OpenAI GPT-6 Family · Luna Tier · September 22, 2026
20 free GPT-6 Luna prompts for everyday tasks — writing, coding, analysis, productivity, and learning. Fast, affordable, and more capable than GPT-5.5. Copy directly into ChatGPT. No signup.
This page gives you 20 copy-paste prompts built for GPT-6 Luna — OpenAI's fast, lightweight tier in the GPT-6 family, released September 22, 2026. GPT-6 Luna is the everyday default for ChatGPT users and API developers who need speed and cost-efficiency without sacrificing the quality jump the GPT-6 generation brings over GPT-5.5. It handles the long tail of real-world tasks — drafting, coding, summarising, analysing — faster and more reliably than its predecessor.
The prompts here are structured for Luna's strengths: clear, specific instructions that produce consistent, usable output on the first try. Each prompt includes bracketed placeholders — fill them in with your specific context and paste directly into ChatGPT or the OpenAI API.
Everyday writing, coding fixes, emails, summaries, FAQs, data lookups. The right default for 80% of ChatGPT tasks and all high-volume API use.
Complex analysis, longer structured documents, research synthesis, multi-step reasoning tasks. Mid-tier cost with meaningfully deeper output.
Hardest tasks — full codebase audits, 100+ page documents, adversarial analysis, complex agentic workflows. GPT-6's maximum capability.
Tested on ChatGPT and the OpenAI API. Works on GPT-6 Luna, Sol, and Terra.
Fix the following code. Return only the corrected code and a bullet list of every change made with a one-sentence explanation of why. Language/framework: [specify] Error message (if any): ``` [paste error] ``` Code: ``` [paste code] ```
Draft a professional email for the following situation: Context: [describe the situation — what happened, what you need, who you're writing to] Tone: [formal / friendly-professional / direct] Key points to include: [list 2–4 things the email must say] Desired outcome: [what should the reader do after reading it?] Keep it under 200 words. No filler opener ('I hope this finds you well'). Start with the main point.
Explain what this code does in plain English. No technical jargon unless necessary — write for someone who can read code but doesn't know this specific codebase. For each major block: 1. What it does 2. Why it exists (the problem it solves) 3. Any gotchas or non-obvious behaviour Code: ``` [paste your code] ```
Create a detailed blog post outline for the following topic: Topic: [your topic] Target audience: [who will read this] Primary keyword: [the search term you want to rank for] Desired word count: [500 / 1000 / 1500 / 2000 words] Tone: [conversational / authoritative / educational / opinionated] Deliver: - H1 headline (include the primary keyword) - 4–6 H2 sections with 2–3 bullet points each describing what each section covers - A suggested introduction hook (one sentence — the problem or surprising claim that opens the post) - A suggested conclusion CTA
I have the following data. Give me 5 actionable insights in plain English — no statistical jargon. For each insight: 1. State the observation 2. Explain why it matters 3. Suggest one specific action to take Data: [paste your data — CSV rows, a table, or key metrics]
Explain [topic or concept] to someone who has never heard of it before. Use an analogy from everyday life. Avoid jargon — if you must use a technical term, define it immediately. At the end, give me 3 follow-up questions a curious beginner would naturally ask next.
Write 5 social media captions for the following content: Product/topic: [what are you promoting or talking about?] Platform(s): [Instagram / LinkedIn / X / TikTok — or all] Tone: [casual / inspirational / witty / informative] Key message: [the one thing you want people to take away] For each caption: - The caption text (platform-appropriate length) - 3–5 relevant hashtags - A one-line note on which audience segment it targets
Summarise the following meeting transcript or notes into a structured meeting summary. Meeting type: [standup / planning / client call / retrospective / strategy session] Expected readers: [attendees / stakeholders who didn't attend] Summary format: 1. DATE AND ATTENDEES 2. KEY DECISIONS MADE — bulleted, each as one sentence 3. ACTION ITEMS — [who] will [do what] by [when] 4. OPEN QUESTIONS — things discussed but not resolved 5. NEXT MEETING — date, topic, and owner Transcript/notes: [paste your notes or transcript]
Generate unit tests for the following function. Framework: [Jest / Vitest / pytest / RSpec / other] Test cases to cover: - Happy path - Empty/null input - Boundary values (min/max) - Invalid type inputs - Any async behaviour (if applicable) For each test: a descriptive name, the Arrange/Act/Assert structure, and a one-line comment on why this case matters. Function: ``` [paste your function] ```
Write 3 product description variants for the following product: Product name: [name] What it does: [one sentence] Key features: [list 3–5] Target customer: [describe] Where it will appear: [website product page / Amazon listing / ad copy / email] Variant A — Feature-led (what it does) Variant B — Benefit-led (how it improves the buyer's life) Variant C — Storytelling (opens with the problem the buyer is solving) Each variant: 60–100 words, no filler phrases ('game-changing', 'revolutionary', 'unique').
