GPT-5.4's Million-Token Context: Finally, No More Truncation

Core Upgrades

GPT-5.4 launched March 5, 2026 with two major improvements:

1. Million-Token Context (Default)

Previous mainstream limits were 128k (GPT-4o) and 200k (Claude 3.5). GPT-5.4 pushes to 1 million tokens—approximately:

• 750,000 Chinese characters
• 10x a novella
• Complete transcript of 10 hours of audio

Enabled by default on API, no extra application needed.

2. Mid-response Steerability

Addresses the real pain point: AI veers off track mid-answer, forcing a full regeneration.

Now you can adjust output direction mid-conversation:

• “steer toward technical detail”
• “stop giving code examples, switch to analogies”
• “pause at this argument, give me the conclusion”

Who Benefits

Million-token context isn’t hype. These use cases see direct gains:

Use case 1: Codebase review
Input: entire monorepo (500k tokens)
Output: global architecture analysis + dependency graph + risk areas

Use case 2: Long document analysis
Input: full text of a 300-page PDF
Output: demand-driven summary, comparison table, knowledge graph

Use case 3: Conversational data analysis
Input: 100 quarterly reports
Output: cross-report trend identification + anomaly detection + hypothesis generation

How Mid-response Steerability Works

Technically, this injects steering vectors dynamically into the model’s attention mechanism, controlling generation direction without interrupting generation.

Not regeneration—real-time adjustment.

# API usage example
response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "analyze this codebase"}],
    mid_steer=[
        {"at": 500, "direction": "more_technical"},
        {"at": 1500, "direction": "fewer_examples"}
    ]
)

Practical Limitations

More tokens means higher inference cost and latency. Million-token context introduces unacceptable latency for some tasks. OpenAI’s guidance: use smaller contexts for short tasks, full context for complex ones.

Also, steering vectors’ effectiveness varies across tasks—complex logical derivation mid-steering can break the reasoning chain.