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02
Om GPT-5.4 Nano
Kort sagtPer 23. september 2026
GPT-5.4 Nano er en modell fra OpenAI i kategorien Multimodal. På Railwail koster GPT-5.4 Nano 0,24 USD per 1M inndatatokens og 1,50 USD per 1M utdatatokens. Kontekstvinduet inneholder 400 000 tokens, og ett svar kan være opptil 32 000 tokens langt.
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.
Bakgrunn
Om OpenAI
Grunnlagt 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.
Trening og lisens
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.
Lisens: Proprietary commercial license via OpenAI API and Azure OpenAI.
Sikkerhetstesting: Evaluated under OpenAI's Preparedness Framework with internal and external red-teaming.
Kjente begrensninger
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 er en modell fra OpenAI i kategorien Multimodal. På Railwail kan du kalle den med en API-nøkkel gjennom Railwail API.
Hvor mye koster GPT-5.4 Nano på Railwail?
På Railwail koster GPT-5.4 Nano 0,24 USD per 1M inndatatokens og 1,50 USD per 1M utdatatokens. Du betaler for det hver forespørsel faktisk bruker. Bruk betales fra forhåndskjøpte kreditter; 1 kreditt tilsvarer 0,01 USD.
Hvor stort er kontekstvinduet til GPT-5.4 Nano?
Kontekstvinduet til GPT-5.4 Nano inneholder 400 000 tokens. Ett svar kan være opptil 32 000 tokens langt.
Hvor rask er GPT-5.4 Nano?
Det finnes ennå ikke nok målte kjøringer av GPT-5.4 Nano på Railwail til å angi en kjøretid. Det avhenger av inndataene, innstillingene og belastningen hos leverandøren.
Er GPT-5.4 Nano bedre enn BLIP?
Det avhenger av oppgaven. GPT-5.4 Nano (OpenAI) og BLIP (Salesforce) er begge modeller i kategorien Multimodal. Sammenligningssiden viser prisene og spesifikasjonene deres side ved side.
Ja. GPT-5.4 Nano godtar bilder som inndata i tillegg til tekst.
Hvordan bruker jeg GPT-5.4 Nano gjennom API-en?
Opprett en Railwail API-nøkkel og send forespørselen din med modell-ID-en gpt-5-4-nano. Kodeeksempler for curl, Python og JavaScript finnes i API-delen av denne siden.
