GPT-6 Astra API: Pricing, Astra Pro, Batch and Access Guide

Announced by OpenAI on September 3, 2026, GPT-6 Astra is positioned in the company's own words as "the world's smartest and most aligned model." According to the announcement, the focus is no longer just question answering: it is computer and browser use, software engineering, professional work, and science. OpenAI says the model can work across collections of files, write and run code, and carry long tasks through with less human direction. In the Onysoft catalog, openai/gpt-6-astra is live with a 1,050,000-token context window and a 128K maximum output.

This guide covers what GPT-6 Astra changes, its Onysoft pricing in USD and TRY, how to choose between Astra, Astra Pro, and the half-price :batch model ID, where GPT-5.6 Sol, Terra, and Luna are still the better fit, and how to send your first request with Python and curl from Türkiye or anywhere else.

What GPT-6 Astra Changes

According to OpenAI's announcement, Astra targets four areas: computer and browser use, software engineering, professional knowledge work, and science. For developers, that translates into three concrete shifts:

• Longer agent tasks: the provider emphasizes that the model can see long tasks through with less human direction. In practice, that makes "set the goal, review the checkpoints" workflows worth testing instead of approving every single step.
• Writing and running code: a 1.05M-token context is enough to carry a multi-file codebase plus its documentation in one request, and the 128K output ceiling lets long patches and plans finish in a single response.
• Computer and browser use: this capability needs a tool environment. Through Onysoft you reach the model over the OpenAI-compatible Chat Completions API; no ready-made screen or browser environment comes with the request. Your own application provides the browser, file system or code-execution environment.

Cybersecurity threshold and restricted capabilities: according to OpenAI, GPT-6 Astra is the first model to meet the "Critical" cybersecurity threshold in the company's Preparedness Framework. For that reason, the provider has placed cyber-sensitive capabilities behind a trusted access program and is rolling the model out in stages. For developers, this means the model's behavior on sensitive security work such as penetration testing or vulnerability analysis is bounded by provider policy. Test those workloads with your own scenarios before building on Astra. Accessing the model through Onysoft is not a way around those policies.

GPT-6 Astra Pricing: Onysoft Rates in USD and TRY

The table below shows Onysoft pricing per 1 million tokens. The GPT-5.6 family is included for comparison; all six models in the table have a 1,050,000-token context and a 128K-token maximum output.

Model ID	Input ($/1M)	Output ($/1M)	Input (TRY/1M)	Output (TRY/1M)
openai/gpt-6-astra	$15	$75	727.41 TL	3,637.06 TL
openai/gpt-6-astra-pro	$15	$75	727.41 TL	3,637.06 TL
openai/gpt-6-astra:batch	$7.50	$37.50	363.71 TL	1,818.53 TL
openai/gpt-5.6-sol	$3	$15	145.48 TL	727.41 TL
openai/gpt-5.6-terra	$3	$18	145.48 TL	872.89 TL
openai/gpt-5.6-luna	$0.30	$1.80	14.55 TL	87.29 TL

Prices are Onysoft rates as of September 13, 2026; TRY amounts use the Central Bank of the Republic of Türkiye (TCMB) rate of September 11, 2026 (1 USD = 48.4941 TL). See /models for current pricing.

Worked example: a single request with 100,000 input tokens and 10,000 output tokens costs $2.25 (about 109.11 TL) on openai/gpt-6-astra, $1.125 (about 54.56 TL) on openai/gpt-6-astra:batch, $0.45 (about 21.82 TL) on openai/gpt-5.6-sol, and $0.048 (about 2.33 TL) on openai/gpt-5.6-luna. Multiply that by millions of tokens and the budget impact of model choice becomes obvious.

How billing works: Onysoft bills every request as the model's input and output token counts multiplied by these rates, deducted from your balance at the TCMB exchange rate. Special pricing mechanics on providers' direct APIs (for example, cache-read discounts) are not reflected in this table: what you pay is the listed input and output price. Because Astra output tokens cost 5 times as much as input tokens, keeping an eye on output length in agent loops is a good habit.

Astra, Astra Pro, or :batch: Which One When?

All three model IDs reach the same model family but serve different usage patterns:

• openai/gpt-6-astra (default): interactive agent loops, code generation, and analysis. The starting point for any Astra job where the response needs to arrive in reasonable time.
• openai/gpt-6-astra-pro (deeper reasoning mode): the same unit price as Astra ($15 / $75); the difference is the operating mode. Deeper reasoning usually means longer responses and longer waits, so even at the same unit price, total tokens and time per request can go up. Use it per request on steps where mistakes are expensive (an architecture decision, a critical patch, the final version of a report) rather than as your default.
• openai/gpt-6-astra:batch (half price): $7.50 / $37.50, half the standard Astra rate. Built for non-urgent bulk work: overnight evaluation suites, bulk labeling of document archives, model regression tests. It comes as a separate model ID, so switching is just a change to the model name in your request. Validate your response-time expectations against your own workload before going to production.

Pinned ID or alias? The ~openai/gpt-astra-latest alias (the tilde prefix is required) points, as the name suggests, to the current release of the Astra family. It is convenient for prototypes and internal tools. For production pipelines where consistent behavior matters, prefer a pinned ID such as openai/gpt-6-astra and move to new releases deliberately, after regression testing.