Rewrite my resume bullet points to be more impactful. Apply this framework: [action verb] + [specific task or project] + [quantified result or impact]. For each bullet: 1. The rewritten version (strong action verb, specific, quantified where possible) 2. A note on what I should fill in to add a number (if the original had no metric) 3. A confidence rating: does this bullet now make the shortlist? My current bullets: [paste your bullets — one per line]
Write a regular expression for the following pattern-matching task: Task: [describe what you want to match — e.g. 'match all valid UK postcodes', 'extract all URLs from text', 'validate an email address'] Language/context: [JavaScript / Python / SQL / text editor / other] Edge cases to handle: [list any specific cases, e.g. 'must allow hyphens but not underscores'] Return: 1. The regex 2. A plain English explanation of each part 3. 3 test cases: one match, one near-miss (should NOT match), one edge case
Help me respond to the following feedback professionally. The goal is to acknowledge the concern, show I understand it, and commit to a specific next step — without being defensive or over-apologetic. Feedback received: [paste the feedback] Context: [is this a client complaint, a code review comment, a performance review, a user complaint?] What I actually think: [honest reaction — agree partially / fully / disagree] What I can realistically commit to: [describe] Write 2 versions: one where I broadly agree and commit to change, one where I partially disagree and explain my reasoning respectfully.
Create a structured 4-week learning plan for the following goal: Skill/topic to learn: [what do you want to learn?] Current level: [complete beginner / some background / intermediate] Available time per week: [hours] Preferred learning style: [video / reading / hands-on / all] Goal: [what should I be able to do at the end of 4 weeks?] Plan format: - Week 1–4, each with: a focus theme, 2–3 specific resources (type + topic), and one hands-on exercise - A 'checkpoint test' question for each week so I can verify I've understood the material - The single most common mistake beginners make at each stage
Write a SQL query for the following task: Database: [PostgreSQL / MySQL / SQLite / BigQuery / other] Task: [describe what data you want — e.g. 'find all users who signed up in the last 30 days but have never made a purchase'] Relevant tables and key columns: [describe your schema or paste CREATE TABLE statements] Return: 1. The complete query 2. A plain English walkthrough of the logic 3. Any indexes you should add to make this query fast on large tables
Rewrite the following text in [formal / casual / academic / sales-y / empathetic / technical] tone. Keep the core meaning identical — change only the register, vocabulary, and sentence structure. For each sentence you change significantly, add a brief inline note explaining the key shift. Original text: [paste your text]
Generate a FAQ section for the following product, service, or topic: Subject: [describe what you're writing FAQs for] Audience: [who will read these — first-time visitors, technical users, enterprise buyers?] Number of FAQs: [6 / 8 / 10] Focus: [pre-purchase objections / technical setup / pricing / general curiosity] For each FAQ: - A natural, search-friendly question (written as users actually type it, not corporate language) - A concise, direct answer (50–80 words) - Confidence: will this answer make a hesitant customer convert? (Yes / Needs evidence / Needs shortening)
Interpret the following chart or graph for a non-technical stakeholder. Do not just describe what the chart shows — analyse what it means. Chart description or data: [describe the chart or paste the data it represents] Audience: [who will read this interpretation — CEO / investor / customer / student?] Deliver: 1. ONE SENTENCE HEADLINE — the single most important finding, written as a newspaper headline 2. THREE KEY OBSERVATIONS — the most significant trends, in plain language 3. THE SURPRISING THING — what a casual viewer might miss or misread 4. THE RECOMMENDED ACTION — what should someone do with this information?
