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OpenAI GPT-6 Family · Terra 1.1 · Released October 26–27, 2026

GPT-6.1 Terra Prompt Generator

20 free GPT-6.1 Terra prompts for visual reasoning, computer use, fast writing, and data extraction. OpenAI's fastest GPT-6.1 model — 3× quicker than Sol at half the cost. No signup.

3× Faster Than Sol Precise Computer Use Visual Reasoning $1/$5 Per MTok New — Oct 26 2026

What Is the GPT-6.1 Terra Prompt Generator?

This page gives you 20 copy-paste prompts built for GPT-6.1 Terra — OpenAI's mid-tier model in the GPT-6.1 family, released on October 26–27, 2026. GPT-6.1 Terra is positioned for tasks where speed matters: it runs roughly three times faster than GPT-6.1 Sol and GPT-6 Astra, at $1/$5 per MTok (half Sol's cost). Its core strengths are visual reasoning — interpreting screenshots, charts, forms, and images with precision — precise computer use for UI navigation and automation, and clear, natural writing for everything from emails to API docs.

The prompts below are structured for Terra's actual strengths. Each one includes bracketed placeholders — fill them in and paste directly into ChatGPT Work, Codex, or the API (gpt-6.1-terra). They work on GPT-6.1 Sol and GPT-6 Astra too, but Terra's speed and pricing make it the default choice for production workflows that don't need Sol's deep agentic coding depth.

GPT-6.1 Terra at a Glance

3×
Faster Than Sol

Average task speed vs GPT-6.1 Sol and GPT-6 Astra

$1/$5
Per MTok

Input/output pricing — half of GPT-6.1 Sol's $2/$10

1.05M
Context Window

Full long-context support — same as GPT-6.1 Sol

$0.05
Cached / MTok

Lowest cached-input rate in the GPT-6.1 family

20 Free GPT-6.1 Terra Prompts — Copy & Paste

Built for computer use, visual reasoning, and fast professional writing. Works on GPT-6.1 Terra, Sol, and Astra.

C
Computer Use

Computer Use — App Navigation

Open [application name] on my desktop and complete the following task: Task: [describe exactly what you want done — e.g. 'Export all rows from the Customers tab of the spreadsheet as a CSV file named customers_export.csv to the Downloads folder'] Constraints: - Do not open any browser tab or application not required for this task - If a dialog or prompt appears that is ambiguous, stop and describe what you see rather than guessing - Confirm each major step before proceeding to the next When complete, describe: what you did, which files were affected, and the exact state of the application when you finished.

S
Visual Reasoning

Screenshot Data Extraction

Extract all structured data from this screenshot. [attach screenshot] Output format: [table / JSON / bullet list / CSV] Fields to extract: [list the specific fields — e.g. name, date, amount, status] If a field is partially visible or cut off: include it with a note that it is truncated If text is ambiguous due to image quality: flag it with a confidence marker (HIGH / MEDIUM / LOW) Return the extracted data in the requested format, followed by a one-line summary of what the screenshot shows and any data quality issues I should be aware of.

U
Computer Use

UI Bug Report from Screenshot

Analyse this screenshot of a UI and write a complete bug report. [attach screenshot] Product context: [what product or page is this — e.g. 'our SaaS dashboard's billing page'] Expected behaviour: [what should this UI look like or do?] Browser/device context: [e.g. Chrome 130, macOS, 1440px viewport] Bug report format: 1. TITLE (one sentence, starts with the affected component) 2. SEVERITY (Critical / High / Medium / Low) with rationale 3. STEPS TO REPRODUCE (numbered, precise enough that a developer could follow them) 4. ACTUAL BEHAVIOUR (what the screenshot shows is happening) 5. EXPECTED BEHAVIOUR (what should happen instead) 6. LIKELY CAUSE (your hypothesis — CSS, state management, API response, etc.) 7. SUGGESTED FIX (the minimal change most likely to resolve it)

F
Writing

Fast Document Summary

Summarise the following document at three levels of detail. Document: [paste or attach the document] Audience: [who will read this summary — exec / customer / developer / general?] Level 1 — EXECUTIVE (3 bullets, each under 20 words): the single most important fact from each major section Level 2 — OPERATIONAL (one paragraph, 100–150 words): what decision or action this document supports, who is responsible, and the key deadline or constraint Level 3 — DETAILED (structured outline): headers matching the document's own structure, one or two sentences per section capturing the essential content Flag any section where the source material is ambiguous, contradictory, or missing information a reader would need.