GPT-6 Astra or GPT-5.6? A Decision Matrix

Astra's input price is 5 times that of GPT-5.6 Sol and 50 times that of Luna. So the right question is not "is Astra more capable?" but "does this job actually need what Astra adds?":

Workload	Recommended model	Why
Long-running agent tasks with little direction, work across collections of files	openai/gpt-6-astra	The scenario the provider highlights for Astra
Critical steps where mistakes are expensive	openai/gpt-6-astra-pro	Deeper reasoning mode at the same unit price
Non-urgent bulk evaluation and processing	openai/gpt-6-astra:batch	Half the Astra price
Production chatbots, RAG, routine coding assistance	openai/gpt-5.6-sol or openai/gpt-5.6-terra	One-fifth of Astra's input price
Classification, summarization, data extraction at high volume	openai/gpt-5.6-luna	Lowest unit price in the pricing table above
A second flagship or cross-check for heavy coding and knowledge work	anthropic/claude-fable-5.1	Same price point, different provider
High-volume multimodal work with audio, video, and PDFs	google/gemini-3.8-flash	Much lower unit price, multimodal input

When is GPT-5.6 enough? Sol, Terra, and Luna also ship with a 1.05M-token context. For single-step or short-chain jobs (summarizing a document, answering a question with RAG, writing a single function), moving to Astra usually raises cost without clearly changing the result. For tier details, see our GPT-5.6 Sol, Terra, and Luna guide.

Skip Astra when:

• Latency is the product itself, as in short interactive replies (autocomplete, live chat).
• You run classification or extraction pipelines that generate hundreds of thousands of calls a day.
• You would make it your default without first benchmarking it against GPT-5.6 on your own workload.
• The work falls into the cyber-sensitive security tasks the provider restricts.

Quick positioning against other flagships: anthropic/claude-fable-5.1, announced by Anthropic on September 1, 2026, sits at the same price point as Astra on Onysoft ($15 / $75, 1M context, 128K output). Anthropic positions Fable 5.1 among "the world's most advanced models" for coding and knowledge work; according to the provider, it can discover security vulnerabilities but does not develop exploits. google/gemini-3.8-flash, announced by Google on September 2, 2026, comes in at a far lower $1.125 / $5.625 and accepts text, image, audio, video, and PDF input. According to Google, 3.8 Flash deliberately spends more thinking tokens, so measure output token usage before you commit. For details, see our Claude Fable 5.1 guide and Gemini 3.8 Flash guide, or browse the full families on the Claude models and Gemini models pages.

Getting GPT-6 Astra API Access, Step by Step

You don't need a separate OpenAI account to use GPT-6 Astra; Onysoft AI Gateway gets you started in a few minutes:

1. Create an account. Teams in Türkiye get corporate e-invoicing and KVKK compliance as standard.
2. Generate an API key with the sk-ony- prefix from the dashboard.
3. Add balance. Usage is pay-as-you-go with no subscription or monthly commitment; balances in Türkiye are held in TRY and charged at the TCMB exchange rate.
4. Before writing code, run openai/gpt-6-astra and openai/gpt-5.6-sol side by side with the same prompt in the Playground. It is the fastest way to see which jobs genuinely need Astra.
5. Set base_url to https://api.onysoft.com/v1 in your application and pass the model name.

If you already have an OpenAI integration, migration is a change to base_url, the key, and model. The same key reaches all 754+ models in the catalog: one balance, one invoice, one API. For the broader picture in Türkiye, see our ChatGPT API in Türkiye guide and the AI API guide. If you get stuck, 24/7 support is available.

Your First GPT-6 Astra Request with Python, curl, and Streaming

Because Onysoft is OpenAI-compatible, you connect with the official openai Python package. Streaming responses come back as raw OpenAI chunks and work directly with the SDK, which is also the recommended path for long agent outputs:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.onysoft.com/v1",
    api_key="sk-ony-YOUR_KEY",
)

stream = client.chat.completions.create(
    model="openai/gpt-6-astra",
    messages=[{"role": "user", "content": "Draft a step-by-step plan to improve test coverage in this repository."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

For non-streaming requests, Onysoft wraps the response in a {"success": true, "data": {...}} envelope, with the model output inside data. You can read it through the SDK's raw response:

raw = client.chat.completions.with_raw_response.create(
    model="openai/gpt-6-astra-pro",
    messages=[{"role": "user", "content": "Compare the risks of these two architecture options."}],
)
body = raw.http_response.json()
print(body["data"]["choices"][0]["message"]["content"])
print(body["data"]["usage"], body["data"]["cost"]["amount"])

The data.cost object shows the request cost as amount and currency ("USD"), and data.usage shows the token counts. The same request with curl:

curl https://api.onysoft.com/v1/chat/completions \
  -H "Authorization: Bearer sk-ony-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-6-astra",
    "messages": [{"role": "user", "content": "Draft a debugging plan for this service."}]
  }'

To stream with curl, add "stream": true to the body; the -N flag keeps output from being buffered:

curl -N https://api.onysoft.com/v1/chat/completions \
  -H "Authorization: Bearer sk-ony-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "openai/gpt-6-astra", "stream": true, "messages": [{"role": "user", "content": "Hello Astra"}]}'

To get half-price bulk processing, set the model name to openai/gpt-6-astra:batch; for deeper reasoning, use openai/gpt-6-astra-pro. The request schema stays the same.

OnyRouter tip: for mixed traffic where not every request needs Astra, set the model to onysoft/auto and OnyRouter picks the model for each request. Routing is free; you only pay the price of the selected model. Details are in the OnyRouter guide, and every parameter and error code is in the API documentation.