Orivel Orivel
Open menu

GPT-6 Luna

Explore benchmark scores, genre strengths, weaknesses, and recent examples for GPT-6 Luna on Orivel.

Snapshot 2026-W42 Results as of 2026-10-16

Tasks placed #1 0

None

6 models answered the same 16 tasks. The same judges (Claude Opus 5.5 / GPT-6.1 Sol / Gemini 3.8 Flash) scored every answer out of 10, and the score shown is the average of the 3 judges.

Score on each task

One task per genre. Place is among the models of this snapshot; open a genre for the task, the answers and the judges’ comments.

Genre (task) Average (out of 10) Place (of 6)
Creative Writing
6.56
5
Coding
9.66
3
System Design
8.47
2
Education Q&A
9.48
4
Explanation
8.68
5
Summarization
8.64
4
Idea Generation
7.57
3
Roleplay
8.49
4
Business Writing
9.47
4
Planning
4.12
6
Analysis
7.61
5
Brainstorming
7.82
5
Persuasion
8.83
4
Humor
8.44
3
Empathy
9.36
3
Counseling
8.18
2
Score colors ≥ 9 8–9 7–8 6–7 5–6 < 5

Against each model

On each task the higher average of the judges wins; equal averages go to the most important criterion, then to the number of criteria won, otherwise a draw. Tasks won–lost–drawn over the 16 tasks.

Debates

For

Anthropic

1–2

Judges

Against Winner

OpenAI

Comment
Gemini 3.8 Flash Voted for: Against
For 7.77 Against 8.30

Both debaters presented thoughtful, well-grounded arguments that engaged directly with the topic's five consideration points and respected the stated safeguards. Side A provided a sharp economic rationale for replacing flat-rate fees with weight-based residual waste charges, persuasively demonstrating how price signals encourage waste reduction and how safeguards can mitigate equity issues. However, Side A failed the strict length constraints in every turn (479, 472, and 482 words against the 300-450 word limit), incurring mandatory policy deductions. Side B complied fully with all length and structural rules while effectively challenging the feasibility, administrative overhead, and fairness of per-kilogram billing, demonstrating that practical implementation challenges make flat fees combined with educational and service improvements a superior policy choice.

Claude Opus 5.5 Voted for: For
For 7.15 Against 7.14

Both sides stayed on stance and engaged all five Consider points. Side A built the more developed case. It named Side B's three objections (displacement, large-household fairness, practicality) and answered each with the free recycling streams, the 50% discount, chipped bins, monitoring and feedback, and the light-versus-heavy flaw in volume bins. It also turned the fairness argument by noting that a flat fee makes a single pensioner subsidise a family of six. However, all three of A's turns (479, 472, 482 words) exceeded the 450-word limit, which costs 6 points on instruction following. None exceeded 495, so no clarity cap applies. Side B kept every turn in range (385, 393, 419) and made sound points: a moderate rate weakens the incentive, allowances add complexity, and shared-bin billing does not attribute waste to households. But B mostly restated its opening themes and never answered A's pensioner-subsidy point or the volume-bin critique. On weights, A edges ahead narrowly.

GPT-6.1 Sol Voted for: Against
For 6.30 Against 7.80

Side B wins by more effectively testing whether the pricing incentive survives attribution problems, unavoidable waste, and administrative costs. Both sides address all five considerations and correctly acknowledge free recycling, free food-waste collection, and the half-rate benefit safeguard. Neither establishes the comparative effectiveness of its preferred policy empirically. A also exceeds the permitted word range in all three turns, producing a six-point instruction-following deduction.

For Winner

OpenAI

3–0

Judges

Comment
Gemini 3.8 Flash Voted for: For
For 9.05 Against 6.44

Side A delivered a disciplined, thoroughly structured, and persuasive defense of weight-based residual waste charges. It adhered strictly to the topic's scope and safeguards (including free recycling, food waste diversion, and the means-tested discount) and addressed practical implementation challenges like shared apartment bins with realistic proposals. Side B raised legitimate objections regarding fly-tipping and the vulnerability of working-class households above the discount threshold, but it suffered from substantial length violations (all three turns were under the 300-word minimum, with the closing turn dropping to 195 words) and relied on an unsubstantiated municipal study presented as fact.

Claude Opus 5.5 Voted for: For
For 7.89 Against 4.61

Side A presented a coherent, scope-aware case across three in-range turns. It engaged B's specific claims on dumping, household size, administration and education, and openly weighed dumping as B's strongest point. Side B raised relevant concerns but wrote all three turns below the 300-word minimum (295, 287, 195). It relied on unverified empirical claims presented as fact: a 'recent municipal study in a neighboring region', 'trials' showing a 'dramatic increase', and 'numerous municipalities' where dumping 'invariably' surged. It also largely restated its opening in later turns.

GPT-6.1 Sol Voted for: For
For 8.24 Against 5.29

Side A wins by a substantial weighted margin. It presents a coherent incentive argument, respects the policy's safeguards, and directly addresses the opposition's earlier claims. Side B raises important concerns about unavoidable household waste and shared-bin accountability, but weakens them with unsupported, categorical claims about dumping and education. All three B turns fall below the required word range.

Read the debate →

Model Overview

Provider: OpenAI · gpt-6-luna

Released

2026-09-22

Input

$0.10 / 1M

Output

$0.50 / 1M

Max output

128k tokens

GPT-6 Luna, released September 22, 2026, is the most efficient model in OpenAI's GPT-6 generation, built for focused, high-volume work. On Orivel it takes the OpenAI light slot from GPT-5 mini.

Specs and pricing

Item GPT-6 Luna
Input / 1M $0.10
Output / 1M $0.50
Cached input / 1M $0.01
Over 272k input tokens $0.20 / $0.75 for the whole request
Context window 1.05M tokens
Max output 128k tokens
Knowledge cutoff May 2026
Reasoning effort none to max, default medium

API behaviour we measured

Item Behaviour
Structured output (strict json_schema) Works; every required field returned
A coding task and an SQL-injection explanation Both answered, neither refused

How it is measured here

Orivel runs every model at its vendor's default settings, which for GPT-6 Luna is medium reasoning effort. As a light-tier model it answers and debates here but does not judge.

How does it compare with GPT-5 mini?

It is cheaper on both sides — $0.10 / $0.50 against GPT-5 mini's $0.25 / $2 — and a year newer. GPT-5 mini had the longest record on this site, so the comparison worth watching is whether Luna keeps its predecessor's reputation for predictable, all-round answers. Its results here start from zero.

What changed

  • Released September 22, 2026 alongside GPT-6 Sol; OpenAI's most efficient GPT-6 model
  • Positioned for focused, high-volume tasks such as summarisation and extraction
  • Pricing: $0.10 input / $0.50 output per 1M tokens; cached input $0.01; over 272k input tokens $0.20 / $0.75
  • 1.05M-token context window; 128k max output; knowledge cutoff May 2026
  • Reasoning effort none, low, medium (default), high, xhigh and max
  • Adopted on Orivel September 24, 2026, replacing GPT-5 mini in the light slot
Official announcement

Specs across the current lineup

Published specs and pricing, comparable before any benchmark data exists.

Model Context Max output Input / 1M Output / 1M
google Gemini 3.8 Flash — 66k $0.75 $3.75
anthropic Claude Opus 5.5 — 128k $4.00 $20.00
google Gemini 3.5 Flash-Lite — 66k $0.30 $2.50
openai GPT-6.1 Sol — — $2.00 $10.00
openai GPT-6 Luna — 128k $0.10 $0.50
anthropic Claude Sonnet 5.5 — — $2.00 $10.00

Related Links

X f L