GPT-5.6 works best when the prompt clearly defines the desired outcome, relevant evidence, hard constraints, success criteria, and required output—while allowing the model to determine the most efficient reasoning path.

This differs from older prompting practices that relied on long instructions, repeated rules, extensive examples, or rigid step-by-step reasoning procedures. According to OpenAI’s GPT-5.6 prompting guidance, leaner and outcome-oriented prompts can improve both task performance and token efficiency.

GPT-5.6 in ChatGPT

The official model name is GPT-5.6. In standard ChatGPT conversations, the available options map to different models and reasoning levels:

ChatGPT option Underlying model Recommended use
Instant GPT-5.5 Instant Translation, rewriting, simple questions, and quick conversations
Medium GPT-5.6 Sol General analysis, research, coding, and professional work
High GPT-5.6 Sol Difficult debugging, quantitative analysis, architecture, and complex research
Extra High GPT-5.6 Sol Quality-first problems requiring extended reasoning
Pro GPT-5.6 Sol Pro The most difficult tasks and long-running workflows

GPT-5.5 Instant remains the default model for fast everyday responses. GPT-5.6 Sol powers the Medium, High, and Extra High reasoning options, while GPT-5.6 Sol Pro powers Pro. Availability depends on the user’s plan and workspace settings, and the rollout may be gradual. See GPT-5.6 in ChatGPT for the current availability details.

For most serious tasks, start with Medium. Move to High only when additional reasoning produces a meaningful improvement.

The Core Prompting Principle

The most important GPT-5.6 prompting principle is:

Define the destination and completion standard precisely, but avoid prescribing every reasoning step.

A strong prompt should answer the following questions:

  1. What result should be produced?
  2. What context or evidence should be used?
  3. What constraints must be respected?
  4. What must be true before the task is considered complete?
  5. What output format is required?
  6. What should happen when evidence is missing?
  7. How should the result be validated?

OpenAI recommends describing the user-visible outcome and completion criteria instead of forcing the model through an unnecessarily rigid process. GPT-5.6 can usually select an efficient search, tool-use, or reasoning path when the definition of success is clear. See the official GPT-5.6 prompting guide.

Use the following structure as a starting point for complex GPT-5.6 prompts:

Role:
[Describe the model's function and relevant professional context.]

Goal:
[Describe the finished result that should be produced.]

Context:
[Provide relevant background, source material, files, data, and definitions.]

Success criteria:
- [Required conclusion or deliverable]
- [Required evidence or calculations]
- [Required validation]
- [Conditions that must be satisfied before completion]

Constraints:
- [Safety, policy, business, or evidence constraints]
- [Actions the model may or may not perform]
- [Information that must not be invented]
- [Existing content, behavior, or formatting that must be preserved]

Tools:
[Specify which tools may be used and when they should be used.]

Output:
- Language: [English, Traditional Chinese, or another language]
- Format: [Markdown, table, code, report, or JSON]
- Length: [brief, comprehensive, or a specific limit]
- Citations: [inline hyperlinks or another citation style]
- Mathematics: [KaTeX-compatible notation]

Stop rules:
- [When to ask a clarification question]
- [When to use a fallback]
- [When to report missing evidence]
- [When the task is complete]

Verification:
[Specify the calculations, facts, code, formatting, or visual output that
must be checked before returning the final answer.]

Each section should remain short. Add instructions only when they materially change the model’s behavior.

Keep Prompts Lean

OpenAI recommends removing unnecessary prompt scaffolding when migrating to GPT-5.6. This includes:

  • Repeated statements of the same rule
  • Repeated style or workflow instructions
  • Examples that do not change behavior
  • Instructions for behavior the model already performs reliably
  • Tools and tool descriptions unrelated to the task
  • Long lists of prohibitions that can be replaced with one decision rule

Preserve the instructions that actually determine the result:

  • The user-visible outcome
  • Success criteria
  • Stopping conditions
  • Evidence requirements
  • Safety and permission boundaries
  • Required output structure
  • Validation requirements
  • Tool-routing rules that depend on context

Contradictory instructions are particularly harmful because GPT-5-class models follow prompt contracts closely. For example, asking the model to be both “comprehensive” and “extremely brief” without defining which requirement has priority can create unstable results.

Choose Reasoning Effort Intentionally

Higher reasoning effort is not automatically better. A useful conceptual objective is to select the reasoning level that maximizes quality after accounting for cost and latency:

\[U(e) = Q(e) - \lambda_C C(e) - \lambda_L L(e)\]

where:

  • \(e\) is the reasoning-effort level.
  • \(Q(e)\) is the expected output quality.
  • \(C(e)\) is the computational or token cost.
  • \(L(e)\) is the response latency.
  • \(\lambda_C\) and \(\lambda_L\) represent the importance of cost and latency for the application.

