GPT-5.6 Sol is OpenAI's flagship text model with a context window of roughly 1,000,000 tokens. It reasons through multi-step problems, analyzes images, calls tools and functions, searches the web, and generates images. The model is available in GPTunneL, so you can chat with it right away — no separate accounts needed. This guide covers how to phrase your requests to get the most out of it.
What to keep in mind when writing prompts
The model's capabilities shape how you should structure requests:
- Huge context (~1M tokens). You can load an entire book, a codebase, or a stack of documents into one conversation. The larger the context, though, the more important it is to point the model at the specific part it should rely on.
- Reasoning. The model handles multi-step problems well. Complex tasks benefit from asking it to break the solution into stages.
- Vision. Screenshots, diagrams, photographed tables — attach them to your prompt and say exactly what to extract from the image.
- Tools and web search. When you need fresh data, explicitly ask the model to verify facts on the web and list its sources — that cuts down on guesswork.
- Image generation. Describe the picture you want in concrete terms: subject, style, composition, what belongs in the frame.
Prompting techniques
Request structure: role, goal, format, criteria
A universal skeleton that sharply improves answer quality:
Role: you are an editor at a technical blog.
Goal: rewrite the text below so a beginner can understand it.
Format: under 500 words, subheadings, no jargon.
Criteria: keep every fact, remove bureaucratic phrasing.
Text: [...]
The role sets the style and expertise, the goal says what to do, the format defines what the result looks like, and the criteria tell the model how to distinguish a good answer from a bad one.
Zero-shot vs few-shot
Zero-shot means a task with no examples: "Classify this review as positive or negative." For routine tasks that's enough. Few-shot means showing 2–3 input → output examples so the model copies the pattern:
Review: "Delivered in a day, everything intact" → positive
Review: "Waited a month, box was crushed" → negative
Review: "Price is fine, but the packaging is meh" → ?
Few-shot is especially useful when you need a strict output format or non-obvious classification logic.
Chain-of-thought: ask it to reason
For tasks involving logic, calculations, or ambiguous conditions, add: "Reason step by step: first list the known facts, then the options, then pick the best one and explain why." A reasoning model does this on its own, but explicitly asking it to show its work lets you audit the logic and spot mistakes.
Long context — work in stages
Even with a million-token window, don't dump everything into one "read this and draw conclusions" request. A staged approach works better:
- Load the documents and ask for a brief map: "List the sections and what each covers."
- Ask targeted questions referencing a section: "In section 3, which risks are mentioned?"
- Final synthesis: "Based on everything we've covered, write a summary with quotes from the source."
Ask the model to cite its source ("state where in the document each fact comes from") — it keeps the model honest and makes verification easier.
Ready-made prompts: copy and adapt
Long document breakdown:
Below is a contract. Build a table: clause → obligation → risk for the contractor. At the end, list 5 clauses worth discussing with a lawyer, with quotes from the text.
UI screenshot analysis:
The image shows a mobile app screen. List every interface element, rate each for usability, and suggest 3 specific improvements with reasoning.
Research with web search:
Search the web for current information on [topic]. Give me a 300-word summary, with sources in a separate list. If sources contradict each other, show both versions.
Code review:
You are a senior developer. Review the code below: first a list of bugs ordered by severity, then style issues, then a refactoring proposal. For each item, include the line of code and the fixed version.
Image generation:
Generate an image: a minimalist illustration for an article about working with data, isometric style, calm blue-gray palette, no text in the picture.
Diagnosing common failures
- The answer is too generic. The prompt lacks criteria and an audience. Add who the answer is for, what counts as a good result, and what to avoid. Demand specifics: "with examples," "with figures from the document."
- The format broke. Describing the format once isn't enough — provide a sample output (few-shot) and add "follow the template strictly, output nothing but the table." Long conversations dilute instructions: restate format requirements in your latest message.
- Guesswork and invented facts. Explicitly allow uncertainty: "If the document doesn't contain the data, write 'not stated in the text' — don't fill in the gaps." For factual questions, enable web search and require sources. Ask for direct quotes from the source — a verbatim quote is harder to fabricate than a paraphrase.
For API details and OpenAI model capabilities, see the official documentation. And you can try GPT-5.6 Sol in GPTunneL: one balance, all the top models, no subscriptions.