Decision Model vs LLM: Two More Points Cost 87 Times as Much

The best AI chat models pick the right label about two points more often than a decision model. They also charge up to 87 times as much. We tested this on 25 September 2026 with 400 new classification tasks. Claude Opus 5.5 got 99.2% right and Jev got 97.2%. A million Jev answers cost under $20; the same million from Opus cost $1,708.

Key facts

• What we compared: two decision models (Jev and Tev) against two large language models, or LLMs (GLM-5.3 and Claude Opus 5.5).
• Accuracy: the LLMs got about two more tasks in every hundred right.
• Cost: the LLMs cost 41 to 87 times as much per answer.
• Speed: the decision models answered in under half a second; the LLMs took about five times as long.
• Where the LLMs pulled ahead: rules with dates and day counts. On sorting customer messages by intent, all four were perfect.
• The whole test cost $1.10. The items, code and results are public on GitHub.

What is a decision model, and how is it different from an LLM?

A decision model reads your text and picks an answer from a list you give it. An LLM reads the same text and writes a reply. Anthropic's Claude Opus 5.5 is one. Z.ai's GLM-5.3, from its General Language Model (GLM) family, is another. You then have to check that the reply is one of your options.

Give both a customer email and four options: refund, store credit, deny, or send to a person. The decision model returns one of the four, with a probability for each. The LLM thinks it through in words, often at length, then names one.

Giving up the writing makes decision models small, fast and cheap. Jev is TypeSafe's hosted decision model, launched on 15 September 2026. Tev1-4B-experimental is Together AI's open-weight copy of the idea, released on 23 September 2026. Together says it cost about $17 to train.

Jev and Tev both charge $0.042 per million input tokens, and output is free. A token is about three quarters of a word. Our Jev vs Tev post compares the two directly.

How much more accurate is an LLM?

The best LLM beat the best decision model by 2.0 points. Claude Opus 5.5 got 99.2% of our 400 tasks right, and Jev got 97.2%.

In counts, that is 11 wrong answers against 3.

The tasks come in 200 pairs. The two halves of a pair differ by one small edit that changes the answer, such as a return requested one day later. A model that skims gets one half right and the other wrong. The second bar counts pairs where both halves were right. The gap widens there, to 95.0% against 98.5%.

Rare answers tell the same story. We also scored each answer label on its own and averaged them, so a rare answer counts as much as a common one. On that measure, Jev scored 95.5% and Opus 5.5 scored 99.4%.

Claude wrote all 400 items, so no model had seen them, not even one trained on public datasets. Our guide on how to test a classifier explains the method.

What does the difference cost?

A million answers from Claude Opus 5.5 cost $1,707.74 at list price. The same million from Jev cost $19.64. That makes Opus 87 times as expensive, and GLM-5.3 41 times.

The LLMs cost more for two reasons. Their prices per token are higher: Opus 5.5 lists at $4 per million tokens read and $20 per million written. They also write output, including any reasoning they bill for. Jev also costs more than Tev at the same price. Its API bills about twice as many input tokens for the same text.

Put the two numbers together. Moving a million answers from Jev to Opus 5.5 adds $1,688.10 to the bill. In return, you get 20,000 more right answers. Each extra right answer costs about 8.4 cents.

Whether that is cheap depends on what a wrong answer costs you. Our post on cost per solved task walks through that sum.

How much slower is an LLM?

The typical LLM answer took five to six times as long as a Jev answer. The LLMs took 2.5 to 2.9 seconds; Jev took under half a second.

The slowest answers were further apart. One answer in twenty took over 6.7 seconds on GLM-5.3. On Opus 5.5, it took over 8.2. On Jev, that mark was just under one second.

A person waiting on a chat reply notices the difference. A nightly batch job does not. Jev's times include one extra hop through AI Space, our model gateway; Tev was called on Together directly.

Where did the LLM earn its price, and where did it not?

