GPT-5.4 Nano
OpenAI's smallest and cheapest GPT-5.4 variant. Built for high-volume classification, extraction and coding subagents at edge-grade latency.
GPT-5.4 Nano is multimodal AI model from OpenAI, priced at €0.000 per 1M input tokens with a 400K tokens context window.
About this model
0.7
Pricing
API Integration
Use our OpenAI-compatible API to integrate GPT-5.4 Nano into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple — just pass a string
const reply = await rw.run("gpt-5-4-nano", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("gpt-5-4-nano", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("gpt-5-4-nano", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive — OpenAI's GPT-5.4 Nano
OpenAI is the AI research lab founded in December 2015 by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever and others. The company shipped GPT-1 through GPT-5 and the unified GPT-5.x line (2025-2026). GPT-5.4 nano was released alongside GPT-5.4 mini on March 17, 2026 as the smallest, cheapest variant of the GPT-5.4 generation, aimed at the 'subagent era' of agentic workflows. OpenAI is backed by Microsoft and other major investors with a 2026 valuation above $300 billion.
Visit OpenAI →GPT-5.4 nano is the lightest member of the GPT-5.4 family, released March 17, 2026 as OpenAI's smallest and cheapest reasoning-capable model. It is heavily distilled from larger GPT-5.4 teacher models and trained to excel at classification, extraction, ranking and coding subagent tasks. Architecturally it retains the unified GPT-5.4 design: native text + image input, integrated 'Thinking' tier on demand, and full tool-use API including parallel tool calls. The 400K context window is unusually large for a nano-class model and supports long-document subagent work. Post-training emphasized reliability on structured outputs, JSON schema adherence and function calling, since nano-class models are typically deployed in deterministic pipelines.
- Parameters
- Undisclosed (estimated single-digit-billion-parameter class)
- Context
- 400K tokens
- Smallest and cheapest GPT-5.4 variant
- 400K token context window
- Native multimodal input: text and images
- Integrated 'Thinking' tier for occasional harder tasks
- Strong on classification, extraction, ranking and coding subagent tasks
- Native tool use, function calling and parallel tool calls
- Reliable structured JSON output and schema adherence
- Edge-grade latency suitable for real-time pipelines
- Major upgrade over GPT-5 nano on every measured benchmark
- Available in ChatGPT (free tier) and the OpenAI API
- Best for: classification, data extraction, ranking, coding subagents under an orchestrator.
Heavily distilled from larger GPT-5.4 teacher models. Pretraining uses a multi-trillion-token mixture; post-training combines supervised fine-tuning, RLHF and RL against verifiable rewards with a strong emphasis on structured-output reliability. Knowledge cutoff approximately late 2025.
License: Proprietary commercial license via OpenAI API and Azure OpenAI.
Known limitations
- Below GPT-5.4 mini on hard reasoning and agentic coding benchmarks
- Limited depth on niche or long-tail topics
- No native audio or video input
- Knowledge cutoff in late 2025
- Thinking mode is available but slow relative to model size
Research papers
Frequently asked questions
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