Generate 20 name options for the following: What it is: [product, company, feature, campaign, event — describe in one sentence] Target audience: [who needs to connect with this name?] Tone/feeling to evoke: [e.g. trustworthy, playful, premium, technical, accessible] Names to avoid (too similar to competitors): [list any] Domain/trademark constraints: [.com preferred? No existing trademarks?] Organise the 20 names into 4 groups: - DESCRIPTIVE (says what it does) - EVOCATIVE (creates a feeling) - INVENTED (made-up word or blend) - METAPHOR (borrows meaning from something else) For each name: a one-sentence rationale.
Proofread the following text. Fix all grammar, spelling, and punctuation errors. Flag (but do not automatically fix) any sentences that are unclear or could be misread. For each change: - Show [original] → [corrected] - For flagged sentences: explain the ambiguity and offer 2 alternative phrasings Do NOT change: the author's voice, vocabulary choices (even unusual ones), or sentence structure unless it's grammatically wrong. Text to proofread: [paste your text]
How GPT-6 Luna compares across the key dimensions for everyday use.
| Model | Best For | Speed | Reasoning | Cost |
|---|---|---|---|---|
| GPT-6 Luna ✓ | Everyday tasks, high-volume API, fast drafting | Very fast | Standard | Low |
| GPT-6 Sol | Complex analysis, structured long documents | Fast | Deep | Medium |
| GPT-6 Terra | Frontier tasks, full codebases, red-teams | Standard | Maximum | High |
| Claude Sonnet 5 | Reasoning-heavy tasks, coding, agentic workflows | Fast | High (xhigh mode) | Low–Medium |
| Gemini 3.8 Flash | Fast multimodal tasks, Google ecosystem | Very fast | Standard | Low |
GPT-6 Luna is OpenAI's lightweight tier within the GPT-6 family, released September 22, 2026. The GPT-6 family introduced a tiered naming system: Luna is the fast, affordable entry point; Sol is the mid-tier; Terra is the flagship. GPT-6 Luna is positioned to replace GPT-4o-mini for everyday tasks — it is faster and cheaper than Sol or Terra while significantly outperforming the GPT-5 generation on standard benchmarks for writing, coding, and analysis.
GPT-6 Luna is the next generation after GPT-5.5. Even as the lightweight GPT-6 tier, Luna outperforms GPT-5.5 on most everyday tasks — coding assistance, writing, summarisation, and question answering — at comparable or lower cost. The key differences: Luna is faster at inference, has better instruction following on structured prompts, and produces more consistent output format. For tasks requiring maximum reasoning depth, GPT-6 Sol or Terra (or Claude Opus 5.5 / Fable 5) are better choices.
GPT-6 Luna is optimised for speed and cost-efficiency on everyday tasks: drafting emails and documents, code review and simple fixes, blog post outlines, FAQ generation, product descriptions, resume editing, meeting summaries, data interpretation, learning plans, and SQL queries. It handles these tasks faster and at lower cost than GPT-6 Sol or Terra, making it the right default for high-volume applications or workflows where you do not need frontier reasoning depth.
All 20 prompts on this page are free to copy and paste. GPT-6 Luna is available in ChatGPT (free tier with usage limits, Plus for priority access) and via the OpenAI API. As the lightweight tier, Luna has the lowest per-token cost in the GPT-6 family, making it suitable for high-volume API use cases. Check the OpenAI pricing page for current rates.
GPT-6 Luna's exact context window specifications were published at launch in September 2026. The GPT-6 family generally improved on GPT-5.5's 128K context window. For tasks requiring very long context (hundreds of pages), Claude Opus 5.5 or Gemini 4's 1M token window may be more suitable. For everyday tasks — emails, standard code review, short documents — GPT-6 Luna's context is more than sufficient.
Use GPT-6 Luna when speed, cost, and everyday task quality matter most — it is the right default for most ChatGPT use cases. Step up to GPT-6 Sol when you need deeper reasoning, longer-form structured documents, complex analysis, or tasks where Luna's output consistently falls short. GPT-6 Terra (and Claude Opus 5.5 / Fable 5) are for frontier tasks that require maximum reasoning depth.
Previous OpenAI flagship — 20 prompts
Anthropic's best-value model — 20 prompts
Frontier reasoning tasks — 20 prompts
Google's flagship multimodal AI — 20 prompts
Fast Google AI with coding focus — 20 prompts
Microsoft's first reasoning model — 20 prompts