OpenAI: GPT-6 Astra Pro

GPT-6 Astra Pro is a text model from OpenAI. It shares the same underlying model as GPT-6 Astra, but it is served with reasoning.mode set to pro, which is aimed at higher quality responses on complex tasks. If you already know the Astra family, the Pro variant is the configuration to pick when the task is hard enough that extra reasoning effort is worth the trade-off in latency and token spend.

On the input side, the model accepts text, images and files, and it produces text only. The context window is 1,050,000 tokens, which is large enough to hold entire codebases, long transcripts or multi-document sets in a single request. The maximum output is 128,000 tokens, so long generated artifacts such as full reports or large refactors are possible without chaining calls. Supported request parameters include max_tokens and max_completion_tokens for output control, reasoning and reasoning_effort plus include_reasoning for steering and inspecting the reasoning behaviour, response_format and structured_outputs for constrained output, seed for reproducibility, and tools and tool_choice for function calling. A verbosity parameter is also available.

Those capabilities point to a fairly specific set of uses. Long-context analysis over contracts, research papers or log archives fits the 1M token window. Image and file inputs make it usable for reviewing screenshots, diagrams, PDFs and spreadsheets alongside a written question. Structured outputs and tool calling suit agentic workflows, data extraction pipelines and anything that needs to hand results to another system in a fixed schema. The large output ceiling helps for drafting long documents or producing complete patches in one pass.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-6-astra-pro. Because the interface follows the OpenAI chat completions shape, existing SDKs and clients work with only a base URL and key change. Pass the model id in the request body and supply messages as usual, with image or file content where relevant. Set max_completion_tokens or max_tokens to bound the response, use reasoning_effort to trade quality against speed, and enable tools or structured_outputs when you need machine-readable results.

One thing to weigh is that pro mode spends far more than the standard configuration, so it is best reserved for genuinely difficult tasks. For simpler prompts, high-volume classification or latency-sensitive paths, the non-pro GPT-6 Astra is likely the better fit. The model outputs text only, so if you need audio or video generation you will need a different model.