GPT-6 Astra: OpenAI's flagship for complex, long-horizon work
GPT-6 Astra is the top model of OpenAI's GPT-6 series, built for deep analysis, software development, research, and document work. It has been available on Genosai since September 27, 2026, with a 256,000-token context, vision, tool calling, and adjustable reasoning effort. Billing is by tokens — 800 / 4000 ₽ per 1M tokens (input / output), with no subscription.
Updated: September 27, 2026
- Long-horizon agentic tasks — According to OpenAI, Astra is especially strong in multi-step scenarios that require computer and browser use.
- Deep analysis and research — It breaks down hard questions, turns materials into conclusions, and keeps its logic across a long chain of reasoning.
- Development and scientific work — It writes and reviews code, explains bugs, and helps with calculations and the structure of scientific texts.
- 256,000-token context — A lengthy contract, a report, or a large slice of a codebase fits into a single request.
- Vision and tools — Accepts up to 10 JPG, PNG, and WebP images per request and supports function calling through the API.
- Reasoning effort — Four levels — low, medium, high, xhigh — let you balance speed, cost, and accuracy.
Contents
- What is GPT-6 Astra
- Capabilities
- Examples prompt and response
- How to use on Genosai
- Prompts
- Generation cost
- How it compares
- Limitations and tips
- FAQ
What is GPT-6 Astra
GPT-6 Astra is the flagship model of OpenAI's GPT-6 series. The series comes in three tiers: Astra is the top model for the hardest tasks, GPT-6 Sol sits in the middle, and GPT-6 Luna is fast and economical. This lineup lets you match the model to the task instead of paying for the maximum where it is not needed.
OpenAI positions Astra for complex, comprehensive work: deep analysis, software development, deep research, scientific work, and document creation. The provider singles out long-horizon agentic tasks — scenarios where the model works through many steps in a row using a computer and a browser, plans, checks intermediate results, and carries the job through to the end.
The model has been available on Genosai since September 27, 2026. It has a 256,000-token context window, image input (JPG, PNG, and WebP, up to 10 files per request), text output, and function calling. There is no built-in web search: the model relies on its own knowledge and on the materials you provide.
On Genosai you do not need a subscription: you pay only for the tokens you use — 800 / 4000 ₽ per 1M tokens (input / output).
Capabilities
Astra shows its strength on tasks where depth and care over a long stretch matter.
Deep analysis and calculations
The model works through business problems step by step: it computes metrics, checks assumptions, and separates facts from scenarios. In the example below it did not just return a number — it noted on its own that each effect was calculated independently and excluded implementation costs. That is exactly the behavior you want in analytics. The approach is useful for unit economics, financial models, report reviews, and preparing decisions for management.
Software development
Astra writes code, finds bugs, and explains how they happen. It notices edge cases — empty data, invalid input, boundary values — and suggests ways to handle them based on what you actually need. Function calling lets you build the model into your own automations: it decides which tool to call and with which parameters.
Documents and structuring
The model turns raw notes, correspondence, and transcripts into clean documents: minutes, briefs, policies, technical specifications. A notable trait is that Astra is honest about what the source lacks: a missing deadline, an unassigned owner, an unclear currency. Instead of inventing details, it moves those gaps into open questions.
Large materials and images
The 256,000-token window fits a lengthy contract, a body of policies, or a large portion of a codebase. Together with the text you can pass up to 10 images: interface screenshots, scanned tables, charts. The model relates the images to the text and answers based on the whole material rather than a conveniently chosen fragment.
Long-horizon agentic scenarios
According to OpenAI, Astra's main strength is long-horizon agentic work involving computer and browser use. On Genosai this is available through the API: you connect your own tools through function calling, and the model plans the steps and calls them. In the studio chat, Astra works as a strong partner for multi-step reasoning.
Examples prompt and response
Below are real GPT-6 Astra responses produced on Genosai with reasoning effort set to medium. The first is a business calculation, the second a bug fix, and the third meeting minutes built from rough notes. Texts are quoted verbatim; the markdown fences around the code were removed and the long answer is shortened, with cuts marked "…".
Prompt: An online store: 12,000 monthly visitors, 1.5% conversion rate, average order value $45, 35% margin, ad spend $1,200. Calculate profit after ads and suggest 3 growth levers with the dollar effect of each. Keep it short, under 150 words.
GPT-6 Astra response:
**Monthly profit after ads: $1,635** (before other operating costs).
- Orders: 12,000 × 1.5% = **180**
- Revenue: 180 × $45 = **$8,100**
- Gross profit: $8,100 × 35% = **$2,835**
- After ads: $2,835 − $1,200 = **$1,635**
**Three growth levers** — illustrative monthly effects, each calculated independently:
1. **Improve conversion to 2%** through better product pages and checkout: 60 extra orders → **+$945 profit**.