The LLMs earned their price on written rules with dates and day counts. On return policies, both scored 100% against Jev's 90%. On customer intent, all four models scored 100%, so the extra spend bought nothing.

Most of Jev's return-policy misses came down to counting days at the edge of a window. One pair used a bookshop policy: full refund within 14 days, store credit from day 15. The box set arrived on 20 February. In one half, the customer asked on 6 March, which is day 14. In the other half, the customer asked a day later. Jev swapped the two answers.

An LLM can count the days out in writing before it answers. A decision model gets one look.

Hard items show the same pattern. On the 170 marked hard, Jev scored 95.9% and both LLMs scored 98.8% or better.

With 50 items per family, one item moves a score by 2 points. Read the family table as a direction, not a ranking.

How do you combine a decision model and an LLM?

Send every item to the decision model first, and re-ask the LLM only when the decision model gives an answer it often gets wrong. In our benchmark, that setup got 98.1% right for $86 per million answers. GLM-5.3 alone got 99.0% for $809. The mix cost 9.4 times less.

Jev answers every task. If its answer is one of three risky answers, the same prompt goes to GLM-5.3, and GLM-5.3's answer is used. The three were respond_directly, approve_store_credit and approve_full_refund. When Jev gave them, it was right 67%, 75% and 88% of the time. GLM-5.3 got all of those tasks right.

Only 8% of tasks went to GLM-5.3. The typical answer still took 479 milliseconds, close to Jev on its own. The rule only looks at Jev's answer, so your code can apply it as each item arrives.

We picked this rule from 20 we tried, so its 98.1% is slightly optimistic. The risky list is also specific to these tasks; build your own from a few hundred labelled examples. Our LLM cascade post has the full sweep.

Jev's confidence score is a second way to split the work: our operations inbox sends anything under 0.8 confidence to Claude (Jev vs GLM-5.2 has the test).

Limits of this test

Four limits apply to the numbers above.

• The test is small. With 400 items, Jev's true accuracy likely sits between 95.5% and 98.8%. For Opus 5.5, the range is 98.2% to 100.0%. Family scores rest on 50 items each.
• Claude wrote the items, and the same kind of model checked the labels, with spot checks. That may tilt the test toward Claude.
• The LLMs ran at their default reasoning settings through AI Space. AI Space may charge a different rate than the list prices we used.
• Only pick-one questions were tested. Jev also answers yes/no and score questions; those were not part of this run.

The full numbers and caveats are in the benchmark's results and methodology.

FAQ

Is a decision model better than an LLM?

For picking one answer from a fixed list, a decision model is usually the better buy. In our test, the best decision model got 97.2% right at $19.64 per million answers. The best LLM got 99.2% at $1,707.74. For anything that needs written output, such as a summary or an extracted name, you need an LLM, because a decision model writes nothing.

When is an LLM worth the extra cost for classification?

When a wrong answer costs more than about 8.4 cents, going by our numbers. That is what each extra right answer cost when we moved from the decision model to the LLM. An LLM also pays on rules with dates or day counts. Both LLMs scored 100% on return policies, against 90% for the decision model.

Can a decision model replace an LLM entirely?

Only for the classification step. Decision models answer questions from a list and write no text. Most pipelines keep an LLM for extraction and writing, and for the items the decision model is unsure about. That split is what we run on our own inbox.

What is the difference between Jev and Tev?

Jev and Tev are both decision models at the same price per token. TypeSafe's hosted Jev scored 97.2% in our test. Together AI's open-weight Tev scored 90.0% and answered faster, in 0.17 seconds against 0.46. Jev vs Tev covers the full comparison.

Sources

• Our benchmark, jev-vs-tev results and methodology, run 25 September 2026
• TypeSafe, Introducing System One Models and Jev, 15 September 2026
• Together AI, How to train your own Jev for $17, 23 September 2026
• Together AI on X, Tev1-4B-experimental pricing, accessed 25 September 2026
• Cloudflare, GLM-5.3 on Workers AI, accessed 25 September 2026