W
Computer Use

Web Form Automation Script

Write a Playwright automation script to fill and submit the following web form. Form URL: [the page URL] Form purpose: [what the form does — e.g. 'new user registration', 'contact inquiry'] Fields and values: [list each field label and the value to enter — e.g. 'First Name: John', 'Email: test@example.com'] Submit button: [describe the button text or selector] Success condition: [what appears on screen after successful submission] Script requirements: - Use Playwright with async/await - Add a 500ms wait after each field to avoid race conditions - Screenshot on success and on any error - Wrap in a try/catch that logs the failing field and the error message - Parameterise the field values so the script can be reused with different data

C
Writing

Clear Natural Writing — Draft

Write the following piece of content in clear, natural prose. Content type: [email / announcement / help article / product description / blog post intro / other] Audience: [who will read this — customers / employees / developers / general public?] Tone: [professional / friendly / direct / empathetic — pick one] Length: [target word count or range] Key information to include: - [fact or point 1] - [fact or point 2] - [fact or point 3] What the reader should do or know after reading: [the single call to action or takeaway] Write the full piece. No filler phrases ('In today's fast-paced world…'), no clichés, no bullet points unless the format explicitly calls for them. Sentences should be short enough to read at a glance.

A
Visual Reasoning

Accessibility Description for Image

Write an alt-text description and an extended image description for the following image. [attach image] Context: [where this image appears — e.g. 'hero image on our product homepage', 'thumbnail for a news article about climate policy'] Audience: [who might rely on this description — screen reader users, low-vision readers, SEO crawlers?] Output two descriptions: 1. ALT TEXT (under 125 characters): the essential information a sighted user gets at a glance — what is shown, not what the image is called 2. EXTENDED DESCRIPTION (2–4 sentences): spatial layout, key colours or visual contrasts, any text visible in the image, and the mood or context the image conveys Avoid starting with 'Image of' or 'Photo of'. Describe, don't label.

E
Writing

Email Response — Fast Draft

Draft a response to the following email. Original email: [paste the email you received] My intended response in plain terms: [summarise what you want to say in 1–3 bullet points — don't worry about wording] Tone: [formal / friendly / direct — pick one] Length: [short (under 100 words) / medium (100–200 words) / full reply (no limit)] Anything to avoid: [topics, commitments, or phrasing you don't want in the reply] Write the full email. Subject line if replying to a thread: Re: [original subject]. No placeholder text. If you need a piece of information to complete the reply that I haven't given you, ask for it rather than making something up.

P
Writing

Product Description — E-Commerce

Write a product description for the following item. Product name: [exact product name] Category: [what type of product it is] Key features: - [feature 1] - [feature 2] - [feature 3] [add more as needed] Target customer: [who buys this — describe in one sentence] Primary use case: [what problem does it solve, or what occasion is it for?] Selling platform: [Amazon / Shopify / own website / other] Length: [short (under 80 words) / standard (80–150 words) / long (150–300 words)] Write the full description. Lead with the benefit, not the feature. Use active voice. End with a sentence that makes the buyer feel confident about the purchase. Include the product name in the first sentence for SEO.

C
Writing

Changelog Entry Writer

Write a public changelog entry for the following product update. Product: [product name] Update type: [new feature / improvement / bug fix / deprecation] What changed (technical): [describe in developer-level detail] Who is affected: [all users / specific plan / specific integration] Release date: [date] Link to docs (optional): [URL if applicable] Changelog entry format: - TITLE: [emoji] [short title — under 60 characters] - SUMMARY (2–3 sentences): what changed, why it matters to the user, and what they can do now that they couldn't before - DETAILS: one or two bullet points for the most important technical specifics - ACTION REQUIRED (if any): what the user needs to do to benefit from this change Tone: clear and factual. Avoid marketing language. Assume the reader is technical.