2. **Raise average order value to $50** with bundles or upsells: $900 extra revenue → **+$315 profit**.
3. **Increase margin to 40%** through supplier negotiations or pricing: **+$405 profit**.
Assumes other metrics and ad spend stay constant; implementation costs are excluded._Generated on Genosai.io with GPT-6 Astra._
Prompt: Find the bug in this Python function and give a fixed version. Explain briefly, under 120 words.
def average_check(orders):
total = 0
for o in orders:
total += o["amount"]
return total / len(orders)
GPT-6 Astra response:
The function raises `ZeroDivisionError` when `orders` is empty. Handle that case explicitly. This version returns `0` for no orders:
def average_check(orders):
if not orders:
return 0
total = 0
for o in orders:
total += o["amount"]
return total / len(orders)
This assumes `orders` is a list-like collection and each order has a numeric `"amount"`. If an empty collection should be considered invalid, raise `ValueError` instead of returning `0`._Generated on Genosai.io with GPT-6 Astra._
Prompt: Turn these meeting notes into structured minutes: decisions, action items with owners and deadlines, open questions. Notes: landing page launch moves to October 14; Mary finishes the copy by Friday; Oleg handles the CRM integration but needs access from IT; ad budget 150k, discuss with the CFO, may increase; A/B test on the headline: yes; who makes the banners: not decided.
GPT-6 Astra response:
## Meeting Minutes
### Decisions
- **Landing page launch:** Rescheduled to **October 14**.
- **Headline A/B test:** Approved.
### Action Items
| Action | Owner | Deadline | Notes |
|---|---|---|---|
| Finalize landing page copy | Mary | Friday | Exact date not specified in the notes. |
| Complete CRM integration | Oleg | Not specified | Requires access from IT. |
| Discuss the advertising budget with the CFO | Not assigned | Not specified | Budget noted as **150k**; may increase. Currency not specified. |
…
### Open Questions
- Who will create the banners?
- Who will discuss the advertising budget with the CFO, and will it increase above 150k?
…_Generated on Genosai.io with GPT-6 Astra._
How to use on Genosai
- Open the Genosai studio on the Text tab.
- Pick GPT-6 Astra in the model list.
- If needed, open the chat settings panel: temperature, top_p, maximum answer length (up to 32,768 tokens), reasoning effort, and a system prompt.
- Describe the task: goal, context, format, and answer length. Attach images if they help — up to 10 files.
- Send the request and refine the result in the same thread: ask it to recalculate, shorten, or restructure.
Signing up takes a minute, nothing needs to be installed, and chat history is saved. Billing is by tokens with no subscription.
Reasoning effort
The chat settings panel has a reasoning effort option that sets how much the model thinks before answering. Astra supports four levels: low, medium, high, and xhigh. The default is low. Astra has no none level: the flagship always reasons at least a little before it answers.
Reasoning tokens are billed as output tokens, so the level you choose directly affects speed and cost. Low and medium suit most everyday work. Switch to high or xhigh on genuinely hard problems — tricky logic, a stubborn bug, a calculation with many conditions — where the extra time pays off in accuracy.
Through the API
For developers, Genosai offers an OpenAI-compatible API. Send requests to POST https://api.genosai.io/v1/chat/completions with the model gpt-6-astra and your sdk_... key. Function calling (tools) and the reasoning_effort parameter are supported, with the same levels as in the studio. The list of available models is at GET /v1/models.
Prompts
The prompts below target Astra's strengths: multi-step logic, code, and document work. The general principle is to specify not only the topic but the shape of the result: sections, number of points, a table, a length limit. The model holds those constraints well.
Review the attached financial model: find the assumptions the result depends on most and estimate what happens if each one is off by 20%.Find the cause of the bug in this code, explain the mechanism, and propose a fix with edge-case handling.Write a technical specification from these notes: goal, requirements, acceptance criteria, open questions. Do not invent anything the notes do not say.Compare the three attached contracts: where do terms, liability, and termination conditions differ? Summarize in a table.Review the interface screenshots and list usability problems by priority, with a suggested fix for each.Generation cost
GPT-6 Astra is billed by tokens: 800 / 4000 ₽ per 1M tokens (input / output). You pay for the actual size of the request and the response, so a short question costs less than a long analysis. As a reference point, the business calculation in the first example took 109 input tokens and 269 output tokens including reasoning — about 1.2 ₽.
Two details affect the final bill more than the rate line does.
Reasoning effort. Reasoning tokens are billed as output tokens, at 4000 ₽ per 1M. The high and xhigh levels noticeably increase output, so use them where the task justifies it.
