GPT-5.6 Terra: an everyday working model
GPT-5.6 Terra is the middle model of OpenAI's GPT-5.6 series, generally available since July 9, 2026. It sits between the Sol flagship and the economical Luna, priced on Genosai at 200 ₽ per 1M input and 1200 ₽ per 1M output tokens. In agentic coding tests Terra still holds its own against rival flagships.
Updated: July 23, 2026
- Price 200/1200 ₽ per 1M — The same as GPT-5.4, but a newer generation with higher benchmark results.
- Flagship-level coding — On the Artificial Analysis Coding Agent Index it edges out Claude Fable 5 at roughly a quarter of the estimated cost.
- 256,000-token context — A large document, a substantial code fragment, or a long conversation fits in one pass.
- Vision and files — Accepts images in JPG, PNG, and WebP formats, up to 10 files per request.
- Token efficiency — It solves the task with fewer tokens, so the final bill comes in below what the rate suggests.
Contents
- What is GPT-5.6 Terra
- Capabilities
- Examples prompt and response
- How to use on Genosai
- Prompts
- Generation cost
- How it compares
- Limitations and tips
- FAQ
What is GPT-5.6 Terra
GPT-5.6 Terra is the middle model of OpenAI's GPT-5.6 series. The series became generally available on July 9, 2026 and comes in three tiers: the Sol flagship, the balanced Terra, and the economical Luna. Terra is built for everyday work — coding, reasoning, and agentic tasks where capability has to be balanced against cost.
The headline of the whole series is "more intelligence per token." OpenAI bets not on record figures in themselves but on performance per dollar, and on the mid-tier models that approach shows most clearly. According to OpenAI, Terra and the smaller Luna outperform Claude Fable 5 at roughly one sixteenth of the cost. On the Artificial Analysis Coding Agent Index, Terra scores slightly above Fable 5 — at roughly a third of the time, half the output tokens, and about a quarter of the estimated cost.
In knowledge work Terra exceeds the peak-performance level of GPT-5.5, the previous flagship. That is arguably the most practical thing about the model: what recently required the top tier of the line is now handled by the middle one.
On Genosai, Terra has a 256,000-token context window and image support. The client price is 200 ₽ per 1M input and 1200 ₽ per 1M output tokens, exactly what the previous-generation GPT-5.4 costs. The gain is direct: the price has not changed and the generation is newer.
Capabilities
GPT-5.6 Terra is an all-rounder: text, code, analysis, and image work at the same level.
Everyday code and development
Terra writes and reviews code, explains its edits, walks through edge cases, and plans steps. Its Artificial Analysis Coding Agent Index result sits above Fable 5 — that is, at the level of strong rival flagships but for a fraction of their cost. For a developer's typical work that is enough: refactoring, tracking down a failure, covering edge cases, making sense of someone else's code. A flagship is worth switching on where the logic is long and dependent; on ordinary tasks the quality difference does not justify the price difference.
Text and knowledge work
The model writes articles, emails, documentation, and structured answers, holding tone and the requested length. It turns scattered material — documents, correspondence, notes — into a coherent result. In knowledge work Terra exceeds the peak-performance level of the previous flagship GPT-5.5, which is unusual for a mid-tier model and explains why it deserves to be treated as a workhorse rather than a compromise.
Agentic scenarios
The GPT-5.6 series supports programmatic tool calling: the model writes and runs lightweight programs that orchestrate tools, filter intermediate data, and choose the next action as work proceeds. Fewer tokens, fewer model calls, less hand-holding at each step. Terra inherits this mechanism, so it handles tasks of the form "work through this material, compare the options, and propose a solution with justification."
Vision and files
Terra accepts images in JPG, PNG, and WebP formats, up to 10 files per request. You can attach an interface screenshot and ask it to describe the problem, hand over a diagram for analysis, or pull text from a photo of a document. This pairs well with a text task: attach a mockup and ask for copy to match it.
Examples prompt and response
Below are real GPT-5.6 Terra responses produced on Genosai. The first is a content plan, the second explains marketing metrics. Texts are quoted verbatim, with the long answer shortened.
Prompt: Draft a Telegram post outline for launching a new cosmetics line: 5 bullet points with short subheadings.
GPT-5.6 Terra response:
- **Meet the New Line**
Introduce the collection, its name, and the inspiration behind it.
- **Hero Products**
Highlight 2–3 standout items and their key benefits.
- **Made for Your Routine**
Explain skin types, finishes, shades, or everyday use cases.
- **Ingredients & Values**
Share notable formulas, cruelty-free status, sustainability, or dermatological testing.
- **Launch Details**
Include release date, pricing, where to shop, and a clear call to action._Generated on Genosai.io with GPT-5.6 Terra._
Prompt: Explain in simple terms the difference between ROAS and ROMI in online advertising, with a numeric example.
GPT-5.6 Terra response:
**ROAS** and **ROMI** both measure advertising performance, but they answer slightly different questions.
### ROAS: Return on Ad Spend
ROAS compares **revenue generated** to **ad spend**.
Example:
- You spend **$1,000** on Google Ads.
- Those ads generate **$5,000** in sales revenue.
So your ROAS is **5:1** (or **500%**).
That means: for every **$1 spent on ads**, you generated **$5 in revenue**.
**Limitation:** ROAS does not account for the cost of the product, shipping, staff, agency fees, discounts, or other marketing costs. High ROAS does not automatically mean high profit._Generated on Genosai.io with GPT-5.6 Terra._
How to use on Genosai
- Open the model catalog on Genosai and pick GPT-5.6 Terra among the text models.
