GPT 5.4 Context Window Explained: Why the 1M Token Claim Matters

Mar 6, 2026

Cowrie

Dev @ Bswen

I was working on a large codebase analysis project when I noticed something confusing in the GPT 5.4 CLI output. The announcement claimed a 1 million token context window, but my CLI kept showing 258K as the threshold.

Was this false advertising? A bug? Or something else entirely?

Context window: 258K tokens (compaction threshold)

That’s when I realized I needed to understand what context windows actually mean in practice, not just in marketing materials.

The Confusion

Here’s what I found in the wild. OpenAI’s GPT 5.4 announcement prominently featured “1M token context window” as a breakthrough feature. But when I actually used the model:

1. The CLI displayed 258K as a significant threshold
2. Community members on r/codex confirmed seeing the same behavior
3. No clear explanation of why the numbers differed

User A: "The announcement claims 1M tokens but CLI shows 258K?"
User B: "I can verify firsthand that's what it compacts at"

This gap between marketing and implementation isn’t unique to OpenAI. But understanding why it exists changed how I think about context windows entirely.

What Is a Context Window, Really?

A context window is the maximum amount of text an LLM can process in a single request. This includes:

• Your input text
• System instructions
• Conversation history
• Any retrieved documents
• The model’s generated output

Think of it like RAM in your computer. You might have 16GB installed (theoretical maximum), but your OS starts swapping to disk well before you hit that limit (practical threshold).

Token Basics

For context:

• 1 token ≈ 4 characters or 0.75 words in English
• 1M tokens ≈ 750,000 words or 3,000 pages of text
• 258K tokens ≈ 193,500 words or 770 pages

So 258K tokens is still a massive amount of context. But why is it different from the advertised 1M?

The 1M vs 258K Discrepancy

Here’s what I learned after digging through documentation and community discussions:

┌─────────────────────────────────────────────────────────┐
│                 GPT 5.4 Context Window                   │
├─────────────────────────────────────────────────────────┤
│                                                          │
│  ████████████████████████████████████░░░░░░░░░░░░░░░░░  │
│  │←───── 258K Compaction Point ─────→│←── 742K Overflow│
│                                      │                  │
│  Normal operations       │  Emergency buffer           │
│  Context compaction      │  Hard ceiling (theoretical) │
│  begins here             │  Model cannot exceed        │
│                                                          │
└─────────────────────────────────────────────────────────┘

The 1M tokens is the hard ceiling - the absolute maximum the model architecture can theoretically handle.

The 258K is the proactive management point - where the system begins optimizing context to maintain performance.

This is not false advertising. It’s architectural nuance that matters for real-world usage.

What Is Context Compaction?

Context compaction is the technique the model uses to manage large conversations. Here’s how it works:

Step 1: Monitor Token Count
         Current tokens: 250K / 258K threshold

Step 2: Threshold Approaching
         System prepares for compaction

Step 3: Summarize Earlier Context
         Old messages → Key points summary

Step 4: Prioritize Recent Information
         Recent messages preserved in full detail

Step 5: Drop Oldest Context (if needed)
         Least relevant information removed

When your conversation approaches 258K tokens, the model doesn’t just crash or fail. It proactively:

1. Summarizes earlier messages into key points
2. Preserves recent and critical information
3. Drops truly obsolete context

This is similar to how your brain works in a long conversation. You remember the key points from 2 hours ago, but not every word spoken.

Why Compaction Matters

Without compaction, processing 1M tokens every request would be:

• Computationally expensive - longer processing times
• Quality-degrading - the “lost in the middle” problem
• Costly - more tokens = higher API costs
• Slow - increased latency for every response

The “Lost in the Middle” Problem

Here’s something I didn’t expect. Research shows that when LLMs are given extremely long contexts, they tend to overlook information in the middle.

