xAI/grok-4.6-text-to-text

grok-4.6-text-to-text

Try Grok 4.6 API by xAI for text reasoning, writing, coding, analysis, and streaming responses through Flaq AI's stable, affordable unified LLM API.

Text Chat

Related Grok 4.6 Models

grok-4.6-text-to-texttext-to-text

grok-4.6-image-to-textimage-to-text

Documentation

API Examples

JavaScriptPythoncURL

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: 'grok-4.6-text-to-text',
    messages: [
      {
        role: 'user',
        content: 'Explain the key differences between REST and GraphQL APIs.'
      }
    ],
    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': 'grok-4.6-text-to-text',
        'messages': [
            {
                'role': 'user',
                'content': 'Explain the key differences between REST and GraphQL APIs.',
            }
        ],
        '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": "grok-4.6-text-to-text",
    "messages": [
      {
        "role": "user",
        "content": "Explain the key differences between REST and GraphQL APIs."
      }
    ],
    "stream": true,
    "max_tokens": 2048
  }'

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Grok 4.6 Text to Text Pricing

Parameters	Price	Original Price	Discount

Token: input	$2.0000 per 1M input tokens	-	Standard
Token: output	$6.0000 per 1M output tokens	-	Standard

README

Grok 4.6 Text-to-Text API (Advanced Reasoning and Coding)

Grok 4.6 Text-to-Text API brings xAI's reasoning-focused model to Flaq AI for demanding text workflows. Built for software engineering, structured problem solving, and professional knowledge work, this Grok API integration helps teams turn detailed instructions into useful written output, technical explanations, and implementation-oriented responses. The Flaq route is intentionally scoped to text input and text output, making it a clear choice when a production workflow needs focused language-model behavior without attaching media files.

Key Features of Grok 4.6 Text-to-Text API

Reasoning-Led Text Workflows: Handle multi-step prompts, detailed requirements, and structured questions with a model positioned for technical and knowledge-work tasks.
Strong Coding Assistance: Turn product requirements, bug reports, and implementation questions into code-oriented explanations, plans, reviews, and drafts that fit developer workflows.
Professional Knowledge Work Support: Create summaries, briefs, analyses, and operational documentation from natural-language instructions without changing tools between routine tasks.
Focused Text-Only Integration: Keep the route aligned with text-to-text use cases, so teams can set clear expectations around the input boundary in chat, automation, and back-office flows.
Flexible Output Control: Use the available output-length control to balance concise answers, detailed technical responses, and downstream processing needs.
Production-Friendly API Workflow: Send structured conversation messages through Flaq AI and integrate generated text into applications, internal tools, or review pipelines.

How to Use Grok 4.6 Text-to-Text API on Flaq AI

Input: Text-based conversation messages and natural-language instructions for analysis, drafting, coding, or question answering.
Output: Text responses suitable for display, internal review, automation, or follow-up processing.
Message Context: Structure requests with the supported conversational roles to provide task instructions, user requirements, and prior context.
Capabilities: Reasoning-oriented text generation, coding assistance, professional writing, technical explanation, and structured problem-solving support.

Best Use Cases for Grok 4.6 Text-to-Text API Integration

Software Engineering Workflows: Draft implementation plans, explain code behavior, prepare refactoring notes, and accelerate issue-triage conversations.
Technical Documentation: Turn source requirements and product notes into clearer guides, release drafts, specifications, and engineering summaries.
Knowledge-Work Automation: Help teams prepare research outlines, internal briefs, decision memos, and structured first drafts from detailed prompts.
Support and Operations Assistants: Build text-first helpers that classify requests, produce response drafts, and surface next-step guidance for operators.
Analytical Prototyping: Explore complex questions, compare options, and generate organized reasoning scaffolds before a human review stage.

Note Validate generated content before using it for high-impact decisions, production changes, legal, medical, financial, or safety-critical work. Keep prompts and downstream uses aligned with applicable xAI and Flaq AI policies.

Grok 4.6 Text-to-Text API vs Competitors: Comparative Analysis

Grok 4.6 vs. Grok 4.5
Grok 4.5 remains a practical option for established Grok integrations. Grok 4.6 is the newer reasoning and coding-focused generation, making it a stronger fit when the task involves more demanding technical planning or professional knowledge work.
Grok 4.6 vs. Gemini 3.7 Flash
Gemini 3.7 Flash is positioned for efficient agentic and multimodal workflows. Grok 4.6 Text-to-Text keeps the Flaq integration focused on text reasoning and coding-oriented output, so the best choice depends on the workflow's input boundary and task evaluation results.
Grok 4.6 vs. Claude Sonnet 5
Claude Sonnet 5 is widely considered for careful long-form and coding tasks. Grok 4.6 offers another frontier-oriented option for teams that want to test reasoning, software engineering, and knowledge-work performance within the same text API pattern.
Grok 4.6 vs. GPT 5.6 Terra
GPT 5.6 Terra supports broad general-purpose AI workloads. Grok 4.6 differentiates with its xAI model family and a text route designed for implementation-focused prompts, technical explanations, and structured professional work.
Grok 4.6 vs. Kimi 2.7
Kimi 2.7 is a useful alternative for text and coding scenarios. Grok 4.6 is a compelling option when teams want a reasoning-led model to evaluate on complex engineering and analytical prompts.