Bring a complex written task, software problem or document-based question into a conversation with GPT 6 Astra. State the goal, provide the relevant evidence and describe the result you want to review. The existing Vidgo entry offers Chat Completions and Responses, with conversation text and supported output settings. Read the answer against your source material, inspect token usage and use follow-up messages to add constraints or test feedback.
GPT 6 Astra is developed by OpenAI. Vidgo provides access to the models. Model names and trademarks belong to their respective owners.Use the existing Astra entry to prepare a complete question or software brief and select the API format your application uses. The task provides conversation fields, output settings and current token prices.
Describe the task in a user message and include the document text, code excerpt or requirements needed to answer it. Use messages with Chat Completions or input with Responses, keeping the relevant earlier turns when continuing the work. Specify the expected structure and completion criteria, then choose an output-token limit that fits the answer you need.
| Model / task | Input and workflow |
|---|---|
| GPT-6 Astra | GPT-6 Astra handles long-context reasoning, software engineering, image understanding, and agent workflows. |
GPT 6 Astra supports demanding code analysis and technical reasoning. OpenAI’s published charts show research debugging, kernel optimization and machine-learning engineering evaluations. The tasks use the documented research environments and verification procedures.
The research-debugging evaluation uses real faults from OpenAI experiments and compares rubric scores against output-token usage. Astra improves over GPT 5.6 Sol on the reported tasks. Provide the relevant code, failure symptoms and experiment records to investigate likely causes and develop a fix that can be checked with your own tests.
KernelGen 1P evaluates kernel development under specific hardware, runtime and correctness constraints. The chart shows performance across output-token budgets. Supply the implementation, hardware assumptions and measured bottlenecks when requesting an optimization plan, then run correctness and performance tests to assess each proposed change.
MLE-Bench Revised evaluates work on machine-learning problems within a defined experimental setup. The chart compares Astra and GPT 5.6 Sol at the stated effort level. Provide the dataset description, training code and evaluation objective to request an experiment plan, diagnose implementation issues or organize improvements for further testing.
Prepare the source material and define what a useful answer should contain. Request an analysis, structured output, code review or task plan with explicit constraints, then verify the result against the evidence and acceptance criteria. Use a follow-up to provide new findings or test results when refining the work.
Prepare the document passages or facts needed for the question and describe the result to produce. Review the answer for its use of evidence, requested structure and completion criteria.
Provide the relevant code, intended behavior and constraints, then ask for a proposed change plan. Review the plan against the project’s requirements and use concrete implementation or test feedback in later turns.
Supply the implementation and the checks that matter, then request a review tied to the code and expected outcome. Verify suggested changes with your own tests before applying them.
Provide the source note and define exact output fields and units. Inspect and validate the returned data before passing it into an application, catalog or content-processing workflow.
Prepare the facts, permitted actions, desired tone and output format. Review the generated draft against each requirement before using it as a support reply, explanation or project document.
Keep relevant earlier messages and add new findings, clarified constraints or test output. Ask for the next revision and review it against both the original goal and the latest evidence.
The table reads the current starting input-token rate for GPT 6 Astra per one million tokens. Its task page lists input, output, cache-read and cache-write rates. The complete request cost depends on the relevant token categories and their actual usage, including conversation context sent with the request. Review those rates and the returned usage information when budgeting a substantial analysis or several software iterations.
| Model | Provider | Workflow | Input | API formats | Starting input rate |
|---|---|---|---|---|---|
| GPT-6 Astra | OpenAI | Chat | Text instructions and conversation context | Chat Completions / Responses | $8 / 1M tokens |
Test a complete text task online, then integrate the selected model and API format.
Open the selected model, provide the task and source text, and choose its available output settings. Submit the conversation, read the complete answer and inspect available token usage. Add clarifications or test feedback when preparing a follow-up.
Use the existing model conversation, current token prices and task documentation to connect a reviewed answer with your application workflow.
Open GPT 6 Astra and choose Chat Completions or Responses to match your integration. Prepare the task’s goal, relevant text and expected answer before selecting the supported output settings and reviewing the complete token cost.
Open the selected model, provide the task and source text, and choose its available output settings. Submit the conversation, read the complete answer and inspect available token usage. Add clarifications or test feedback when preparing a follow-up.
The table reads the current starting input-token rate for GPT 6 Astra per one million tokens. Its task page lists input, output, cache-read and cache-write rates. The complete request cost depends on the relevant token categories and their actual usage, including conversation context sent with the request. Review those rates and the returned usage information when budgeting a substantial analysis or several software iterations.
Use /v1/chat/completions with model: openai/gpt-6-astra and messages, or /v1/responses with the same model and input. Authenticate with your Vidgo API credentials, send the supported output settings and consume the response in the selected format. Review the assembled text and returned usage information.
Use your Vidgo API credentials to authenticate the request to the selected endpoint. Send the exact model identifier, the conversation fields required by that protocol and the supported output settings. Consume the returned response using the same format you selected.
Accumulate streaming text when the selected workflow streams, or read the complete response body when it returns a full reply. Inspect the final answer and returned usage fields, then keep the relevant messages for the next turn. Return to the model conversation when continuing the online task.
Bring a complex written task, software problem or document-based question into a conversation with GPT 6 Astra. State the goal, provide the relevant evidence and describe the result you want to review. The existing Vidgo entry offers Chat Completions and Responses, with conversation text and supported output settings. Read the answer against your source material, inspect token usage and use follow-up messages to add constraints or test feedback.
Read language-model introductions, API guides and platform comparisons to understand model selection, conversation inputs, response formats and token costs.
