GPT-6 Astra is OpenAI's reasoning and vision model for complex work, suited to software engineering, scientific analysis, and professional content delivery that requires following templates. Its focus is not only on answering questions, but also on understanding multi-step tasks, preserving key constraints, and revising results based on feedback. Applications can integrate it using the public request format in this page's API section.
Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud/v1",
)
response = client.responses.create(
model="gpt-6-astra",
input="Hello!",
)
print(response.output_text)Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
Clarify capacity, input and output, and invocation methods before selecting a model.
Astra's model capabilities and invocation formats are listed separately. Applications pass relevant inputs, history, and tool results through Chat Completions or Responses; actual execution requires an appropriate environment.
Learn what gpt-6-astra can bring to your work.
Astra is well suited to analyzing engineering issues using requirements, relevant code, and test feedback, rather than merely generating isolated snippets. You can ask it to explain the impact of changes, propose validation steps, and continue revising based on execution results. For cross-file changes and complex debugging, this collaboration centered on acceptance criteria is more valuable than one-off code suggestions.
It emphasizes following existing templates, tone, and visual standards, making it suitable for organizing scattered materials into clearly structured reports, presentation content, or analytical explanations. After providing reference styles, the audience, and facts that must be retained, you can ask it to select relevant information and condense repetitive content, bringing the result closer to the delivery format your team actually needs.
Astra can adjust direction based on new requirements while continuing to work around the original task and constraints. When it encounters important gaps that may affect the result, it tends to ask targeted questions. Image understanding can also be incorporated into this process, for example by explaining anomalies based on interface screenshots and then turning observations into troubleshooting recommendations or modification plans.
Start with specific tasks to find where the model can be effective.
Provide error logs, relevant files, reproduction conditions, and existing tests, and ask Astra for root-cause hypotheses, modification plans, and a regression-checklist. Add actual test results in follow-up messages, then have it revise its assessment. The final deliverable can include code suggestions, impact explanations, and unverified items, making it easier for developers to review and merge.
Provide business materials, examples of existing reports, and writing guidelines, and have Astra generate executive summaries, section drafts, or slide-by-slide presentation content. Specify which figures must be cited, which conclusions need caveats, and require it to identify information that still needs to be added. When downloadable documents need to be generated, the application can then integrate the appropriate file-creation tools.
Provide research questions, data summaries, chart screenshots, and analysis code, and have Astra explain observations, check assumptions, and design subsequent validation steps. It is suitable for connecting scientific reasoning with practical analysis work, producing experimental recommendations, code drafts, or result discussions; numerical calculations and simulations should run in controlled environments and return their results.
Choose based on task complexity, input materials, and expected results.
If a task involves code understanding, image assessment, and professional writing at the same time, or requires multiple rounds of revision while preserving the original constraints, Astra is worth evaluating as a priority. Official comparisons show its improvements over GPT-5.6 Sol across multiple engineering and professional tasks, but this does not mean every workload will improve equally; validate it against your own codebase and delivery standards.
GPT-6 Sol, GPT-6 Luna, and GPT-6.1 Sol are different models, not interchangeable names for Astra; Astra Pro should also be distinguished separately. When choosing, do not rank models solely by the version number in their names; instead, compare actual task performance and integration requirements. Existing message-based applications can use Chat Completions, while Responses can be chosen when response event handling is needed.
Based on the characteristics of gpt-6-astra, first validate a small task whose results can be checked.
You can ask directly: Review this cross-system transformation plan and identify conflicting constraints and hidden assumptions. Present counterexamples first, then provide a revised design, validation experiments, and items that still require an owner's decision.
Provide system boundaries, acceptance criteria, and the latest materials; distinguish between facts in the materials, inferences, and conclusions that require experimental validation.
Use the full model ID gpt-6-astra, first confirm the public request format and available parameters on the API page, then connect it to your application. Retain result parsing, exception handling, and related evidence, and use the same set of real samples to evaluate whether it is suitable for continued use.
Example inputs and checking methods to help you design your first trial.
Prepare the pagination function, 12 test records, and expected results of 3 records per page, and specify on which page the duplicates appear. You can ask: “Please analyze the cause of duplicate records on the second page based on this code. Organize the response by existing evidence, root-cause hypotheses, minimal changes, and regression cases; if information is insufficient, first list the materials that need to be provided.”
Check whether the response cites the actual code and reproduction conditions, then run the suggested tests with a program. Bring failure logs and modified code back for the next round, and have the model revise its conclusions. When using screenshots to supplement interface observations, clearly specify the abnormal area and expected behavior as well.
Before formal use, understand the output quality and scope of capabilities.
Answers to common questions about using gpt-6-astra.
Use gpt-6-astra. GPT-6 is the series name and cannot be used directly as the invocation name for this model; Sol, Luna, and Astra Pro should not be mixed up either.
You can provide screenshots in image-and-text messages and include relevant code, logs, and questions in the text. For Chat Completions, use image_url for image content. Clearly specify the interface area to inspect, the expected behavior, and the acceptance criteria so that visual observations and engineering judgment focus on the same issue.
The model can participate in these workflows, but actual execution requires the application to connect the appropriate tools. Submitting code alone will not automatically run tests, and submitting screenshots alone will not automatically click the interface. Your program should perform authorized operations, then return the tool results to Astra for continued analysis and revision.
In Chat Completions, you can set the output format to JSON or JSON Schema, and clearly define field meanings and how missing values should be handled in the task. This is suitable for returning issue lists, review comments, or analysis summaries; the application still needs to parse and validate the results, especially business rules and numerical accuracy.
When using Chat Completions, include relevant history in messages; when using Responses, organize input and related conversation content according to the documentation. Provide the latest materials, revision goals, and key constraints in each round; for longer tasks, retain interim summaries and a final version that can be reviewed independently.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples↗OpenAI Responses API
SDK installation, authentication and examples
↗Using Ace Data Cloud with Cherry Studio
Chat client setup · Base URL, API key and model
↗Using Ace Data Cloud with ChatBox
Chat client setup · Base URL, API key and modelInput
$2.00per 1M tokens
Official $10.00
Output
$10.00per 1M tokens
Official $50.00
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
View Docs