OpenAI GPT 5.4 API for LLM, reasoning, coding help, summaries, and business writing. Stable and affordable for production teams and scalable workflows. Built for free testing and stable API workflows.
Related Gpt 5.4 Models
API Examples
Submit Example
const response = await fetch('https://api.flaq.ai/api/v1/chat/completions', {
method: 'POST',
headers: {
Authorization: 'Bearer YOUR_API_KEY',
Accept: 'text/event-stream',
'Content-Type': 'application/json'
},
body: JSON.stringify({
model: 'gpt-5.4-text-to-text',
messages: [
{
role: 'user',
content: 'Write a concise product update for a developer audience.'
}
],
stream: true,
max_tokens: 2048
})
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = '';
let assistantText = '';
while (true) {
const { done, value } = await reader.read();
if (done) break;
buffer += decoder.decode(value, { stream: true });
const frames = buffer.split('\n\n');
buffer = frames.pop() || '';
for (const frame of frames) {
const lines = frame.split('\n').filter(Boolean);
let eventName = 'message';
const dataLines = [];
for (const line of lines) {
if (line.startsWith('event:')) {
eventName = line.slice(6).trim();
} else if (line.startsWith('data:')) {
dataLines.push(line.replace(/^data:\s*/, ''));
}
}
const raw = dataLines.join('\n').trim();
if (raw === '[DONE]') {
console.log('\nFinal text:', assistantText);
continue;
}
let payload;
try {
payload = JSON.parse(raw);
} catch {
continue;
}
if (eventName === 'error' || payload.error) {
const msg = payload.error?.message ?? payload.message ?? 'Chat request failed';
throw new Error(msg);
}
const delta = payload.choices?.[0]?.delta;
if (delta?.content) {
assistantText += delta.content;
console.log(assistantText);
}
}
}
Submit Example
import json
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/chat/completions',
headers={
'Authorization': 'Bearer YOUR_API_KEY',
'Accept': 'text/event-stream',
'Content-Type': 'application/json',
},
json={
'model': 'gpt-5.4-text-to-text',
'messages': [
{
'role': 'user',
'content': 'Write a concise product update for a developer audience.',
}
],
'stream': True,
'max_tokens': 2048,
},
stream=True,
)
response.raise_for_status()
event_name = 'message'
assistant_text = ''
for raw_line in response.iter_lines(decode_unicode=True):
if not raw_line:
event_name = 'message'
continue
if raw_line.startswith('event:'):
event_name = raw_line.replace('event:', '', 1).strip()
continue
if raw_line.startswith('data:'):
raw_data = raw_line.replace('data:', '', 1).strip()
if raw_data == '[DONE]':
print('\nFinal text:', assistant_text)
continue
payload = json.loads(raw_data)
if event_name == 'error' or payload.get('error'):
error = payload.get('error') or payload
raise RuntimeError(error.get('message', 'Chat request failed'))
choices = payload.get('choices') or []
if choices:
delta = choices[0].get('delta') or {}
content = delta.get('content')
if content:
assistant_text += content
print(content, end='', flush=True)
Submit Example
curl -N -X POST "https://api.flaq.ai/api/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Accept: text/event-stream" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.4-text-to-text",
"messages": [
{
"role": "user",
"content": "Write a concise product update for a developer audience."
}
],
"stream": true,
"max_tokens": 2048
}'
GPT 5.4 Text to Text Pricing
| Parameters | Price | Original Price | Discount |
|---|
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Fast & Affordable GPT 5.4 Text-to-Text API (a fast and affordable OpenAI model for professional AI workflows)
GPT 5.4 Text-to-Text API on Flaq AI provides OpenAI API access for professional workflows that need dependable reasoning, coding help, and practical chat experiences. This fast, affordable, and practical OpenAI API integration helps developers build text-to-text experiences with flexible conversation context, task-aware prompting, supported tool workflows, and route-specific settings on Flaq AI. Built for production workloads, it gives teams a stable way to add advanced AI capability without managing model infrastructure or writing provider-specific plumbing from scratch.
Key Features of GPT 5.4 Text-to-Text API
- Advanced Text Reasoning: Handle complex prompts, multi-step instructions, code tasks, and professional writing workflows with reliable language understanding.
- Conversation-Aware Output: Preserve useful prior context across multi-turn chat so teams can refine drafts, decisions, code explanations, and research notes.
- Flexible API Controls: Use conversation context, task-aware prompting, supported tool workflows, and route-specific settings on Flaq AI to guide simple requests, deep analysis, and production automation.
- Production Tool Support: Connect supported tool workflows, retrieval, automation, and advanced application features without surfacing low-level provider settings.
- Cost-Effective Integration: Build text-to-text features through Flaq AI with clear routing, stable model pages, and durable API messaging for production workflows.
- Developer-Friendly Workflow: Use natural-language instructions, structured prompts, and route-specific inputs to move quickly from prototype to production.
How to Use GPT 5.4 Text-to-Text API for Text Generation on Flaq AI
- Input: Natural language prompts, system instructions, conversation context, and structured task requirements.
- Output: High-quality text responses delivered through a stable chat API integration on Flaq AI.
- Route Configuration: Designed for conversational text workflows with flexible context and follow-up support.
- Configuration: flexible conversation context, task-aware prompting, supported tool workflows, and route-specific settings on Flaq AI.
- Capabilities: Long-form reasoning, coding help, summarization, multilingual writing, structured output planning, and conversational assistance through fast, affordable, and practical OpenAI API integration.
Best Use Cases for GPT 5.4 Text-to-Text API Integration
- Developer Productivity: Draft code, explain systems, review implementation ideas, and accelerate engineering workflows through a dependable chat API.
- Business Writing & Analysis: Create briefs, reports, summaries, emails, and strategic documents with consistent tone and clear structure.
- Research & Knowledge Work: Analyze complex source material, compare options, and generate synthesis for product, market, and operations teams.
- Customer Support Automation: Power helpful support assistants that can answer questions, follow policies, and maintain a natural conversational style.
- Multilingual Content Production: Create and adapt content across languages while keeping meaning, formatting, and brand voice aligned.
Note Please ensure your prompts, uploaded files, and application workflows comply with OpenAI safety and usage guidelines. If an error occurs, review the input for restricted content, simplify the request, and try again.
GPT 5.4 Text-to-Text vs Competitors: Comparative Analysis
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GPT 5.4 vs. Claude Opus 4.7 Claude Opus 4.7 is strong for careful long-horizon reasoning and Claude-style agent workflows. GPT 5.4 offers OpenAI-native behavior, broad application tooling, and a familiar API path for teams already building on the OpenAI ecosystem.
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GPT 5.4 vs. Claude Sonnet 4.6 Claude Sonnet 4.6 focuses on a fast balance of intelligence and latency. GPT 5.4 is a strong choice when developers want OpenAI model behavior, flexible chat workflows, and integrated text-to-text capabilities on Flaq AI.
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GPT 5.4 vs. Gemini Gemini models are attractive for Google-native multimodal and workspace-adjacent use cases. GPT 5.4 differentiates with OpenAI-style reasoning, dependable text quality, and production-friendly integration.
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GPT 5.4 vs. DeepSeek Reasoner DeepSeek Reasoner is valued for affordable reasoning experiments. GPT 5.4 provides a managed API experience for professional text-to-text workflows, especially where reliability and ecosystem support matter.
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GPT 5.4 vs. Llama Llama models give teams open-model flexibility and deployment control. GPT 5.4 removes infrastructure overhead and delivers a scalable API for developers who need fast integration and consistent output quality.