- Describe the task in the chat: goal, tone, format, and answer length.
- Attach files if needed — a document, data, or an image — and refer to them in the request.
- Send the request and read the answer as it streams, without waiting for the full generation.
- Refine in the same thread: ask to shorten, add an example, or restructure.
Terra works well as a default model: strong enough that you are not switching to the flagship at every turn, and cheap enough to make dozens of calls a day. Genosai runs in the browser with no installation, signing up takes a minute, and billing is by actual usage, so you can start with a single request and judge the model on your own task before wiring it into a routine process. Chat history is saved, so an analysis you started is easy to continue later in the same thread.
Prompts
The prompts below play to Terra's strengths: careful code, document work, and structured analysis. The general principle is to specify not only the topic but the structure of the answer: sections, number of points, table format. The model holds those constraints well and returns a result that is ready to use without rework.
Refactor this function: add edge-case handling and type hints. Explain each change.Read the attached document and build a 7-point summary. Preserve all numbers and dates.Write a 600-word technical article on the difference between synchronous and asynchronous code, with examples.Compare the two approaches in my text and build a pros-and-cons table. Four points for each.Draft a 6-stage project plan with dependencies between the stages. Format: numbered list.Analyze the attached screenshot and describe what is happening and which actions are available to the user.Check the text for factual errors and flag the places that need a source check.Generation cost
On Genosai, GPT-5.6 Terra is billed by tokens — you pay for the actual size of the request and the response, so a short question costs less than a long analysis. The client price is 200 ₽ per 1M input tokens and 1200 ₽ per 1M output tokens.
The comparison with the flagship is more telling than any benchmark: Sol costs 400/2400 ₽ per 1M, twice as much. The sensible strategy is to keep Terra as the default and bring in Sol selectively, only for tasks where you hit a ceiling. On that split, monthly spend differs twofold while quality on most tasks barely does.
A starter balance after sign-up lets you try GPT-5.6 Terra for free. Current rates and your balance are in the Pricing section.
How it compares
Terra is the middle of the GPT-5.6 series and a sensible default: quality close to the top step, price close to the bottom one.
| Model | Context (Genosai) | Price ₽/1M (in/out) | Files | Niche |
|---|---|---|---|---|
| GPT-5.6 Terra | 256,000 | 200 / 1200 | yes | Balance of power and price |
| GPT-5.6 Sol | 256,000 | 400 / 2400 | yes | Flagship for hard tasks |
| GPT-5.6 Luna | 256,000 | 80 / 480 | yes | Economy and volume |
| GPT-5.4 | 380,000 | 200 / 1200 | yes | Same price, previous generation |
For genuinely complex agentic scenarios, step up to GPT-5.6 Sol. For high-volume tasks and classification, step down to GPT-5.6 Luna. The previous generation, GPT-5.4, costs the same 200/1200 ₽ per 1M and scores lower, but holds a 380,000-token context, which sometimes decides the matter. Among same-class rivals, compare Claude Sonnet 5 with its 800,000-token window.
A practical rule of thumb: start with Terra and switch only where you feel a limit. On Genosai all models are available in one account, so switching takes seconds and needs no separate subscriptions per model.
Limitations and tips
Terra's main limitation is context. On Genosai that is 256,000 tokens, whereas the previous-generation GPT-5.4 has a 380,000-token window and Claude Sonnet 5 has 800,000. If your task runs into the volume of material rather than the depth of reasoning, choose by context rather than by generation.
The higher-effort and parallel-agent modes OpenAI describes are features of its own API. In the Genosai studio the effort level is not exposed and the provider default applies, so plan around the model's ordinary behavior.
A practical tip: use file support and attach a document instead of pasting text into the prompt, which is both more precise and more convenient. For code, ask for the explanation along with the result: it makes it easier to spot when the model understood the task differently than you intended.
One more technique is to state quality criteria directly in the request. Describe which phrasings to avoid, what counts as a good answer, and which data must be preserved. The more precisely you describe the expected result, the fewer iterations you spend on rework. And if the topic needs fresh or exact data, ask the model to flag the places that need a source check.
FAQ
What is GPT-5.6 Terra?
It is the balanced model of OpenAI's GPT-5.6 series, generally available since July 9, 2026. It targets everyday coding, reasoning, and agentic work where capability has to be weighed against cost. In the API documentation it corresponds to the mini tier of earlier GPT-5 generations.
How does Terra differ from Sol and Luna?
Sol is the series flagship for the hardest tasks, Luna is the most economical for high-volume work, and Terra is the middle ground. On Genosai the Terra price per 1M tokens (input/output) is 200/1200 ₽ against 400/2400 ₽ for Sol and 80/480 ₽ for Luna. For most working tasks Terra is the sensible starting point.
What is the context window on Genosai?
Up to 256,000 tokens per request. That covers a large document, a substantial slice of a codebase, or an entire long conversation. The model also accepts images, up to 10 files at a time.
How much does a GPT-5.6 Terra request cost?
Billing is by tokens: for the client, 200 ₽ per 1M input tokens and 1200 ₽ per 1M output tokens, based on the actual size of the prompt and the response. That is exactly what the previous-generation GPT-5.4 costs.
How strong is Terra at coding?
On the Artificial Analysis Coding Agent Index it scores slightly above Claude Fable 5, at roughly a third of the time and about a quarter of the estimated cost. For everyday code, review, and refactoring that level is plenty.
When should you move up to Sol?
Move up to Sol when the task consists of many dependent steps and the cost of a mistake is high: a complex agentic scenario, a large codebase, a high-stakes analysis. Otherwise Terra delivers close quality at half the price.