 73% at $0.18 and 38 seconds per case. Treat these as the vendor’s numbers; the independent Claude measurements on the Jev vs Claude page are the closest third-party check on the same kind of task.
Source: Arize — TypeSafe’s Jev: can decision models replace LLM judges?, September 2026.
The analysis
What the measurements add up to, and what they leave out.
Start with what each one is. ChatGPT is an application; GPT-6 Astra and GPT-5.6 Terra are the generative models behind it and the API. They produce text, call tools, write code and, in Astra’s case, operate a computer for 40 minutes at a stretch. Jev produces no text at all: it reads the state once and returns a probability distribution for each typed question. That is why there is no benchmark on which both sit, and why GPT’s GPQA and FrontierMath scores say nothing about Jev, and JevBench says nothing about GPT.
Where the two overlap is the narrow decision prompts teams send to GPT because it is already in the stack: which queue, which tier, is this spam, does this answer cite the source, how angry is the customer. On those, TypeSafe’s own evaluation puts Jev level with GPT-5.6 Terra at $0.0004 against $0.03 per case and 0.4 against 10 seconds. No independent GPT measurement exists yet; the independent Claude studies, where Jev trails a frontier model by about three points on a hard intent set and matches a small one on decomposed questions, are the best guide to what to expect.
The price gap is structural. OpenAI bills GPT-6 Astra at $10 per million input tokens and $50 per million output; Jev bills $0.042 per million input and nothing for output. A classification call that returns a 20-token JSON label on GPT pays for reasoning capacity it does not use. The latency gap is structural too: Jev emits a few numbers from one pass, GPT generates tokens. Against that, GPT reads a million tokens where Jev reads 64 thousand, and GPT can tell you why.
Price and speed, as published
List prices on October 7, 2026: Jev at $0.042 per million input tokens with output free; GPT-6 Astra at $10 input, $50 output, $1.00 cached input read and $12.50 cache write per million tokens, as quoted by DataCamp from OpenAI’s price list. Check OpenAI’s pricing page for the current GPT-5.6 Terra rate.
JevBench v1.6.0 measured the hosted Jev API at 0.24s median / 0.30s p95 per request, with a calibration score of 90.6; the Jev 1.13.0 guide has the version’s limits and the pricing page has Jev AI’s own plans.
So which should you use?
Pick Jev if
- The output is a label, a probability or a score, and nobody needs to read a sentence.
- The decision runs at volume or inside a request path, where 0.4 seconds and $0.0004 per case are the budget.
- The rules change often and you want to edit the question rather than a prompt whose JSON you then parse.
Pick ChatGPT if
- The output is text: a reply, a summary, a translation, code or an explanation.
- The task needs multi-step reasoning, arithmetic, tool use, browsing or computer use.
- The input is longer than 64k tokens, or is an image, audio or video.
Use both when
- Jev decides which GPT model to send a request to, and whether it needs the expensive one at all; GPT writes the answer.
- GPT drafts; Jev scores the draft against a rubric or a source before it ships, and sends low scores back for a rewrite.
- Jev screens inbound text for prompt injection and checks tool calls before a GPT agent acts on them.
Measure it on your own cases
Every study above says the same thing in its caveats: the result depends on the task and the prompt. Run your own routing, judging or screening cases on Jev here with no setup, keep the answers, and compare them with what ChatGPT returns on the same inputs.
Jev vs ChatGPT FAQ
Is Jev a ChatGPT alternative?
No. ChatGPT chats and writes; Jev cannot. Jev is an alternative for the classification, routing, scoring and guardrail prompts that teams send to GPT models, where it returns a probability instead of text at a small fraction of the cost.
Is Jev more accurate than GPT?
On TypeSafe’s own four-workflow evaluation Jev and GPT-5.6 Terra both score 68% agreement with a frontier reference; Claude Opus 5 scores 73%. No independent measurement of Jev against a GPT model has been published yet. On reasoning benchmarks such as GPQA Diamond the question does not apply: Jev does not reason or generate.
How much cheaper is Jev than ChatGPT’s API?
On list price, Jev is $0.042 per million input tokens with free output; GPT-6 Astra is $10 per million input and $50 per million output. On TypeSafe’s evaluation a case costs $0.0004 on Jev and $0.03 on GPT-5.6 Terra.
Is Jev faster than GPT?
Yes, by construction. Jev returns a few probabilities from one pass, vendor-reported at 70 to 500 ms per call and measured at a 0.24-second median on JevBench v1.6.0; GPT generates tokens, taking seconds per reply and minutes per agent task.
Does Jev support function calling or structured outputs?
Its whole output is structured: every answer is a probability over the options you defined, so there is nothing to parse and no schema to enforce. It does not call functions; the MCP tool router use case shows how to use Jev to choose a tool that your code then calls.
Can I send a ChatGPT prompt to Jev?
Not as is. A Jev request is a state plus typed questions with their options, sent to a decision endpoint, not a chat prompt. The how-to-use-Jev guide shows how to turn a classification prompt into questions.
Related comparisons
Every figure on these pages was read from its source on October 7, 2026.
- Jev vs LLMHow a decision model differs from chat models and classifiers in general.
- Jev vs ClaudeA decision model against a generative one: close on labels, far apart on speed, cost and what comes back.
- Jev vs BERTThe same job as a fine-tuned classifier, with the labels written at request time instead of trained in.
- JevBench resultsJev against the open decision models measured under one method.
Sources
ChatGPT and every other product named on this page belongs to its respective owner and is not affiliated with Jev AI. Jev is developed by TypeSafe; Jev AI is an independent playground and API. Figures are quoted from the sources above as they read on October 7, 2026 and can change.