API429 / gpt-6-astra

GPT-6 Astra API: complex tasks, cost and integration

OpenAI positions GPT-6 Astra for the most complex reasoning, programming, research and document preparation tasks. Define a measurable outcome and compare Astra with Sol on the same material. This page separates manufacturer information, ordinary token estimates and API429 access checks.

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Published: · Updated:

Exact ID: gpt-6-astra. Check access for your key using GET /v1/models; a published model does not guarantee access for every account.

Prices and billing units

API429: published ratesManufacturer: base Standard
$3.750 / 1,000,000 cache-write input tokens · Context up to 272,000 tokens inclusive
$7.50 / 1,000,000 cache-write input tokens · Context over 272,000 tokens
$0.30 / 1,000,000 cached input tokens · Context up to 272,000 tokens inclusive
$0.60 / 1,000,000 cached input tokens · Context over 272,000 tokens
$3.00 / 1,000,000 input tokens · Context up to 272,000 tokens inclusive
$6.00 / 1,000,000 input tokens · Context over 272,000 tokens
$15.00 / 1,000,000 output tokens · Context up to 272,000 tokens inclusive
$22.50 / 1,000,000 output tokens · Context over 272,000 tokens
$10 / $50 per 1M ordinary input / output tokens

Standard; input ≤272,000 tokens

Manufacturer sourceChecked: Oct 24, 2026. Cache, tools and special modes are excluded from this rate pair.

API429 catalog generated . This dates the catalog data, not the model’s addition.

Workload budget: input and output

API429$13.5
Manufacturer$45
Difference from manufacturer price70%API429 costs less for this workload

Ordinary input and output are counted, excluding cache, tools, fees and repeated calls. This estimates spending; it does not validate whether a request is accepted. RUB results use only your entered rate; they are neither a bank quote nor a top-up amount.

Specifications and compatibility boundaries

The manufacturer specifies a context window of 1,050,000 and a maximum output of 128,000 tokens for its own API (specification: OpenAI, checked Oct 24, 2026). These are not verified effective API429 limits: the current public catalog does not publish them. Confirm the required volume and mode before migrating your workload.

API429 capabilities published for this exact ID: chat function_calling multimodal_input reasoning structured_output vision . A missing property does not mean a prohibition, but it does not establish support. Check input format, tools, JSON/schema, reasoning and streaming separately for the selected method.

Reviewing a complex plan: define a verifiable result

A representative Astra scenario is reviewing a plan where several dependent steps must lead to a finished result. Before migrating data, for example, ask for prerequisites, completeness checks and rollback conditions. Provide specific boundaries: permitted operations, known data and what should only be described. OpenAI mentions complex reasoning, code work, research and document creation for Astra. These areas help select a control task but do not guarantee correctness. Compare Astra with GPT-6.1 Sol using detected contradictions, usability of the proposed sequence and necessary clarifications. For research, define permitted sources and the evidence format in advance. A long confident answer alone does not establish that all dependencies have been checked or the task is complete.

The native Astra contract requires reviewing parameters

In the OpenAI API, Astra has no reasoning effort none mode. Documentation also disallows custom temperature and top_p and obtaining log probabilities. Native tool calls require the Responses API. Transferring an older template with the same fields therefore cannot be called a verified integration after changing model alone. These restrictions belong to the manufacturer and do not automatically describe API429 support for additional fields. A first request needs only short messages, the exact gpt-6-astra, max_tokens and stream from the public specification. Check required options individually afterward. Formal method availability or HTTP 200 is not evidence that a requested mode was applied. If your application makes decisions from the result, validate response structure and handling of partial or empty results.

The cost of a complex task depends on attempts

Astra’s high token price does not let you calculate the cost of completed work without usage data. One case may need one answer; another may require clarifications, repeated analysis and another document. Record ordinary input, output and run count on a control set. The calculator counts only these tokens for independent requests by default; cache and tools are excluded. The manufacturer sets a separate condition for input above 272,000 tokens: higher rates apply to the entire request. This is not progressive billing only for the excess. Use fresh API429 rates and actual usage for your customer budget. A comparison with Sol should account for the accepted result’s quality and all attempts, rather than just one token price or the final text length.

Clarifications and boundaries on independent work

OpenAI’s guide describes Astra asking for clarification more often when the answer could affect the result and paying closer attention to instructions in available files. Explicitly state your goal, completion criterion and actions it may only propose. If you need only a plan audit, say so and list the expected response sections. If input includes several documents, set their priority and ask for contradictions to be flagged. These suggestions help organize evaluation but do not add independent action execution to the basic API429 example. Do not turn a native agent description into a promise of a built-in browser or access to your applications. The first text test checks the model response; additional modes need separate contract confirmation and result validation.

Public model observations

Over the last 24 hours: mean successful response time — 4.7 s; service error rate — 0%. Data as of .

Source: public 24-hour statistics. The mean is neither a median nor a response time guarantee; the error rate and observation window refer to the API429 service. Model quality is not measured here.