Prompt cache. A repeated beginning of a request — a system prompt, a long instruction, the same document — is cached automatically. Cached input costs 160 ₽ per 1M instead of 800 ₽. We tested this with a repeated prompt of about 7,300 tokens: on the second call, 7,168 tokens were read from the cache and the request cost about 4.6 times less. Cache hits are not guaranteed, but when you work with one large document across several requests the savings add up.
There is no subscription. Current rates and your balance are in the Pricing section.
How it compares
The main choice is within the GPT-6 series itself. Astra costs five times as much as Sol and a hundred times as much as Luna, so it is worth switching on deliberately.
| Model | Context (Genosai) | Price ₽/1M (in / out) | Niche |
|---|---|---|---|
| GPT-6 Astra | 256,000 | 800 / 4000 | Flagship for complex, long tasks |
| GPT-6 Sol | 256 000 | 160 / 800 | Balance of power and price |
| GPT-6 Luna | 256 000 | 8 / 40 | Speed and volume |
| GPT-5.6 Sol | 256,000 | 400 / 2400 | Previous-generation flagship |
| GPT-5.6 Terra | 256,000 | 160 / 960 | Everyday work |
| GPT-5.6 Luna | 256,000 | 16 / 96 | Economical tasks |
| Claude Opus 4.8 | 120,000 | 480 / 2400 | Anthropic's flagship |
For most everyday tasks, start with GPT-6 Sol: it is much cheaper and covers writing, document analysis, and ordinary code. For classification, short answers, and high-volume processing, GPT-6 Luna fits. If you already work with GPT-5.6 Sol, Astra is the next step for tasks where the previous flagship hits its limit. Among flagship-class rivals, compare Claude Opus 4.8.
Step up to Astra when a smaller model loses the thread in a long chain or simplifies where precision is required. All models live in one account, so switching takes seconds.
Limitations and tips
Astra's main limitation is price. On simple tasks you overpay for depth that goes unused: translation, short copy, or classification are handled confidently by Luna at a fraction of the cost.
There is no built-in web search. If the task needs fresh data — exchange rates, prices, news — pass it in the request yourself or connect a search tool through function calling in your own application.
High reasoning effort slows the answer down. At xhigh the model may think noticeably longer, so for interactive work start with low or medium and raise the level only if the result falls short.
A few practical tips:
- Describe the task as a whole rather than step by step. Astra's strength is long-range logic, and splitting the work into small errands takes away its main advantage.
- State quality criteria in the request: what counts as a good answer, which data must be preserved, what to avoid.
- For long documents, keep the unchanging part — the instruction and the document itself — at the start of the request. The cache then hits more often and requests cost less.
- Ask the model to flag its assumptions and the places where data is missing.
Most importantly, verify the output manually whenever important decisions depend on it — financial, legal, medical, or technical. Astra is a powerful tool, but responsibility for the final decision stays with a person.
FAQ
What is GPT-6 Astra?
It is the flagship model of OpenAI's GPT-6 series, designed for complex, comprehensive tasks: deep analysis, software development, deep research, scientific work, and document creation. OpenAI describes it as especially strong in long-horizon agentic tasks that use a computer and a browser. It has been available on Genosai since September 27, 2026.
How does Astra differ from GPT-6 Sol and GPT-6 Luna?
Astra is the top tier of the series, Sol sits in the middle, and Luna is the fast, economical option. Client price per 1M tokens (input / output): Astra 800 / 4000 ₽, Sol 160 / 800 ₽, Luna 8 / 40 ₽. Astra makes sense where the task is genuinely hard and a mistake is costly.
How much does a GPT-6 Astra request cost?
Billing is by tokens: 800 / 4000 ₽ per 1M tokens (input / output), and cached input costs 160 ₽ per 1M. Reasoning tokens are billed as output. A short analytical request with a 300–400-token answer costs roughly 1.2–1.6 ₽. There is no subscription.
What is reasoning effort and which level should I pick?
It controls how much the model thinks before answering. Astra has four levels: low (the default), medium, high, and xhigh. There is no none level — the model always reasons at least a little. Higher levels are slower and more expensive but more accurate on hard tasks. Low or medium covers everyday work.
What context and files does the model support?
Up to 256,000 tokens per request. The model accepts images in JPG, PNG, and WebP formats — up to 10 files at a time — and replies with text. That is enough for a large document, interface screenshots, or scanned tables together with detailed instructions.
Can GPT-6 Astra search the web?
No, the model has no built-in web search. It works with what you pass in the request: text, files, and images. If you need fresh data, paste it into the request yourself or connect a search tool through function calling in your own API integration.
Can I use Astra through an API?
Yes. Genosai provides an OpenAI-compatible endpoint, POST https://api.genosai.io/v1/chat/completions: set the model to gpt-6-astra and use your sdk_ key. Function calling and the reasoning_effort parameter are supported. The model list is available at GET /v1/models.