OpenAI launched GPT-5.4 Mini and Nano alongside the full GPT-5.4 model in March 2026. These compact variants deliver the reasoning improvements of the 5.4 generation at price points that make high-quality chatbot deployment affordable for startups and mid-size companies. Mini comes within 5% of the full model on programming benchmarks while running twice as fast. Nano offers the lowest per-token cost in OpenAI’s lineup, targeting high-volume, latency-sensitive workloads.
How Mini and Nano Compare to Full GPT-5.4
- GPT-5.4 Mini scores within 5% of full GPT-5.4 on HumanEval, GPQA, and MMLU benchmarks
- GPT-5.4 Nano offers 3x lower per-token cost with competitive performance on classification and extraction
- Mini supports a 256K token context window; Nano supports 64K tokens
- Both variants include the improved instruction following from the 5.4 generation
- Nano generates 120+ tokens per second, making it suitable for real-time conversational interfaces
The Economics of Compact LLMs for Chatbots
Running a customer-facing chatbot on a flagship model gets expensive fast. A support chatbot handling 100,000 conversations per day on the full GPT-5.4 model could cost several thousand dollars daily in API fees. GPT-5.4 Nano handles the same volume for a fraction of that, while maintaining the conversational quality that users expect.
GPT-5.4 Nano cuts chatbot API costs by up to 70% compared to the flagship model while maintaining the instruction-following improvements that define the 5.4 generation.
The tiered approach lets teams route requests intelligently. Simple questions go to Nano, complex reasoning goes to Mini, and only the hardest problems escalate to the full model. This routing strategy, already common in production, becomes more effective when all three variants share the same training methodology.
Practical Performance for Developer Workflows
Mini is particularly strong for coding assistants. On the SWE-bench Verified evaluation, Mini resolved 42% of real-world GitHub issues compared to 47% for the full model. That 5-point gap is narrow enough that many development teams will prefer Mini’s speed and cost advantages over the marginal accuracy improvement of the full model.
Nano excels at structured tasks: JSON extraction, text classification, sentiment analysis, and content moderation. These workloads do not need deep reasoning but do need speed and low cost. Nano delivers both.
When to Choose Mini, Nano, or Full GPT-5.4
Choose Nano for high-volume, straightforward tasks where speed and cost matter most. Choose Mini for coding, analysis, and moderate-complexity reasoning where you want near-flagship quality without flagship pricing. Reserve the full GPT-5.4 for tasks that require the extended 1-million-token context window, maximum accuracy, or the Pro reasoning mode.
All three variants are available now through the OpenAI API with identical authentication and request formats. Switching between them requires only a model name change in your API calls.
