OpenAI's efficient mid-tier model. 2x faster than its predecessor, 400k context, approaches GPT-5.4 quality on SWE-Bench Pro at a fraction of the cost.
Released March 17, 2026, GPT-5.4 mini brings the strengths of GPT-5.4 to a smaller, faster model designed for the subagent era. 400K context, vision input, integrated tool use, and 2x faster latency than GPT-5 mini. Significant gains on coding, reasoning, multimodal understanding and tool use; approaches the full GPT-5.4 on SWE-Bench Pro and OSWorld-Verified. Recommended for subagent workflows, customer-facing chat, coding assistants and high-volume API workloads.
OpenAI was founded in December 2015 as a non-profit AI research organisation and transitioned to a capped-profit structure in 2019. The GPT lineage spans GPT-1 (2018) through GPT-5 (mid-2025) and the GPT-5.x family (2025-2026) which unified the o-series reasoning models with the general-purpose GPT line. GPT-5.4 mini and nano were announced on March 17, 2026 as the small-model tier of the GPT-5.4 generation. OpenAI is backed by Microsoft, Khosla, Andreessen Horowitz, Thrive Capital and Sequoia, with total funding above $60 billion and a 2026 valuation above $300 billion.
Unified Transformer (mid-tier, with integrated 'Thinking' tier)
GPT-5.4 mini was announced March 17, 2026 alongside GPT-5.4 nano as the small-model tier of the GPT-5.4 generation. It is a smaller variant of the unified GPT-5.4 architecture, retaining native text + image input, an integrated 'Thinking' tier for reasoning on demand, and the full tool-use API, while running more than 2x faster than GPT-5 mini at significantly lower cost. Pretraining used a similar multi-trillion-token mixture as GPT-5.4 with heavier distillation pressure from larger teacher models. Post-training included supervised fine-tuning, RLHF and reinforcement learning against verifiable rewards on coding, reasoning and tool-use trajectories. On evaluations such as SWE-Bench Pro and OSWorld-Verified, GPT-5.4 mini approaches the performance of full GPT-5.4 while costing roughly one-third as much.
Paramètres
Undisclosed (estimated tens of billions of parameters, likely sparse MoE)
Approaches full GPT-5.4 on SWE-Bench Pro and OSWorld-Verified
400K token context window
Native multimodal input: text and images
Integrated 'Thinking' tier activates for harder reasoning
Native tool use, function calling and parallel tool calls
Designed for the subagent era: works well under an orchestrator
Strong coding, classification and extraction performance
Available in ChatGPT, Codex CLI and the OpenAI API
Regional processing endpoints available with 10% uplift
Best for: subagent workflows, customer-facing chat, coding assistants, high-volume API workloads.
Formation & licence
Pretrained on a multi-trillion-token mixture of web text, code, scientific papers and licensed data; heavy distillation from larger GPT-5.4 teacher models. Post-training uses supervised fine-tuning, RLHF and RL against verifiable rewards. 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-mini",
"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-mini",
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-mini",
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-mini", [
{ role: "user", content: "Explain what a vector database is in two sentences." },
], { max_tokens: 1024 });
console.log(res.choices[0].message.content);