M
Writing

Meeting Notes Formatter

Format the following raw meeting notes into a clean, structured summary. Raw notes: [paste your unedited notes — bullet points, fragments, timestamps are fine] Meeting context: [meeting name, date, attendees if relevant] Audience for the summary: [who will read this — the same team / a manager / a client?] Output format: 1. MEETING PURPOSE (one sentence) 2. KEY DECISIONS MADE (bullet list — only final decisions, not discussion) 3. ACTION ITEMS (each as: WHAT → WHO → BY WHEN) 4. OPEN QUESTIONS (unresolved items that need a follow-up) 5. NEXT MEETING (if mentioned) Do not invent information not in the raw notes. If a decision or owner is unclear, mark it with [TBC] rather than guessing.

A
Writing

API Documentation Writer

Write documentation for the following API endpoint. Endpoint: [HTTP method + path — e.g. 'POST /api/v2/orders'] Purpose: [what this endpoint does in one sentence] Authentication: [Bearer token / API key header / OAuth2 / other] Request body / parameters: [describe each field: name, type, required/optional, description, example value] Successful response: [describe the response shape: fields, types, example JSON] Error responses: [list each error code, when it occurs, and what it means] Document the endpoint in this format: - Overview (2–3 sentences) - Authentication note - Request parameters table (name | type | required | description) - Example request (curl) - Example response (JSON) - Error codes table (code | description | resolution) - Notes or rate limits if applicable

D
Computer Use

Desktop Screenshot → Instructions

Look at this screenshot of a desktop or web application and write step-by-step instructions for completing the visible task. [attach screenshot] Target audience for the instructions: [technical user / non-technical user — pick one] Task to complete: [what the user is trying to accomplish — e.g. 'export the data as a CSV'] Level of detail: [brief (3–5 steps) / thorough (full numbered steps with UI element names)] Write the instructions as if the reader has never used this application. Use the exact names of buttons, menus, and fields as they appear in the screenshot. If a step involves clicking a specific UI element, describe it precisely (e.g. 'click the blue Export button in the top-right corner' rather than 'click Export').

F
Coding

Fast Code Generation

Write the following code. Keep it clean, correct, and minimal — nothing extra. Language: [language + version if relevant] Task: [describe what the code must do in one or two sentences] Inputs: [what data goes in — types and examples] Outputs: [what the code returns or produces] Constraints: [no external libraries unless specified / must be async / must handle X edge case / other] When done: 1. Paste the complete, runnable code 2. Add a two-line comment above each function explaining WHAT it does and WHY (not how) 3. Identify the one input case most likely to break this code and add a guard for it

V
Visual Reasoning

Visual Layout Comparison

Compare these two screenshots and describe the differences. [attach Screenshot A] [attach Screenshot B] Context: [what these screenshots show — e.g. 'before and after a UI redesign', 'two versions of a landing page', 'competitor A vs competitor B'] Focus areas: [what to prioritise in the comparison — layout / typography / colour / content / spacing / navigation] Comparison output: 1. LAYOUT DIFFERENCES: how the overall structure and hierarchy changed 2. VISUAL CHANGES: colour, typography, spacing, imagery 3. CONTENT CHANGES: what text, labels, or elements appear, disappear, or move 4. UX IMPACT: which version is clearer for the user and why — one paragraph 5. RECOMMENDATION: if choosing one, which and the single strongest reason

C
Writing

Content Repurposing Brief

Repurpose the following content for a different format and audience. Source content: [paste the original — article, transcript, report, post, etc.] Source format: [what the original is — blog post / podcast transcript / whitepaper / other] Target format: [what to turn it into — LinkedIn post / tweet thread / email newsletter / FAQ / press release / slide deck outline] Target audience: [who will read the output — different from the source audience? describe the shift] Tone shift (if any): [e.g. 'from technical to accessible', 'from formal to conversational'] Length target: [word count or post count] Produce the repurposed content in full. Do not add information not in the source. If the source is too thin for the target format, list what additional content would be needed to complete it properly.

