GPT-5.4 nano is the lightest member of the GPT-5.4 family, released alongside Mini in March 2026. 400K context window, vision input, optimized for classification, data extraction, ranking and coding subagents that handle simpler supporting tasks. A major upgrade over GPT-5 nano, designed for the subagent era of agentic workflows where orchestrator models delegate to many cheap workers.
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.
Unified Transformer (small, optimized for high-throughput subagent workloads)
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.
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.
Entraînement et licence
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.
Licence: Proprietary commercial license via OpenAI API and Azure OpenAI.
curl https://railwail.com/api/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5-4-nano",
"messages": [
{
"role": "user",
"content": "Explain what a vector database is in two sentences."
}
],
"max_tokens": 1024
}'
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["RAILWAIL_API_KEY"],
base_url="https://railwail.com/api/v1",
)
completion = client.chat.completions.create(
model="gpt-5-4-nano",
messages=[
{
"role": "user",
"content": "Explain what a vector database is in two sentences.",
},
],
max_tokens=1024,
)
print(completion.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.RAILWAIL_API_KEY,
baseURL: "https://railwail.com/api/v1",
});
const completion = await client.chat.completions.create({
model: "gpt-5-4-nano",
messages: [
{
role: "user",
content: "Explain what a vector database is in two sentences."
}
],
max_tokens: 1024
});
console.log(completion.choices[0].message.content);
// npm install railwail
import railwail from "railwail";
const rw = railwail(process.env.RAILWAIL_API_KEY);
const res = await rw.chat("gpt-5-4-nano", [
{ role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });
console.log(res.choices[0].message.content);