┌──────────────────────────────────────────────────────┐
│         Information Retention by Position            │
├──────────────────────────────────────────────────────┤
│                                                       │
│  HIGH    ████████████                      ████████████
│           Beginning                            End    │
│                                                       │
│  LOW                           ████                   │
│                                  Middle               │
│                                                       │
└──────────────────────────────────────────────────────┘

This means simply having 1M tokens of context doesn’t guarantee effective use of all that context. Information in the middle might be ignored.

Practical tip: Place critical instructions at both the beginning AND end of your context for optimal performance.

Real-World Scenarios

Let me show you what this means in practice.

Scenario 1: Large Codebase Analysis

Task: Analyze a 500K token codebase for security vulnerabilities

What happens:
├─ First 258K tokens: Full detail analysis
├─ Conversation grows: Earlier sections get compacted
├─ Key findings: Preserved in summary
└─ Detailed context: May be summarized

Workaround: Process in sections, maintain running summary

Scenario 2: Long Debugging Session

Task: Multi-hour debugging with extensive context

What happens:
├─ Natural growth toward 258K limit
├─ Old debugging attempts: Summarized
├─ Current focus area: Full detail maintained
└─ Dead-end explorations: May be dropped

Workaround: Start fresh conversations for new directions

Scenario 3: Document Processing

Task: Process 800K token document

What happens:
├─ Document fits in 1M theoretical limit
├─ Processing may involve internal chunking
├─ Middle sections: May receive less attention
└─ Edge sections: Better retention

Workaround: Split into logical chunks, process sequentially

Practical Guidance

Here’s what I do now when working with large contexts:

1. Count Before You Submit

Before sending large content, I check the token count:

import tiktoken

def count_tokens(text, model="gpt-5"):
    encoding = tiktoken.encoding_for_model(model)
    return len(encoding.encode(text))

# Check before submitting
if count_tokens(my_codebase) > 258000:
    print("Warning: Approaching compaction threshold")

2. Use Truncation Strategies

OpenAI provides truncation options:

{
  "truncation_strategy": {
    "type": "auto",
    "last_messages": 10
  }
}

3. Strategic Context Placement

┌─────────────────────────────────────────────┐
│ CRITICAL INSTRUCTIONS (Beginning)           │
├─────────────────────────────────────────────┤
│ Long context content (Documents, code)      │
├─────────────────────────────────────────────┤
│ REMINDER OF KEY REQUIREMENTS (End)          │
└─────────────────────────────────────────────┘

4. Monitor Token Pressure

I track before/after token counts to detect when critical details might be pruned.

Model Comparison

Here’s how GPT 5.4 compares to other models:

Model	Context Window	Compaction Point
GPT 5.4	1M tokens	~258K
GPT-4.1	1M tokens	Varies
GPT-4 Turbo	128K tokens	~100K
Claude	200K tokens	~150K

Key insight: Every model has a gap between theoretical maximum and practical effective limit.

What This Means for You

After understanding this, I changed my approach:

1. 1M tokens is the ceiling, not the target - Think of it as overflow capacity
2. 258K is the proactive management point - Context compaction begins here
3. Compaction is a feature, not a bug - It preserves performance and quality
4. Strategic context management matters - More than raw size

The real question isn’t “Is the 1M claim real?” but rather “How do I work effectively within both the theoretical and practical limits?”

Bottom Line

GPT 5.4’s 1M token context window is real. The 258K CLI display reflects the practical compaction threshold - an architectural feature that balances theoretical maximum with computational efficiency and output quality.

Understanding this distinction helps you:

• Plan context usage more effectively
• Avoid surprises when working with large inputs
• Optimize your prompts for best results
• Make informed decisions about when to chunk vs. when to use full context

The gap between marketing claims and implementation details is frustrating. But in this case, it’s not deception - it’s engineering reality.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

• 👨‍💻 OpenAI Chat API Documentation
• 👨‍💻 OpenAI Cookbook - Context Management Examples
• 👨‍💻 Reddit r/Codex Discussion
• 👨‍💻 Lost in the Middle: How Language Models Use Long Contexts

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

Comments

Tags:

• llm
• ai
• career
• productivity

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