The practical recommendations are:

  • Use Instant for straightforward requests.
  • Use Medium as the default for substantive work.
  • Use High when additional reasoning materially improves the result.
  • Reserve Extra High, Max, or Pro for the hardest quality-first workflows.
  • Before increasing reasoning effort, check whether the prompt is missing evidence, success criteria, tool-routing rules, or validation requirements.

OpenAI recommends testing the current reasoning setting and one level lower on representative tasks because GPT-5.6 can often preserve quality while using fewer tokens. See the GPT-5.6 model guidance.

Research and Citation Best Practices

For research tasks, explicitly define the evidence and citation requirements.

A strong research prompt should state:

Use current authoritative sources for claims that may have changed.

Cite only sources that were actually retrieved.

Place each inline hyperlink immediately after the claim it supports.

Distinguish sourced facts from inference.

If reliable sources disagree, explain the disagreement.

If required evidence cannot be found, narrow the conclusion or state that
the evidence is insufficient instead of guessing.

Do not simply request “citations.” Specify:

  • Which claims require citations
  • Which sources are acceptable
  • Whether current web research is required
  • Whether primary sources should be preferred
  • Where citations should appear
  • What the model should do when evidence is unavailable

OpenAI’s guidance recommends attaching citations directly to the claims they support, identifying conflicts between sources, and reporting missing evidence instead of converting an unsuccessful search into a factual conclusion. See grounding, citations, and retrieval guidance.

Coding Best Practices

For coding tasks, provide the repository context, expected behavior, constraints, and validation requirements.

Use instructions such as:

Inspect the relevant code before proposing or implementing changes.

Preserve existing architecture, public interfaces, naming conventions, and
unrelated user changes.

Implement the smallest complete change that satisfies the request.

After editing, run the most relevant available validation:
- targeted tests for the changed behavior
- type checks or lint checks when applicable
- build checks for affected packages
- a minimal smoke test when full validation is too expensive

If validation cannot be run, explain why and identify the next-best check.

Avoid asking only:

Fix this code.

Instead, define:

  • The observed behavior
  • The expected behavior
  • Reproduction steps
  • Relevant files or components
  • Compatibility constraints
  • Acceptance criteria
  • Tests that must pass

GPT-5.6 is designed for complex coding and tool-using workflows, but validation should still be explicitly required. OpenAI recommends giving the model access to appropriate validation tools and naming the checks that matter. See the official GPT-5.6 prompting guidance.

Long-Running and Agentic Tasks

For long-running tasks, define both the objective and the model’s authority.

A compact authorization policy can be written as:

For requests to answer, explain, review, diagnose, or plan, inspect the
relevant materials and report the result. Do not implement changes unless
the request also asks for them.

For requests to change, build, or fix, make the requested in-scope changes
and run relevant non-destructive validation.

Require confirmation before external writes, destructive actions, purchases,
deployments, or material expansion of scope.

This allows GPT-5.6 to continue safe, in-scope work without repeatedly asking for permission while preventing it from silently expanding the task.

For long-running workflows, also request concise progress updates:

Before beginning a multi-step tool workflow, briefly state the first step.

Provide another update only when a major phase begins, a material result is
found, or the plan changes.

Do not narrate every routine tool call.

OpenAI recommends distinguishing research, design, implementation, review, and external coordination so that the model does not silently move from one layer of work to another. See the autonomy and approval guidance.

Writing and Editing Best Practices

For rewriting, translation, summarization, or article editing, explicitly state what must be preserved.

For example:

Preserve the original factual claims, meaning, structure, genre, and level
of detail.

Improve clarity, grammar, consistency, and flow.

Do not add new claims, sections, examples, or promotional language unless
requested.

Return the complete revised content rather than a summary of the changes.

This is especially important when translating technical articles. Without preservation instructions, the model may reorganize sections, simplify technical qualifications, or introduce new explanatory material.

For shorter answers, define what must remain:

Lead with the conclusion.

Preserve all required facts, decisions, calculations, caveats, citations,
and next steps.

Remove introductions, repetition, generic reassurance, and optional
background first.

GitHub Pages and KaTeX Prompt Example

The following prompt is suitable for producing a source-backed technical article for GitHub Pages:

Goal:
Produce a complete technical guide explaining Binance VIP tiers, maker fees,
maker rebates, and effective trading costs.

Evidence:
Use the latest official Binance documentation. Verify every current fee,
volume threshold, eligibility rule, and rebate claim through web research.

