GPT‑5.4 Mini and Nano
openai.com
March 17, 2026
5 min read
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60/100
Summary
GPT-5.4 mini and nano are newly released small models that enhance performance for high-volume workloads. GPT-5.4 mini offers over 2x faster processing and improved capabilities in coding, reasoning, multimodal understanding, and tool use, nearing the performance of the larger GPT-5.4 model in various evaluations.
Key Takeaways
- OpenAI released GPT-5.4 mini and nano, which are designed for high-volume workloads and offer improved performance and efficiency over previous models.
- GPT-5.4 mini runs more than 2x faster than GPT-5 mini and approaches the performance of the larger GPT-5.4 model in several evaluations.
- GPT-5.4 nano is the smallest and cheapest version of GPT-5.4, recommended for tasks requiring speed and cost efficiency, such as classification and data extraction.
- Both models excel in coding workflows, providing low latency and strong performance in tasks like codebase navigation and debugging.
Coverage & discussions
Hacker News · openai.com
Community discussions
Community Sentiment
Positives
- The GPT-5.4 Nano model demonstrates impressive speed, averaging around 200 tokens per second, which enhances its usability in real-time applications.
- Smaller model releases like GPT-5.4 Mini often show significant quality improvements, making them practical for everyday use and more accessible for developers.
- The trend of decreasing costs for mini models is encouraging, as it allows for broader adoption and integration of AI in various real-life applications.
Concerns
- Users report that the GPT models struggle with understanding instructions, leading to frustrations in agentic tasks despite their conversational strengths.
- The performance of older models like GPT-5 Mini is criticized for being slow, with only 55-60 tokens per second, which hampers efficiency in practical scenarios.
- There is concern that the evaluation of models lacks rigor, relying on unscientific methods rather than structured, version-controlled assessments, which could mislead architectural decisions.
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