First request: curl, Python, JavaScript

Create a key, check your balance and set API429_API_KEY on the server. The generation example incurs a charge. It does not retry automatically; a timeout does not prove the operation was not charged. Inspect the content and actual usage after receiving a response. Python and JavaScript first check the available model list.

curl

Before generation, check the exact ID in the response from GET https://gateway.api429.com/v1/models using the same Bearer key.

curl --fail-with-body https://gateway.api429.com/v1/chat/completions \
  -H "Authorization: Bearer $API429_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-6-astra","messages":[{"role":"user","content":"In the plan “create a table, migrate data, delete the old table”, identify two missing verification conditions. Do not perform any actions."}],"max_tokens":512,"stream":false}'

Python 3

import json
import os
import urllib.request

key = os.environ["API429_API_KEY"]
base = "https://gateway.api429.com/v1"
headers = {"Authorization": "Bearer " + key, "Content-Type": "application/json"}
models_request = urllib.request.Request(base + "/models", headers=headers)
with urllib.request.urlopen(models_request, timeout=30) as response:
    available = {item["id"] for item in json.load(response)["data"]}
model = "gpt-6-astra"
if model not in available:
    raise ValueError("Model is unavailable to this key")
payload = json.loads("{\"model\":\"gpt-6-astra\",\"messages\":[{\"role\":\"user\",\"content\":\"In the plan “create a table, migrate data, delete the old table”, identify two missing verification conditions. Do not perform any actions.\"}],\"max_tokens\":512,\"stream\":false}")
request = urllib.request.Request(base + "/chat/completions",
    data=json.dumps(payload).encode("utf-8"), headers=headers, method="POST")
with urllib.request.urlopen(request, timeout=60) as response:
    result = json.load(response)
choice = result["choices"][0]
if choice.get("finish_reason") != "stop" or not choice.get("message", {}).get("content"):
    raise ValueError("Incomplete or empty response")
print(choice["message"]["content"])
print(result.get("usage"))

JavaScript: Node.js 18+

const key = process.env.API429_API_KEY;
if (!key) throw new Error("Set API429_API_KEY");
const base = "https://gateway.api429.com/v1";
const headers = { Authorization: "Bearer " + key, "Content-Type": "application/json" };
const model = "gpt-6-astra";
const available = await fetch(base + "/models", { headers, signal: AbortSignal.timeout(30000) });
if (!available.ok) throw new Error("Models HTTP " + available.status);
if (!(await available.json()).data.some(item => item.id === model)) throw new Error("Model unavailable to this key");
const response = await fetch(base + "/chat/completions", {
  method: "POST", headers, signal: AbortSignal.timeout(60000),
  body: JSON.stringify({"model":"gpt-6-astra","messages":[{"role":"user","content":"In the plan “create a table, migrate data, delete the old table”, identify two missing verification conditions. Do not perform any actions."}],"max_tokens":512,"stream":false})
});
if (!response.ok) throw new Error("Generation HTTP " + response.status);
const result = await response.json();
const choice = result.choices?.[0];
if (choice?.finish_reason !== "stop" || !choice?.message?.content) throw new Error("Incomplete or empty response");
console.log(choice.message.content);
console.log(result.usage);

The examples use basic fields from the published API contract. They are checked offline; no successful paid execution or output accuracy is claimed.

n8n: minimal HTTP Request

  1. Create an HTTP Request with method POST and URL https://gateway.api429.com/v1/chat/completions.
  2. Store the Bearer key in credentials; add Content-Type: application/json. Do not put the key in an exported workflow example.
  3. Send the JSON from curl: the exact model gpt-6-astra, messages, max_tokens and stream=false.
  4. After the HTTP node, check choices, finish_reason and the response content before writing the result to your production system.

HTTP Request documentation. This recipe describes HTTP Request. Compatibility with specific Agent nodes, SDKs and other API methods needs separate verification.

Three models for your own comparison

These are options for a controlled comparison on the same task, rather than a quality ranking. Base rates apply to a short text request; see the catalog for full pricing.

ModelAPI429: input / output per 1M tokens
GPT 6 Solgpt-6-sol$0.6 / $3
GPT 6 Lunagpt-6-luna$0.03 / $0.15
GPT 5.6 Lunagpt-5.6-luna$0.3 / $1.8

Version and release date

Manufacturer API release date, OpenAI: . The exact predecessor has not been established in the reviewed sources. The date of addition to API429 is unknown. Current availability is determined by the catalog and key permissions, rather than the manufacturer’s historical release date.

Frequently asked questions

When should I compare Astra with GPT-6.1 Sol?

When mistakes in complex analysis materially affect the outcome and you have a control set. Use the same source data, requirements and acceptance criteria. Different rates establish neither superiority on every task nor lower total project cost.

Can I disable reasoning with none?

In the native OpenAI API, Astra does not support none. Do not copy that field into an API429 example as a universal setting. Check client documentation and the required mode separately; this page’s basic request does not set effort parameters.

Does the model provide built-in browser access?

The manufacturer’s computer use description concerns its tool API and workflow setup. An API429 text call alone does not establish a browser, account permissions or external action execution. That integration needs a separately verified contract.

Which model is Astra’s predecessor?

The reviewed sources do not establish one exact predecessor for this page’s field. Other models can be compared by task, but that does not make them an earlier Astra version or a compatible alias.

Sources and review

Editorial review: . Pricing and general calculator · Getting started · Compare the family: OpenAI

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