Jev vs GPT-5.6 Luna: cost per decision
Production example · monthly
- Jev 1.13
- $126.00
- GPT-5.6 Luna
- $624.00
GPT-5.6 Luna: 5.0x the Jev bill
Luna is the cheapest frontier-family LLM most teams reach for when classifying. Here is what the same workload costs on each, with output tokens counted.
Standard base token prices checked 2026-10-17. OpenAI pricing source. Check vendor terms before budgeting; other tiers and long-context rates can differ.
Light
10k items/day, 300-token state, 2 questions
- Jev 1.13
- $8.57/mo
- GPT-5.6 Luna
- $36.00/mo
4.2x the Jev bill
Production
100k items/day, 500-token state, 4 questions
- Jev 1.13
- $126.00/mo
- GPT-5.6 Luna
- $624.00/mo
5.0x the Jev bill
High volume
2M items/day, 300-token state, 1 question
- Jev 1.13
- $1,512/mo
- GPT-5.6 Luna
- $4,800/mo
3.2x the Jev bill
Monthly, 30 days. Both sides answer all questions for an item in a single call. GPT-5.6 Luna additionally pays for its output tokens at $1.2/M; Jev 1.13 has no output charge. Jev 1.13’s figures include the ~260 tokens every request is measured to bill; the GPT-5.6 Luna figures count your content only, with no system prompt or schema, so they are a floor. List prices at standard tier, excluding caching and batch discounts. Comparison rates checked 2026-10-17; verify current vendor prices before budgeting.
Where GPT-5.6 Luna still wins
Luna can write the answer. If the step after classification needs prose, a summary or a tool call with free-form arguments, Jev cannot produce it.
The honest caveat
This compares price, not accuracy. A model that is 100x cheaper and wrong 5% more often can easily be the more expensive choice once you price the mistakes. Jev returns a calibrated probability with every answer, so the practical move is to route the confident cases automatically and send the rest to GPT-5.6 Luna or a human — which makes this a routing decision rather than a replacement decision.
Run these numbers on your own workload →
What Jev AI is, what it costs and its limits →
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