GPT-5.4 Mini & Nano
The Ultra-Fast AI Sub-Agents for High-Volume Workflows
GPT-5.4 Mini and Nano are the essential choice for AI systems architects who need to deploy cost-effective sub-agents at scale. They bend the price-to-performance curve, making multi-agent workflows economically viable without sacrificing reliability.
Why we love it
- Incredibly cheap API pricing ($0.20/1M input for Nano)
- Massive 400K context window across both small models
- Operates at more than 2x the speed of the previous GPT-5 Mini generation
Things to know
- The Nano model lacks multimodal vision and native tool search capabilities
- Output token costs ($4.50 for Mini) are still relatively high for generative tasks
- Still falls short of the flagship model's deep logic in extremely complex edge cases
About
Executive Summary: GPT-5.4 Mini and Nano represent OpenAI's latest leap into the small-model ecosystem, engineered specifically to serve as the high-speed 'worker layer' in multi-agent architectures. Designed for developers and businesses building complex automated systems, these models bring a massive 400K context window to lightweight tasks, handling everything from real-time code scanning to UI screenshot interpretation at unprecedented speeds.
Running more than twice as fast as their predecessors, these models are shifting the paradigm from relying on a single slow, expensive AI to delegating tasks to a coordinated swarm of fast sub-agents. While the Mini nearly matches the flagship GPT-5.4 in coding evaluations like SWE-Bench Pro, the Nano variant is heavily stripped down for raw speed in classification and data extraction. GPT-5.4 Mini & Nano offers a Paid Only plan, with paid tiers starting at $0.20. It is Less expensive than average for this category. By natively supporting computer use and multimodal reasoning (in the Mini version), they allow developers to orchestrate seamless, low-latency workflows at a fraction of the traditional computational cost.
Key Features
- ✓Process up to 400,000 tokens of context for deep file and document analysis
- ✓Interpret dense UI screenshots in real-time for automated computer use (Mini only)
- ✓Execute automated coding tasks and GitHub PR reviews with near-flagship accuracy
- ✓Classify and extract massive datasets using the ultra-low-latency Nano variant
- ✓Act as dedicated sub-agents in complex, multi-model coding frameworks like Codex
Product Comparison
| Dimension | GPT-5.4 Mini | GLM-5-Turbo |
|---|---|---|
| Core Use Case | Multimodal computer use & code execution | Ultra-fast agentic coding & long-chain tasks |
| API Cost (Input / Output) | $0.75 / $4.50 (Mini) | $0.96 / $3.20 |
| Context Window | 400,000 Tokens | 202,752 Tokens |
| Computer Use / Vision | Native Support (Flawless) | Limited / Missing |
| Sub-Agent Architecture | Pairs perfectly with GPT-5.4 Flagship | Native OpenClaw compatibility |
Frequently Asked Questions
While the GPT-5.4 Mini excels at computer use, UI interpretation, and complex coding loops, the Nano variant has an absolute advantage in high-volume, lightweight data extraction. If your agent needs to 'see' screenshots or write code, use Mini; for pure text classification, use Nano.
They act as delegators. The flagship GPT-5.4 model handles the top-level architectural planning, while spawning GPT-5.4 Mini sub-agents to scan codebases and review files in parallel. This tiered architecture drastically reduces latency and API quota consumption.
The models are paid via the API. GPT-5.4 Mini costs $0.75 per 1M input tokens and $4.50 per 1M output tokens. The heavily stripped Nano costs just $0.20 per 1M input tokens and $1.25 per 1M output tokens, effectively destroying competitors' pricing structures.
No, it does not. To achieve its ultra-low $0.20 input cost, OpenAI explicitly removed multimodal vision, tool search, and computer use from the Nano model. You must upgrade to the Mini version to parse UI screenshots or execute GUI automation.
No. Consistent with OpenAI's enterprise policies, data sent through the API for both the Mini and Nano models is strictly isolated and is not used to train their global foundational models, ensuring proprietary code remains secure.
Yes, absolutely. By leveraging the Mini's massive 400K context window and its native computer use capabilities, you can build a local Python loop that continuously takes screenshots and issues mouse commands, mirroring the core functionality of Manus.