D
Visual Reasoning

Data Table from Image

Convert the data visible in this image into a clean, structured table. [attach image — chart, handwritten table, printed form, screenshot of a spreadsheet, etc.] Output format: [Markdown table / CSV / JSON array of objects / HTML table] Date or time format to use: [ISO 8601 / MM/DD/YYYY / other] Numeric format: [preserve original / convert to integers / convert to float] Missing or illegible cells: [use null / leave blank / flag with '?'] After the table, note: - Total row count extracted - Any cells flagged as uncertain and why - Any data that appeared in the image but could not be mapped to the table structure

H
Writing

Help Centre Article

Write a help centre article for the following topic. Product: [product name] Topic: [what the article explains — e.g. 'How to reset your password', 'Understanding billing cycles'] Target reader: [paying customer / free user / admin / developer] Prerequisites (if any): [what the reader should already know or have done before following this article] Article format: 1. TITLE: [action-oriented — starts with 'How to' or 'Understanding' or 'Setting up'] 2. OVERVIEW (2 sentences): what this article covers and who it is for 3. BEFORE YOU BEGIN (optional): any prerequisites or settings to check first 4. STEPS: numbered, each under 20 words, starting with an action verb 5. RESULT: what the reader sees or can do when they have followed all steps 6. TROUBLESHOOTING (2–3 items): the most common problems and their fixes 7. RELATED ARTICLES: [leave as a placeholder list] Tone: patient, factual, no jargon beyond what the product itself uses.

S
Writing

Slide Deck Outline

Create a slide-by-slide outline for the following presentation. Presentation topic: [what the deck is about] Audience: [who will see this — board / team / client / conference] Presentation length: [how many minutes] Goal: [what the audience should think, feel, or do after the presentation] Key information to cover: - [point 1] - [point 2] - [point 3] [add more as needed] For each slide: - SLIDE NUMBER and TITLE - VISUAL SUGGESTION (one line: what kind of visual — data chart, photo, diagram, icon grid, etc.) - SPEAKER NOTES (2–3 sentences the presenter says, not reads) - SLIDE CONTENT (bullet points or short sentences — max 6 items per slide, each under 10 words) Target: [number] slides. Last slide must be a clear call to action or next step.

C
Visual Reasoning

Competitor Feature Comparison

Analyse these competitor screenshots and build a feature comparison table. [attach Screenshot 1 — Product A] [attach Screenshot 2 — Product B] [attach Screenshot 3 — Product C (optional)] Our product: [describe your product and its key differentiators] Comparison categories: [list the dimensions to compare — e.g. pricing, onboarding, navigation, key features visible in screenshots] Output: a Markdown table with one row per feature and one column per product After the table: 1. GAPS (features competitors have that we don't, visible in the screenshots) 2. ADVANTAGES (features we have that competitors don't, based on your description) 3. ONE RECOMMENDATION: the single highest-impact feature gap to close first, and why

GPT-6.1 Terra vs GPT-6 Family (2026)

How GPT-6.1 Terra fits in the GPT-6 lineup for speed, visual tasks, and cost.

Model Best For Speed Context Cost
GPT-6.1 Terra ✓ New Computer use, visual reasoning, fast writing 3× faster than Sol 1.05M tokens $1/$5 MTok
GPT-6.1 Sol Deep agentic coding, multi-agent orchestration Standard 1.05M tokens $2/$10 MTok
GPT-6 Astra Frontier reasoning, computer use, max depth Slower 1M+ tokens High
GPT-6 Luna High-volume, affordable everyday tasks Fastest Standard Low
Claude Sonnet 5.5 Fast agentic coding, visual tasks Fast 1M tokens $3/$15 MTok

GPT-6.1 Terra Prompting Tips

Do This:

  • Use Terra as your default for computer use tasks — its visual reasoning and speed make it the most cost-effective choice for UI automation
  • Attach screenshots directly — Terra extracts structured data from images with high accuracy; don't transcribe when you can attach
  • Set reasoning_effort: "none" to regain temperature/top_p control for deterministic extraction tasks
  • Leverage the $0.05/MTok cached input rate for agents that repeatedly pass the same system prompt or application context
  • Use Terra for high-throughput writing tasks where Sol's extra cost isn't justified
  • Specify the exact output format (Markdown table, JSON, bullet list) — Terra returns clean structured output reliably

