OpenAI
The fast tier of GPT-5.6 — same generation, built for speed and volume.
1.05M tokens
Context
February 16, 2026
Knowledge cutoff
128,000 tokens
Max output
Fast
Speed
Three prompts, three unedited answers, generated on September 2, 2026 through NinjaChat. Click the prompt to copy it and run it yourself.
Write a Python function that merges overlapping intervals, plus three short tests. Keep the whole answer under 40 lines of code.
def merge_intervals(intervals):
"""Return overlapping (or touching) intervals merged."""
if not intervals:
return []
intervals = sorted(intervals)
merged = [list(intervals[0])]
for start, end in intervals[1:]:
if start <= merged[-1][1]:
merged[-1][1] = max(merged[-1][1], end)
else:
merged.append([start, end])
return [tuple(interval) for interval in merged]
# Tests
assert merge_intervals([]) == []
assert merge_intervals([(1, 3), (2, 6), (8, 10)]) == [(1, 6), (8, 10)]
assert merge_intervals([(5, 7), (1, 2), (2, 4)]) == [(1, 4), (5, 7)]
Read: GPT-5.6 Sol vs Terra vs Luna — Which GPT-5.6 size to use for what, with the same prompts run on all three.
Read: We ran the same 3 prompts on 12 AI models — Unedited answers, latency and price for every model, measured through the NinjaChat API in September 2026.
Served by OpenAI as gpt-5.6-luna. Answers vary run to run; these are one take each, not the best of several.
Emails, summaries and first passes that come back in seconds.
Extraction, classification and formatting at scale.
The quick questions that make up most of a day.
Screenshots and documents, read fast.
Tuned to this model — click any line to copy.
GPT-5.6 Luna is the fast tier of OpenAI's GPT-5.6 family, released in July 2026 alongside Sol and Terra. The number is the generation; the name is the capability tier. Luna is the one built for speed and volume: quick answers, high-throughput tasks, and the kind of everyday work where waiting for deep reasoning is the wrong trade. On NinjaChat it is included in every plan.
It is still a GPT-5.6 model. Luna reads images and files, calls tools, handles a context window past a million tokens with 128K tokens of output, and follows instructions the way the rest of the family does. What it gives up is depth on the hardest problems: for a multi-step plan or a tricky refactor, Sol thinks longer and gets further. For summaries, drafts, extraction, classification and most chat, Luna is the better experience because it answers fast.
The practical pattern most people settle into is to start on Luna and escalate. Draft with Luna, review with Sol; triage with Luna, decide with Sol. On NinjaChat both are in the same picker, so the escalation is one click, and the same goes for Claude, Gemini and the open models beside them.
Luna's training data runs to February 2026, the same as its siblings. For anything newer, give it the source or turn on search.
From zero to your first result in under a minute.
01
Create a NinjaChat account and choose a plan
02
Open chat and pick GPT-5.6 Luna from the model list
03
Ask for the draft, the summary or the extraction
04
Escalate to Sol when a task needs deeper reasoning
05
Compare with Gemini 3.7 Flash for the same speed class
Where it wins, and where it doesn't.
Much faster and cheaper for everyday tasks
Sol reasons far deeper on hard problems
Faster and cheaper
Terra sits in the middle on capability
OpenAI's tool ecosystem and instruction following
Gemini 3.7 Flash is the closest rival in the fast class
The same subscription covers the image studio and video generation.



One subscription covers every model on NinjaChat, GPT-5.6 Luna included.