Comparison

Jev vs GPTSame score on TypeSafe's own eval. 76x cheaper, 25x faster.

On TypeSafe's own four-workflow eval, Jev scores 67.8% and GPT-5.6 Terra scores 67.9%. That is a tie. Jev gets there for $0.0004 per case instead of $0.0304, in 0.4 seconds instead of 10.1. It also cannot write a single word, which is the part every other page on this SERP leaves out.

Last updated: September 2026

Jev decides. GPT writes. OpenTweet publishes.

The short answer

These are not competing products. GPT is a generator that can be pushed into making decisions. Jev is a decision model that cannot generate. If your call ends in text a person reads, GPT is the only option of the two. If your call ends in a branch in your code, Jev does it for about one seventy-sixth of the price at the same accuracy on TypeSafe's own eval.

The number people quote, 193.6x faster and 444.6x cheaper, is TypeSafe's homepage headline against the slowest and costliest baseline on the board. TypeSafe itself writes that those figures "are on the higher end of real world gains". The comparable model is Terra, and against Terra the arithmetic on TypeSafe's own numbers is 25x faster and 76x cheaper. That is still a very large number and it has the advantage of being defensible.

Jev vs GPT-5.6 Terra, side by side

MeasureJev (jev-1.13.0)GPT-5.6 Terra
Workflow accuracy, 4 workflows
67.8%
67.9%
Cost per workflow case
$0.0004
$0.0304
Latency per workflow case
0.4s
10.1s
Prompt-mode accuracy
Not applicable, there is no prompt mode
61.6%
Input rate card
$0.042 per 1M tokens
$2.00 per 1M tokens
Output rate card
Free, output is unmetered
$12.00 per 1M tokens
Cost per 1,000 decisions at 500 input tokens
About $0.02
About $1.60 with 50 output tokens
Generates text
No. None at all
Yes
Image, audio or video input
No. Text only
Yes
Returns a probability distribution
Yes, over your declared answer space
OpenAI documents logprobs as token-level log probabilities, not a distribution over your enum values
Schema conformance
Guaranteed by construction. TypeSafe states schema matching is guaranteed and that its 0% figure is not empirical
0.58% to 45.5% schema errors on TypeSafe's sourced figures
Decoding
Parallel. Every question evaluated in one pass
Autoregressive, token by token
Context budget
64k tokens per request, 32k for state plus longest question
Model dependent, far larger
Rate limit
250,000 tokens per second, 1,200 requests per minute
Account and tier dependent
Self-host or open weights
No. Hosted closed weights only
No
Access
Waitlist, or OpenRouter, Vercel AI Gateway, Cloudflare
Public API

Accuracy, cost per case and latency from evals.typesafe.ai, TypeSafe's own eval, read on 18 September 2026. Rate cards from docs.typesafe.ai/models and, for Terra, as reported by The Register. Per 1,000 figures are arithmetic on those rate cards. Prompt-mode accuracy is the board's separate prompt-mode column, not the workflow column. Rate limits are from docs.typesafe.ai/models. Capability rows reflect what each vendor's own structured-output documentation returns, not a test we ran.

Four things to know before you quote that table

It is self-reported. TypeSafe built the eval, wrote the four workflows and published the board. TypeSafe says so plainly: the workflows "were made by individuals on our model capabilities team, so some bias could exist".

There is no human ground truth. The reference answer is the average of two other frontier models, Astra and Fable 5.1, at high thinking. So 67.8% means Jev agrees with that consensus about two thirds of the time. Terra's 67.9% means the same thing. Whether the consensus was right is a question the eval cannot answer. TypeSafe also notes this biases the board toward OpenAI and Anthropic models.

Jev does not top the board. Sol scores 74.1% and Opus 5 scores 73.1%, both above Jev and Terra. If you need the best answer rather than the cheapest adequate one, the board says to buy the expensive model.

The zero hallucination number is about schema, not truth. TypeSafe's own blog says "Our number is not empirical. Schema matching is guaranteed." Jev cannot return a value outside the answer space you declared. It can absolutely return the wrong one from inside it, and TypeSafe's CEO acknowledged exactly that on Hacker News. We wrote the long version at can Jev hallucinate.

What each one cannot do

The column every other comparison leaves out. Both lists come from vendor documentation, not from us.

Jev cannot

  • Write a sentence. No prose, no code, no summaries, no JSON authoring. This is the whole design, not a gap.
  • Read an image, a video or audio. The docs say text only: string, JSON object, or array of text values.
  • Count or do arithmetic reliably. TypeSafe tells you to count in code and send the model the judgment instead.
  • Order dates or measure durations. Dates are treated as text, not as ordered values.
  • Handle a question with no declared answer space. Every Choice, Score and Noul declares its options up front.
  • Carry an answer from one question into the next. Questions are evaluated independently in the same pass.
  • Resist adversarial input on its own. TypeSafe says state is not treated as hostile, so user-controlled text can steer the answer.

GPT cannot

  • Return a probability distribution over your options from the structured response. OpenAI documents logprobs as token-level log probabilities, not a distribution over your enum values. See the logprobs section on /jev/vs-openai-structured-outputs.
  • Guarantee the response parses. TypeSafe cites schema error rates from 0.58% to 45.5% across LLM baselines.
  • Answer in under a second at the accuracy tier where the comparison is fair. The eval board puts Terra at 10.1 seconds per case.
  • Stay cheap across a firehose. At Terra’s published $2.00 per 1M input and $12.00 per 1M output, as reported by The Register and sourced in the table above, a per-item judgment over millions of items is a budget line, not a rounding error.
  • Evaluate twenty questions about one document in a single parallel pass. Each generated answer costs tokens and time.

The one independent test that exists

Mike Taylor, head of evals at Every, ran Jev against Claude Fable 5.1 on his own writing.

He sent 37 documents with 21 questions each in a single request and got 777 judgments back in under 0.7 seconds for an estimated quarter of a cent. Across 11 experiments he made 1,709 judgments for under a cent in total. Median latency per passage was 0.35 seconds against 8.83 seconds for Fable 5.1, roughly 25x, and cost was roughly 580x lower.

The accuracy result is the interesting half. On a set of seven planted defects, Fable 5.1 caught all seven and Jev caught six, missing the same unexplained action defect in all three runs. His own conclusion was that he would want a more thorough accuracy check before production and that it works well as an early warning system. That is the right way to hold it. Read it at every.to.

The two-model architecture

You do not pick one. You put the cheap model in front of the expensive one and let it decide what is worth paying for.

  1. Jev decides

    One request carries the draft as state plus every question you want answered. Score the hook, score the clarity, ask three Nouls about slop, bait and toxicity. The answer comes back with a probability distribution, so your code can act above a threshold and escalate below it.

  2. GPT writes

    Only the drafts that failed the bar go to the expensive model. That is the whole saving: the generator runs only on the items the classifier flagged instead of on all of them, and the classifier cost about two cents per thousand at 500 input tokens on the published $0.042 per million.

  3. OpenTweet publishes

    One POST to /api/v1/posts sends the finished text to X, Bluesky and LinkedIn, now or at a scheduled time, with per-platform send status coming back on the post. The same key works for the REST API and the MCP server.

What that looks like in code

Jev asks the questions. GPT rewrites the failures. OpenTweet ships the result.

decide-then-write.ts
import { TypeSafeClient, noul, score } from "@typesafe-ai/sdk";

const jev = new TypeSafeClient();

const { answers } = await jev.systemOne({
  state: { draft_post: draft, platform: "X (Twitter)" },
  questions: {
    hook: score("How well does the opening line stop someone mid scroll?", [
      "No hook. Opens with context or a greeting.",
      "Functional. States the subject plainly.",
      "Strong. Creates tension or surprise in the first line.",
    ]),
    slop: noul("This reads like it was generated by an AI rather than written by a person."),
  },
});

// Score returns a probability weighted mean plus a confidence.
// Noul returns only a float from 0 to 1. There is no confidence field on a Noul.
const needsRewrite = answers.hook.score < 1.5 || answers.slop.noul > 0.6;

Then publish the version that passed.

bash
curl -X POST https://opentweet.io/api/v1/posts \
  -H "Authorization: Bearer ot_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "We cut our CI time from 22 minutes to 6.",
    "platforms": ["x", "bluesky", "linkedin"],
    "scheduled_date": "2026-10-02T14:00:00Z"
  }'

Two schemas, do not mix them

On TypeSafe's native API the yes or no primitive is called a Noul and its type is noul. Through the Vercel AI Gateway the same primitive is spelled boolean in the AI SDK's evaluate call. Both are correct on their own surface and neither works on the other.

