Can GPT-5.5 view screenshots and generate images directly?
GPT-5.5 can combine images and text for understanding, making it suitable for screenshot analysis, chart explanations, and interface-based code discussions. The workflow here delivers text responses and does not equate visual understanding with image generation; when you need to generate or edit images, choose a dedicated image model.
Does a 1M context mean it can output 1M tokens?
No. The context window and maximum single output are different specifications. 1M refers to the native API context capacity published by OpenAI and should not be understood as response length. For long reports or large-scale code, generate in stages and manage each delivery with output length parameters while retaining necessary task context.
Which interface should I choose when integrating GPT-5.5?
Existing messages-based conversational systems can use Chat Completions; when organizing interactions with input and response events, choose Responses. If you only need simplified text conversations, use AI Chat's question and answer approach. All three entry points use gpt-5.5, but their request structures and result parsing methods differ.
How can I have GPT-5.5 continuously discuss a project?
Chat Completions can include necessary history in messages, while Responses can organize context through input. For the simplified conversation entry point, set stateful to true on the first request; for each subsequent request, continue sending stateful: true and the returned conversation id, and provide model and question. It is recommended to organize project constraints, key decisions, and validation results separately rather than relying only on very long chat histories.
Is GPT-5.5 suitable for completing an entire software project in one go?
It is suitable for breaking down complex engineering tasks and continuously refining them, but a complete project still requires code access, test execution, permission management, and an acceptance process. First clarify the scope and completion criteria, then proceed step by step through implementation, testing, and review; after generating code, run tests, and do not treat the model's self-reported completion as an acceptance result.
