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02
Ăber GPT-5.4 Nano
Kurz gesagtStand: 23. September 2026
GPT-5.4 Nano ist ein Modell von OpenAI aus der Kategorie Multimodal. Ăber Railwail kostet GPT-5.4 Nano $Â 0.24 je 1 Mio. Input-Token und $Â 1.50 je 1 Mio. Output-Token. Das Kontextfenster umfasst 400'000 Token, eine Antwort bis zu 32'000 Token.
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.
Hintergrund
Ăber OpenAI
GegrĂŒndet 2015 · San Francisco, USA
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.
Training & Lizenz
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.
Lizenz: Proprietary commercial license via OpenAI API and Azure OpenAI.
Sicherheitstests: Evaluated under OpenAI's Preparedness Framework with internal and external red-teaming.
Bekannte Grenzen
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
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);
GPT-5.4 Nano ist ein Modell von OpenAI aus der Kategorie Multimodal. Ăber Railwail lĂ€sst es sich mit einem API-SchlĂŒssel ĂŒber die Railwail-API aufrufen.
Was kostet GPT-5.4 Nano bei Railwail?
Ăber Railwail kostet GPT-5.4 Nano $ 0.24 je 1 Mio. Input-Token und $ 1.50 je 1 Mio. Output-Token. Abgerechnet wird, was jede Anfrage tatsĂ€chlich verbraucht. Bezahlt wird mit vorab gekauften Credits; 1 Credit entspricht $ 0.01.
Wie gross ist das Kontextfenster von GPT-5.4 Nano?
Das Kontextfenster von GPT-5.4 Nano umfasst 400'000 Token. Eine Antwort kann bis zu 32'000 Token lang sein.
Wie schnell ist GPT-5.4 Nano?
FĂŒr GPT-5.4 Nano gibt es bei Railwail noch zu wenige gemessene LĂ€ufe, um eine Laufzeit anzugeben. Sie hĂ€ngt von der Eingabe, den Einstellungen und der Auslastung beim Anbieter ab.
Ist GPT-5.4 Nano besser als BLIP?
Das hÀngt von der Aufgabe ab. GPT-5.4 Nano (OpenAI) und BLIP (Salesforce) sind beide Modelle aus der Kategorie Multimodal. Die Vergleichsseite zeigt Preise und Spezifikationen nebeneinander.
Ja. GPT-5.4 Nano nimmt neben Text auch Bilder als Eingabe an.
Wie nutze ich GPT-5.4 Nano ĂŒber die API?
Erstelle einen Railwail-API-SchlĂŒssel und sende deine Anfrage mit der Modell-ID gpt-5-4-nano. Codebeispiele fĂŒr curl, Python und JavaScript stehen im Abschnitt API auf dieser Seite.
