OpenAI vs Anthropic: Structuring Prompts for Different LLM Context Windows

Not all Large Language Models process prompts the same way. OpenAI's GPT-4o and Anthropic's Claude 3.5 Sonnet have fundamentally different architectures for handling system instructions, user context, and long documents. A prompt structure that works brilliantly for one model may underperform on the other.

Understanding these differences is critical for developers who build multi-model AI systems or who switch between providers.

Context Window Fundamentals

A model's context window is the total number of tokens (roughly ¾ of a word) it can process in a single request — including both the input prompt and the generated output.

• GPT-4o: 128K token context window
• Claude 3.5 Sonnet: 200K token context window
• GPT-4o mini: 128K token context window
• Claude 3 Haiku: 200K token context window

But raw context size is only half the story. What matters more is how each model attends to information at different positions within that window.

How GPT-4o Processes Prompts

OpenAI's GPT-4o uses a role-based message system with three distinct message types:

System Message

The system message is the highest-priority context. GPT-4o treats system messages as persistent instructions that take precedence over user messages. This is where you define the AI's role, constraints, and output format.

{
  "role": "system",
  "content": "You are a TypeScript expert. Return only code.
              No explanations. Use strict types."
}

User / Assistant Messages

Conversation history is passed as alternating user/assistant messages. GPT-4o processes these sequentially, with a known tendency toward recency bias — information at the end of the conversation receives more attention than information in the middle.

Optimisation Tips for GPT-4o

• Front-load critical constraints in the system message — they persist across the entire conversation
• Repeat key instructions at the end of long prompts — GPT-4o attends most strongly to the beginning and end
• Use JSON mode (response_format: { type: "json_object" }) when you need structured outputs — it dramatically reduces hallucination
• Keep conversations short — start new sessions frequently rather than relying on long message chains
• Use delimiters like triple backticks or XML-style tags to separate code, context, and instructions

How Claude 3.5 Sonnet Processes Prompts

Anthropic's Claude has a fundamentally different approach. Claude excels at processing long, structured documents and uses XML tags as a native structuring mechanism.

XML-Tagged Documents

Claude was specifically trained to understand XML tags as structural delimiters. Wrapping your content in descriptive XML tags dramatically improves Claude's ability to parse and reference specific sections:

<system>
You are a senior React developer reviewing a pull request.
Evaluate code quality, type safety, and adherence to the
project's architectural conventions.
</system>

<project_context>
  <tech_stack>React 18, TypeScript, Vite, Firebase</tech_stack>
  <conventions>Named exports, no 'any' types, hooks in hooks/ dir</conventions>
</project_context>

<code_to_review>
// ... the actual code ...
</code_to_review>

<output_format>
Return a JSON array of issues, each with: file, line, severity, message.
</output_format>

Long Document Handling

Claude's 200K context window and training specifically for long-form document analysis means it can process entire codebases, documentation sets, and specification documents in a single prompt. Unlike GPT-4o, Claude shows relatively consistent attention across the entire context window — the "lost in the middle" problem is less pronounced.

Optimisation Tips for Claude

• Use XML tags extensively — Claude understands them natively and uses them to maintain structure in long contexts
• Provide complete documents rather than excerpts — Claude handles long contexts better than most models
• Place instructions after the document — Claude processes documents holistically and performs well with instructions at the end
• Use the system parameter in the API rather than embedding system instructions in the first message
• Leverage prefilling — you can prefill Claude's response to guide its output format

Structural Comparison

Here's how the same prompt should be structured differently for each model:

GPT-4o: System message + concise user message

System: "You are a TypeScript expert. Respond with code only.
         Use strict types. Follow the App Router pattern."

User: "Create a user profile page with server-side data fetching."

Claude: XML-structured document

<system>You are a TypeScript expert.</system>

<project>
  <framework>Next.js 14 App Router</framework>
  <language>TypeScript strict mode</language>
</project>

<task>
Create a user profile page with server-side data fetching.
Return code only. Use strict types.
</task>

Building Model-Agnostic Prompts

If your system needs to work across multiple LLM providers, build your prompts with a universal structure that translates well to both architectures:

1. Separate system context from task instructions — this maps to GPT-4o's system message and Claude's system parameter
2. Use clear section delimiters — XML tags for Claude, Markdown headers for GPT-4o
3. Specify output format explicitly — both models benefit from explicit format constraints
4. Include constraints as a dedicated section — forbidden patterns, required patterns, and coding standards

AI Prompt Architect generates model-optimised prompts that adapt their structure to your target LLM. Whether you're using GPT-4o, Claude, or both, the output is tailored for maximum effectiveness. Try it free.