OpenAI · GPT-5

GPT-5.4 Mini

GPT-5 economics for high-volume routine tasks.

GPT-5.4 Mini is a mid-tier model in OpenAI's GPT-5 series, designed to balance advanced capabilities and affordability for developers and product teams. It features an expansive 400,000-token context window and multimodal support for text, images, and file inputs, enabling diverse applications such as comprehensive document analysis and hybrid data workflows.

This model provides scalability and cost-efficiency for high-volume or demanding projects, making it ideal for tasks requiring detailed reasoning, extensive comprehension, or integration of multiple data types.

Positioned as a mid-tier offering in the GPT-5 lineup, GPT-5.4 Mini builds on its predecessors by introducing significant enhancements in multimodal functionality and expanding the context window to 400,000 tokens, optimizing it for broader developer adoption and diverse use cases.

Background

GPT-4o is a multilingual, multimodal generative pre-trained transformer developed by OpenAI and released in May 2024. It can process and generate text, images and audio.

Wikipedia

Specs

Context window: 400K tokens
Max output: 128K tokens
Input ($/1M tokens): $0.750
Output ($/1M tokens): $4.50
Modalities: File · Image · Text
Weights: Closed

Pricing last synced Apr 27, 2026 via OpenRouter. Confirm against official docs before committing.

Capabilities

Tool use
Vision
Extended thinking
Prompt caching
Open weights

What it excels at

Large context window
Enables handling of up to 400,000 tokens for complex reasoning, extended dialogues, and document processing.
Multimodal capabilities
Processes text, images, and files, supporting mixed-data workflows across various applications.
Cost efficiency
Offers optimized token pricing, making it affordable for sustained development workloads.
Scalable performance
Designed to handle high-volume tasks with consistent reliability and efficiency.

When to use this model

→Extensive document analysis — Handles long-form research papers, legal documents, or enterprise-level text data.
→Mixed data applications — Integrates text, image, and file inputs seamlessly for hybrid workflows.
→API-driven integrations — Supports back-end functionalities for AI-powered app features with scalable performance.
→Customer support systems — Improves customer interactions by retaining extended context during dialogues.