Google/gemini-3.6-flash-text-to-text

gemini-3.6-flash-text-to-text

Try Gemini 3.6 Flash API by Google for fast text generation, reasoning, writing, coding, and streaming responses through Flaq AI's stable, affordable LLM API.

Text Chat

Related Gemini 3.6 Flash Models

gemini-3.6-flash-text-to-texttext-to-text

gemini-3.6-flash-image-to-textimage-to-text

gemini-3.6-flash-file-analysisfile-analysis

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: 'gemini-3.6-flash-text-to-text',
    messages: [
      {
        role: 'user',
        content: 'Write a concise summary of how transformer models work.'
      }
    ],
    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': 'gemini-3.6-flash-text-to-text',
        'messages': [
            {
                'role': 'user',
                'content': 'Write a concise summary of how transformer models work.',
            }
        ],
        '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": "gemini-3.6-flash-text-to-text",
    "messages": [
      {
        "role": "user",
        "content": "Write a concise summary of how transformer models work."
      }
    ],
    "stream": true,
    "max_tokens": 2048
  }'

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Gemini 3.6 Flash Text to Text Pricing

Parameters	Price	Original Price	Discount

Token: input	$0.7125 per 1M input tokens	$0.7500 per 1M input tokens	95%
Token: output	$3.5625 per 1M output tokens	$3.7500 per 1M output tokens	95%

README

Fast and Efficient Gemini 3.6 Flash Text-to-Text API

Gemini 3.6 Flash Text-to-Text API brings Google's token-efficient, reasoning-capable Flash model to Flaq AI for responsive text workflows. The route is designed for applications that need strong instruction following, coding help, research-oriented drafting, and everyday knowledge work while keeping the integration limited to text input and text output. Its Flaq AI configuration makes it a practical choice for teams that want a capable Gemini API without expanding a text workflow into media or file handling.

Key Features of Gemini 3.6 Flash Text-to-Text API

Efficient Reasoning Workflows: Use a Flash model built to balance responsive operation with capable reasoning for complex prompts and multi-step text tasks.
Coding and Planning Support: Turn requirements, code questions, and technical problems into implementation notes, drafts, explanations, and next-step plans.
High-Quality Instruction Following: Give detailed natural-language directions for writing, transformation, analysis, classification, and structured response workflows.
Focused Text-Only Boundary: Keep the integration aligned with the configured text route, with no image or file input required for the task.
Flexible Response Length: Use the optional output control to tune responses for concise product copy, detailed explanations, or downstream automation.
Streaming-Ready Chat Integration: Support responsive user experiences through the documented streaming and non-streaming request patterns on Flaq AI.

How to Use Gemini 3.6 Flash Text-to-Text API on Flaq AI

Input: Text conversation messages with system instructions, user requests, and prior assistant context where needed.
Output: Generated text for chat experiences, internal tools, content workflows, and API-driven applications.
Response Mode: Choose the documented streaming flow for incremental delivery or a standard JSON response when a complete result is preferred.
Capabilities: Text generation, coding assistance, summarization, rewriting, classification, planning, and knowledge-work support.

Best Use Cases for Gemini 3.6 Flash Text-to-Text API Integration

Developer Copilots: Assist with code explanation, implementation planning, test-case drafts, documentation, and issue-triage workflows.
Business Writing Automation: Create first drafts for briefs, reports, email responses, product copy, and internal communications from structured prompts.
Support Assistants: Classify incoming requests, propose helpful replies, and generate clear operator handoff notes in a text-first support flow.
Research and Analysis Drafting: Summarize supplied notes, organize findings, compare options, and prepare reviewable analytical outlines.
High-Volume Text Applications: Build responsive chat, content, or automation features that need a cost-efficient model configuration and predictable message-based integration.

Note This Flaq AI route is configured for text-to-text interactions. Review generated content before using it for consequential decisions or production changes, and keep use aligned with Google's and Flaq AI's applicable policies.

Gemini 3.6 Flash Text-to-Text API vs Competitors: Comparative Analysis

Gemini 3.6 Flash vs. Gemini 3.5 Flash
Gemini 3.5 Flash remains a practical baseline for routine text workloads. Gemini 3.6 Flash is the newer generation for teams looking for improved token efficiency and stronger support for complex coding and agentic planning tasks.
Gemini 3.6 Flash vs. Gemini 3.7 Flash
Gemini 3.7 Flash is the newer, more capable Flash option for complex workflows. Gemini 3.6 Flash offers a balanced alternative when a text-only integration needs capable reasoning with an efficient configuration.
Gemini 3.6 Flash vs. GPT 5.6 Terra
GPT 5.6 Terra is a broad general-purpose model for advanced language tasks. Gemini 3.6 Flash provides a Gemini API option for teams prioritizing text workflow efficiency, coding support, and message-based integration.
Gemini 3.6 Flash vs. Claude Sonnet 5
Claude Sonnet 5 is often evaluated for long-form reasoning and writing. Gemini 3.6 Flash is a useful alternative for teams comparing responsive, cost-conscious text generation across coding and knowledge-work tasks.
Gemini 3.6 Flash vs. Kimi 2.7
Kimi 2.7 is another strong choice for text and coding workflows. Gemini 3.6 Flash differentiates through Google's Flash model family and its focused Flaq AI text-to-text route.