Billed by the tokens actually used; the unused part of the reservation is refunded.
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10 free credits (US$0.10) when you sign up with Google
Usable 24 hours after sign-up, up to 5 runs per day and at most 2 credits per run. Other sign-in methods start without credits. Enough for 62 runs of this model.
02
About GPT-5.4 Nano
TL;DRAs of 23 September 2026
GPT-5.4 Nano is a model by OpenAI in the Multimodal category. On Railwail, GPT-5.4 Nano costs US$0.24 per 1M input tokens and US$1.50 per 1M output tokens. The context window holds 400,000 tokens, and one response can be up to 32,000 tokens long.
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.
Background
About OpenAI
Founded 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 & license
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.
Safety testing: Evaluated under OpenAI's Preparedness Framework with internal and external red-teaming.
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
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 is a model by OpenAI in the Multimodal category. On Railwail you can call it with an API key through the Railwail API.
How much does GPT-5.4 Nano cost on Railwail?
On Railwail, GPT-5.4 Nano costs US$0.24 per 1M input tokens and US$1.50 per 1M output tokens. You are charged for what each request actually uses. Usage is paid from prepaid credits; 1 credit equals US$0.01.
What is the context window of GPT-5.4 Nano?
The context window of GPT-5.4 Nano holds 400,000 tokens. One response can be up to 32,000 tokens long.
How fast is GPT-5.4 Nano?
There are not enough measured runs of GPT-5.4 Nano on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.
Is GPT-5.4 Nano better than BLIP?
That depends on the task. GPT-5.4 Nano (OpenAI) and BLIP (Salesforce) are both models in the Multimodal category. The comparison page shows their prices and specifications side by side.
Yes. GPT-5.4 Nano accepts images as input in addition to text.
How do I use GPT-5.4 Nano through the API?
Create a Railwail API key and send your request with the model ID gpt-5-4-nano. Code examples for curl, Python and JavaScript are in the API section of this page.