Success criteria:
- Explain Spot and Futures VIP calculations separately.
- Distinguish standard maker discounts from actual negative maker fees.
- Include worked numerical examples.
- Identify whether maker rebates are generally available or limited to
  specific products or programs.
- Distinguish confirmed rules from interpretation.
- Include inline hyperlink references.
- Check every formula for KaTeX compatibility.

Constraints:
- Do not invent missing fee tiers or rebate programs.
- Do not rely on outdated third-party fee tables when official documentation
  is available.
- Do not remove material caveats.
- Use only one YAML front-matter delimiter at the beginning and end.
- Do not place Markdown headings inside mathematical expressions.

Output:
Return raw Markdown for GitHub Pages with the following front matter:

---
layout: post
title: "Binance VIP Tiers, Maker Fees, and Rebates"
date: 2026-07-11
category: trading
---

Use Markdown tables for exact fee comparisons.

Use KaTeX-compatible display mathematics surrounded by double dollar signs.

Place each inline hyperlink immediately after the claim it supports.

Return the complete article without introductory commentary outside the
Markdown content.

Verification:
Before finishing, check:
- every numerical threshold against its cited source
- every calculation
- every inline hyperlink
- YAML front matter
- Markdown table structure
- opening and closing KaTeX delimiters
- aligned-environment line breaks

Common Failure Modes

Vague goals

Weak:

Analyze GPT-5.6 deeply.

Better:

Compare GPT-5.6 Medium and High for quantitative research, coding, and
technical writing. Recommend the appropriate mode for each workload and
explain the quality, latency, and usage-limit trade-offs.

Excessive process instructions

Weak:

First think about the problem. Then create ten hypotheses. Then evaluate
every hypothesis. Then reconsider everything three times.

Better:

Determine the most defensible answer from the available evidence. Evaluate
material alternatives and verify the final conclusion before responding.

Undefined evidence requirements

Weak:

Research the latest information.

Better:

Search current primary sources. Cite every time-sensitive claim with an
inline hyperlink. Clearly label any inference that is not directly stated
by a source.

Undefined completion criteria

Weak:

Improve this article.

Better:

Correct grammar, improve flow, preserve all factual content and citations,
convert mathematics to KaTeX-compatible syntax, and return the complete
revised Markdown article.

Overusing the highest reasoning level

High reasoning cannot compensate for:

  • Missing source material
  • Ambiguous objectives
  • Contradictory constraints
  • Undefined acceptance criteria
  • Missing validation tools
  • Incorrect or outdated evidence

Improve the prompt and evidence first. Increase reasoning effort only when the remaining difficulty genuinely requires deeper analysis.

API Recommendations

For API applications, OpenAI recommends using the Responses API for reasoning, tool calling, and multi-turn workflows.

The primary GPT-5.6 model aliases are:

Model Recommended use
gpt-5.6 Alias for GPT-5.6 Sol and the general default
gpt-5.6-sol Frontier capability and difficult workflows
gpt-5.6-terra Balance of capability, speed, and cost
gpt-5.6-luna High-volume and cost-sensitive workloads

GPT-5.6 supports the following reasoning-effort settings:

none
low
medium
high
xhigh
max

Use medium as the initial balanced setting. Evaluate low for latency-sensitive workloads and reserve max for the hardest quality-first tasks. Do not apply the highest reasoning level globally without workload-specific evaluation. See the current GPT-5.6 model and migration guidance.

Practical Checklist

Before sending a complex GPT-5.6 prompt, verify that it contains:

  • A specific user-visible goal
  • Necessary context and source material
  • Clear success criteria
  • Hard constraints
  • Evidence and citation requirements
  • Output language and format
  • Appropriate reasoning level
  • Tool-use instructions when necessary
  • Authorization and approval boundaries
  • Stopping conditions
  • Validation requirements
  • Instructions for handling missing evidence
  • No repeated or contradictory rules

Conclusion

The best way to use GPT-5.6 is not to construct the longest possible prompt. The goal is to create the smallest prompt that completely defines the desired outcome, relevant evidence, constraints, completion criteria, and validation requirements.

A reliable GPT-5.6 workflow is:

  1. Start with Medium reasoning.
  2. Define the outcome and success criteria.
  3. Supply the necessary evidence.
  4. State hard constraints and authorization boundaries.
  5. Specify the required output format.
  6. Require factual, mathematical, code, or visual validation.
  7. Increase reasoning effort only when measured quality improves.
  8. Refine prompts through representative evaluations rather than intuition alone.

GPT-5.6 was designed to complete more complex, end-to-end work with stronger token efficiency, tool use, coding performance, and design judgment. Its advantages are most useful when the model receives a clear definition of success without unnecessary prompt scaffolding. See OpenAI’s GPT-5.6 launch announcement, ChatGPT availability guide, and official prompting guidance.