Which one to reach for

Reach for GPT when the output is language, when the input has an image in it, when the task needs several steps of reasoning chained together, or when you are making a few hundred calls a month and the price difference is noise.

Reach for Jev when the same small judgment runs over every item in a stream, when you want a number to threshold on rather than a sentence to parse, when the answer space is closed, and when 10 seconds of latency is not available because the decision sits inside a user-facing loop. Then put the two together, because the interesting systems use both. The alternatives page covers the other five options, and the limits page has the rate limits and context budget in full.

Frequently asked questions

Is Jev better than GPT?

Not on accuracy. On TypeSafe's own four-workflow eval Jev scores 67.8% and GPT-5.6 Terra scores 67.9%, which is a tie, and two models on the same board score higher: Sol at 74.1% and Opus 5 at 73.1%. Where Jev wins is cost and latency at that tied accuracy: $0.0004 per case against $0.0304, and 0.4 seconds against 10.1 seconds. The pitch is 76x cheaper and 25x faster for the same score, not a better score.

Can Jev replace GPT?

Only for the part of your GPT traffic that is a decision. Jev generates no text at all: no prose, no code, no summaries, no JSON authoring. If a call ends in something a human reads, GPT stays. If a call ends in a branch in your code, Jev is usually the cheaper way to make that branch. Most production pipelines end up running both.

Are the Jev vs GPT benchmarks independent?

No. The headline numbers come from evals.typesafe.ai, which TypeSafe built, using workflows TypeSafe's own model capabilities team wrote. There is no human ground truth in it. The reference answer is the average of two other frontier models, Astra and Fable 5.1, so the scores measure agreement with a two-model consensus rather than correctness. TypeSafe states these caveats itself. The one independent test published so far is Mike Taylor's at Every.

How much faster is Jev than GPT?

25x on the comparable model, using TypeSafe's own eval: 0.4 seconds per case against 10.1 seconds for GPT-5.6 Terra. TypeSafe's homepage claims 193.6x faster, but that compares against the slowest baseline on the board and TypeSafe itself writes that these figures "are on the higher end of real world gains". The blog's own range is 40x to 200x. Use 25x when the comparison is Terra.

What can GPT do that Jev cannot?

Write anything. Read an image. Count reliably. Order dates. Run a multi-step chain of reasoning. Hold context across questions, since Jev evaluates every declared question independently in one parallel pass. Work without a predefined answer space, because every Jev question declares its options up front. TypeSafe documents all of these limits on its model jaggedness page rather than hiding them.

What is the two-model pattern?

Jev decides, GPT writes, OpenTweet publishes. Jev scores every draft or reply and returns a probability you can threshold on, GPT rewrites the ones that fail the bar, and one POST to /api/v1/posts sends the result to X, Bluesky and LinkedIn. Each model does the half it is shaped for, and the expensive model only runs on the items the cheap one flagged.

Keep exploring

The rest of the Jev cluster, and the API that publishes whatever the two models agreed on.

What Jev is, in one page

System One, the three primitives, and the limits TypeSafe documents itself.

Jev alternatives

Seven options compared on cost per 1,000 decisions, latency and what each one returns.

Jev vs Claude Haiku

The small fast LLM comparison, with cost math on 100,000 replies.

Jev vs Gemini Flash

Multimodal against text only. If your feed has screenshots in it, read this one.

Will it go viral, free scorer

A live Jev scorer. Seven questions, no signup.

Build an AI social media agent

The whole pipeline: a model that decides, a model that writes, an API that posts.

The decision is cheap. The publishing is the part you outsource.

Whichever model makes the call, one POST reaches X, Bluesky and LinkedIn. REST API, 43 MCP tools, from $11.99 a month.

  • 7-day free trial
  • No X developer account needed
  • One key for REST, MCP and the CLI