Anthropic/claude-opus-5.5-text-to-text

claude-opus-5.5-text-to-text

Try Claude Opus 5.5 API by Anthropic for text reasoning, writing, coding, and analysis through Flaq AI's stable, affordable API with streaming responses.

Text Chat

Related Claude Opus 5.5 Models

claude-opus-5.5-text-to-texttext-to-text

claude-opus-5.5-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: 'claude-opus-5.5-text-to-text',
    messages: [
      {
        role: 'user',
        content: 'Draft a technical decision memo for this architecture choice.'
      }
    ],
    stream: true,
    max_tokens: 2048
  })
});

if (!response.ok) {
  const errorBody = await response.json().catch(() => null);
  throw new Error(errorBody?.error?.message ?? errorBody?.message ?? `HTTP ${response.status}`);
}

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(/\r?\n\r?\n/);
  buffer = frames.pop() || '';

  for (const frame of frames) {
    const lines = frame.split(/\r?\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': 'claude-opus-5.5-text-to-text',
        'messages': [
            {
                'role': 'user',
                'content': 'Draft a technical decision memo for this architecture choice.',
            }
        ],
        '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": "claude-opus-5.5-text-to-text",
    "messages": [
      {
        "role": "user",
        "content": "Draft a technical decision memo for this architecture choice."
      }
    ],
    "stream": true,
    "max_tokens": 2048
  }'

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Claude Opus 5.5 Text to Text Pricing

Parameters	Price	Original Price	Discount

Token: input	$3.6000 per 1M input tokens	$4.0000 per 1M input tokens	90%
Token: output	$18.0000 per 1M output tokens	$20.0000 per 1M output tokens	90%

README

Advanced Claude Opus 5.5 Text-to-Text API (Anthropic Coding and Knowledge Work)

Claude Opus 5.5 Text-to-Text API brings Anthropic's model for long-running agentic coding and knowledge work to Flaq AI. It supports complex software architecture questions, analytical writing, and multi-turn conversations when the application supplies relevant message history. Developers can use the text-to-text API to build assistants and knowledge workflows without managing model infrastructure.

Key Features of Claude Opus 5.5 Text-to-Text API

Frontier Cognitive Reasoning: Tackle challenging analytical dilemmas, navigate multi-layered logic proofs, and formulate well-grounded answers to highly ambiguous questions.
Software Engineering Support: Explore distributed architectures, review code, investigate concurrency issues, and plan multi-file refactoring with human verification.
Nuanced Technical & Long-Form Writing: Produce impeccably structured whitepapers, detailed technical monographs, and executive memos that maintain precise tone, cadence, and conceptual depth.
Multi-Turn Context Through Messages: Supply relevant earlier messages with each request so follow-up answers can build on the conversation.
Flexible Response Length Controls: Regulate response length through flexible output token controls to generate concise decisions or comprehensive, detailed treatises.
High-Reliability Streaming Architecture: Receive tokens incrementally via server-sent events to support responsive, real-time interactive user interfaces.

How to Use Claude Opus 5.5 Text-to-Text API for Reasoning Workflows on Flaq AI

Input: Text prompts, system guidance, user instructions, and rich conversation history.
Output: In-depth, well-structured text responses suited for technical reviews, software implementations, editorial revisions, and strategic decisions.
Output Length: Optionally set a maximum output token count for focused feedback or longer responses. No default maximum is specified for this parameter; model output limits still apply.
Streaming: Receive the generated text progressively via server-sent events for immediate rendering and user engagement.
Capabilities: Frontier language reasoning, end-to-end software engineering assistance, structured writing synthesis, and conversational problem solving via Flaq AI API integration.

Best Use Cases for Claude Opus 5.5 Text-to-Text API Integration

Complex System Architecture & Code Refactoring: Guide engineering leads in designing resilient software frameworks, evaluating technology stacks, and executing delicate code refactoring.
Enterprise Research & Strategic Synthesis: Digest disparate domain data into coherent strategic roadmaps, market theses, and executive recommendations.
Long-Form Publishing & Technical Authoring: Draft detailed technical books, comprehensive API documentation, and thought-leadership publications requiring sustained thematic unity.
Multi-Turn Autonomous Reasoning Agents: Drive sophisticated agentic systems that require deep reflection, self-correction, and planning across multi-step objectives.
Legal and Regulatory Drafting Support: Draft summaries and working documents from supplied materials for qualified reviewers to verify.

Note Please ensure your prompts and application workflows comply with Anthropic's safety guidelines and Flaq AI terms. Review model outputs for factual accuracy and safety before deploying them in critical production workflows.

Claude Opus 5.5 Text-to-Text vs Competitors: Comparative Analysis

Claude Opus 5.5 vs. Claude Opus 5
Claude Opus 5 is the earlier model in this series. Claude Opus 5.5 is positioned for long-running agentic coding and knowledge work, with updated model behavior and lower published token pricing than Opus 5.
Claude Opus 5.5 vs. GPT 6 Sol
GPT 6 Sol serves coding and agentic workflows in the OpenAI ecosystem. Claude Opus 5.5 offers an Anthropic-based option for long-running coding and knowledge work on Flaq AI.
Claude Opus 5.5 vs. Claude Sonnet 5
Claude Sonnet 5 offers a faster option in Anthropic's current lineup. Claude Opus 5.5 is positioned for longer coding and knowledge-work tasks where teams can allow more time for analysis.
Claude Opus 5.5 vs. Gemini 3.5 Pro
Gemini 3.5 Pro fits workflows built around Google's model ecosystem. Claude Opus 5.5 offers an Anthropic-based option for text reasoning, coding, and long-form writing through Flaq AI.