Avoid This:

  • Using Terra for the most complex multi-file coding refactors — GPT-6.1 Sol's deeper agentic coding is the better tool
  • Underspecifying computer use tasks — Terra needs to know exactly which UI element to interact with and how to confirm success
  • Expecting Terra to invent information not in the provided screenshot or document — it extracts and reasons; it does not fabricate
  • Skipping the "success condition" in automation prompts — always describe what correct completion looks like
  • Using Terra where Luna suffices — for pure text drafting without visual input, Luna is faster and cheaper still
  • Sending ambiguous screenshots without context — describe what the application is and what task you're trying to complete

GPT-6.1 Terra — Frequently Asked Questions

What is GPT-6.1 Terra?

GPT-6.1 Terra is OpenAI's mid-tier model in the GPT-6 family, released on October 26–27, 2026. It is positioned between GPT-6 Luna (lightweight, affordable) and GPT-6.1 Sol (deep agentic coding). Terra's defining strengths are fast, efficient visual reasoning — interpreting screenshots, images, and documents — precise computer use for UI automation tasks, and clear, natural writing. It runs roughly three times faster than GPT-6.1 Sol and GPT-6 Astra on average, making it the recommended model when speed and cost matter more than maximum reasoning depth.

How is GPT-6.1 Terra different from GPT-6.1 Sol?

Terra and Sol sit at different points in the GPT-6.1 tier: Sol is optimised for deep agentic coding and multi-agent orchestration, while Terra is optimised for speed, visual reasoning, and computer use. Terra processes tasks roughly 3× faster than Sol and Astra, making it the better choice for high-throughput workflows, real-time UI automation, and tasks where response latency matters. Its pricing also reflects this positioning: $1/$5 per MTok (input/output) versus Sol's $2/$10, and cached input at $0.05/MTok — making Terra significantly cheaper for production workloads that reuse context. For difficult multi-file refactors or long research chains that need Sol's coding depth, Sol remains the better choice.

What is GPT-6.1 Terra's pricing?

GPT-6.1 Terra is priced at $1.00 per million input tokens, $0.05 per million cached input tokens, and $5.00 per million output tokens via the OpenAI API. This makes it roughly half the cost of GPT-6.1 Sol ($2/$10 MTok) and substantially cheaper than GPT-6 Astra for sustained production workloads. The $0.05 cached-input rate is particularly valuable for computer-use agents that repeatedly pass the same application context or system prompt across many calls. Check the OpenAI pricing page for current rates, as these are subject to change.

What is GPT-6.1 Terra best used for?

GPT-6.1 Terra excels at three categories of tasks: (1) Computer use — navigating and operating desktop or web applications from screenshots, filling forms, clicking UI elements, and extracting data from screen captures; (2) Visual reasoning — interpreting charts, diagrams, screenshots, scanned documents, and image data with high accuracy and speed; (3) Clear writing — drafting emails, product descriptions, help articles, changelogs, and other structured documents that benefit from natural, fluent prose. Its speed advantage (3× faster than Sol) also makes it the default choice for high-volume batch tasks where near-frontier quality is needed quickly.

Where is GPT-6.1 Terra available?

GPT-6.1 Terra is available in ChatGPT Work and Codex for Plus, Pro, Enterprise, and Education users. Developers can access it via the OpenAI API using the model identifier gpt-6.1-terra. It was released on October 26–27, 2026, with a gradual rollout to eligible ChatGPT subscribers. In the API, it is available as a named model endpoint alongside gpt-6.1-sol, gpt-6-sol, gpt-6-luna, and gpt-6-astra. Pricing and availability for specific regions should be confirmed on the official OpenAI website.

Does GPT-6.1 Terra support the none reasoning effort setting?

Yes. GPT-6.1 Terra supports setting reasoning effort to none, which restores the ability to configure temperature and top_p in the API — settings that are otherwise fixed when a reasoning effort level is specified. This is useful for developers who want precise control over output variation: for example, a temperature of 0 for deterministic data extraction tasks or a higher temperature for creative writing tasks. This setting is available in the OpenAI Responses API and the legacy Chat Completions API when targeting gpt-6.1-terra.

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