GPT 5.6 Luna pricing for sentiment analysis

Quick start

Call GPT 5.6 Luna with the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-5.6-luna",
    max_tokens=100,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.000220 per call at standard rates
# Input:  $0.20/MTok
# Output: $1.20/MTok

How GPT 5.6 Luna compares on cost

Per-call cost for all models scoring 50+ on sentiment analysis benchmarks. Hover for details.

Highest-quality affordable option: Gemini 3.5 Flash-Lite at $0.000400/call (1.8x the cost, 85/100 quality). GPT 5.6 Luna is already the cheapest choice for sentiment analysis.

What drives sentiment analysis cost

Short text in, a label or score out: the cheapest shape an API call can take. Output pricing is irrelevant at this size. Because these run in bulk over historical data as often as in real time, the batch API discount typically applies, and the effective rate lands well below list.

For GPT 5.6 Luna specifically, that means output tokens cost 6.0x more than input, at $1.20 vs $0.20 per MTok. At this use case’s 500/100 token split, output accounts for 55% of the $0.000220 per-call cost.

GPT 5.6 Luna quality benchmarks for sentiment analysis

Performance across all task types

GPT 5.6 Luna scores by task type. Sentiment Analysis highlighted.

Confidence scores are a third-party benchmark snapshot from week 26 of 2026. This snapshot is 17 weeks old, so treat the scores as directional and validate against your own evals.

About GPT 5.6 Luna

Other models from GPT

You can compare GPT 5.6 Luna head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions