claude-opus-4-7

Quick example

curl https://api.reapi.ai/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-7",
    "messages": [
      { "role": "user", "content": "Hello" }
    ],
    "stream": true,
    "max_tokens": 4096,
    "temperature": 0.7
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.reapi.ai/v1",
)

stream = client.chat.completions.create(
    model="claude-opus-4-7",
    messages=[{"role": "user", "content": "Hello"}],
    stream=True,
    max_tokens=4096,
    temperature=0.7,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content or ""
    print(delta, end="", flush=True)

from anthropic import Anthropic

client = Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://api.reapi.ai",
)

with client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=4096,
    messages=[{"role": "user", "content": "Hello"}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.reapi.ai/v1",
});

const stream = await client.chat.completions.create({
  model: "claude-opus-4-7",
  messages: [{ role: "user", content: "Hello" }],
  stream: true,
  max_tokens: 4096,
  temperature: 0.7,
  // `group` is an api.reapi.ai-specific extension; pass via extra body.
  // @ts-expect-error — not part of the OpenAI types
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "io"
    "net/http"
)

func main() {
    body, _ := json.Marshal(map[string]any{
        "model": "claude-opus-4-7",
        "messages": []map[string]string{
            {"role": "user", "content": "Hello"},
        },
        "stream":      true,
        "max_tokens":  4096,
        "temperature": 0.7,
    })
    req, _ := http.NewRequest("POST",
        "https://api.reapi.ai/v1/chat/completions", bytes.NewReader(body))
    req.Header.Set("Authorization", "Bearer YOUR_API_KEY")
    req.Header.Set("Content-Type", "application/json")

    resp, _ := http.DefaultClient.Do(req)
    defer resp.Body.Close()
    out, _ := io.ReadAll(resp.Body)
    fmt.Println(string(out))
}

Authentication

Every request needs a Bearer token. The Claude Opus 4.7 chat workspace lives on the api.reapi.ai platform — sign in there to create a key and top up tokens.

1. Open api.reapi.ai and sign in (or create an account).
2. Generate an API key under API Keys.
3. Top up tokens under Top Up (pay-as-you-go, billed in USD per 1M tokens — see api.reapi.ai/pricing).

Authorization: Bearer YOUR_API_KEY

Endpoints

POST https://api.reapi.ai/v1/chat/completions   # OpenAI-compatible
POST https://api.reapi.ai/v1/messages           # Anthropic-native

Both surfaces accept claude-opus-4-7. Pick whichever matches your SDK of record:

• /v1/chat/completions — drop-in for the OpenAI SDKs. Same request shape, same SSE wire format. Set base_url to https://api.reapi.ai/v1.
• /v1/messages — native Anthropic Messages format. Set base_url to https://api.reapi.ai for the Anthropic Python / TypeScript SDKs. Required for callers that need Anthropic-specific features (cache_control blocks for prompt caching, native multi-block content, the full tool-use spec).

Request body — /v1/chat/completions

model — string, required

Must be "claude-opus-4-7". The value is echoed back in the response envelope.

messages — array, required

Conversation history as an array of message objects. Same shape as the OpenAI Chat Completions spec, plus content-parts for vision:

{
  "role": "system" | "user" | "assistant" | "tool",
  "content": "string OR content-parts array (text + image_url parts)"
}

Multi-turn history is sent in chronological order — the last message is the one Claude responds to.

max_tokens — integer, default 4096

Upper bound on output tokens. Anthropic's API requires max_tokens on every call, including streamed ones — even though the OpenAI SDKs treat it as optional. Set it generously (128000 is the hard cap) for long-form outputs; the model still stops at the natural end of its response.

stream — boolean, default false

When true, the response is streamed as server-sent events (SSE) with Content-Type: text/event-stream. Each event is a JSON delta in the OpenAI format, terminated by a data: [DONE] line.

temperature — number, default 1

Range 0.0 – 1.0. Sampling temperature. Anthropic recommends either temperature or top_p, not both. Lower values produce more deterministic output.

top_p — number, default 1

Range 0.0 – 1.0. Nucleus sampling cutoff.

tools / tool_choice — optional

Standard OpenAI tool-calling parameters. Claude Opus 4.7 supports the full OpenAI tool-use spec via this surface; for Anthropic's native tool-use schema (with cache_control, tool_choice_type, etc.) call /v1/messages directly.

Routing

Routing follows the API key configured in the legacy workspace. No group request parameter is required.

Vision input (multimodal)

Send images alongside text via OpenAI content-parts:

{
  "model": "claude-opus-4-7",
  "max_tokens": 4096,
  "messages": [
    {
      "role": "user",
      "content": [
        { "type": "text", "text": "What does this chart show?" },
        {
          "type": "image_url",
          "image_url": { "url": "https://example.com/chart.png" }
        }
      ]
    }
  ]
}

Supported image formats: PNG, JPEG, GIF, WebP. Base64 URLs work too — prefix data:image/png;base64,.... Each image counts toward the input token budget based on its resolution.

Prompt caching

Anthropic's prompt caching pays off on stable system prompts, recurring RAG context, and long multi-turn agent histories. The first call pays the cache-write rate on the cacheable region; subsequent calls within the cache window pay only the (much lower) cache-read rate on those tokens.

To enable caching, call /v1/messages natively and add a cache_control block. Example:

{
  "model": "claude-opus-4-7",
  "max_tokens": 4096,
  "system": [
    {
      "type": "text",
      "text": "<your long stable system prompt>",
      "cache_control": { "type": "ephemeral" }
    }
  ],
  "messages": [
    { "role": "user", "content": "Question for the assistant" }
  ]
}

The cache key is the hash of the cacheable content; the cache TTL is 5 minutes (default ephemeral) or 1 hour (extended ephemeral) depending on how the cache_control block is configured. See api.reapi.ai/pricing for cache-read and cache-write rates.

Response shape — /v1/chat/completions

Non-streaming (stream: false)

{
  "id": "chatcmpl-018f5a3a1b6e7d9f8c2b4d6e8f0a2c4e",
  "object": "chat.completion",
  "created": 1735000000,
  "model": "claude-opus-4-7",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 9,
    "total_tokens": 21,
    "prompt_tokens_details": {
      "cached_tokens": 0
    }
  }
}

usage.prompt_tokens_details.cached_tokens reports how many input tokens were served from cache — the part billed at the cache-read rate rather than the standard input rate.

Streaming (stream: true)

Content-Type: text/event-stream. Each data: line is a JSON delta in the OpenAI chunk format; the final event before [DONE] carries the finish_reason (stop / length / tool_calls / content_filter).

Pricing

Claude Opus 4.7 is billed pay-as-you-go in USD against your api.reapi.ai token balance. Current rates (input, output, cache-read, cache-write) live on api.reapi.ai/pricing and in the pricing card at the top of the model page.

Per-call bill:

billable_input  = (prompt_tokens - cached_tokens) × input_rate  / 1,000,000
cache_read_bill = cached_tokens                   × cache_read_rate / 1,000,000
output_bill     = completion_tokens               × output_rate / 1,000,000

Cache-write rate applies on the first call that writes a cache block; subsequent hits within the cache TTL pay only the cache-read rate. Failed requests are not charged.

Limits

Streams that hit the output cap finish with finish_reason: "length"; call again with a continuation message if you need more text.

Errors

The error envelope follows the OpenAI shape — HTTP status, plus a JSON body:

{
  "error": {
    "message": "...",
    "type": "invalid_request_error",
    "code": "..."
  }
}

Common cases:

Recipes

Minimum request

{
  "model": "claude-opus-4-7",
  "max_tokens": 4096,
  "messages": [
    { "role": "user", "content": "Summarise this in three sentences." }
  ]
}

Tool use (function calling, OpenAI surface)

{
  "model": "claude-opus-4-7",
  "max_tokens": 4096,
  "messages": [
    { "role": "user", "content": "What's the weather in Tokyo today?" }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Look up the current weather for a city.",
        "parameters": {
          "type": "object",
          "properties": { "city": { "type": "string" } },
          "required": ["city"]
        }
      }
    }
  ],
  "tool_choice": "auto"
}

Vision

{
  "model": "claude-opus-4-7",
  "max_tokens": 4096,
  "messages": [
    {
      "role": "user",
      "content": [
        { "type": "text", "text": "Read the error in this screenshot and suggest a fix." },
        {
          "type": "image_url",
          "image_url": { "url": "https://your-cdn.com/screenshot.png" }
        }
      ]
    }
  ]
}

Long context with prompt caching (native Anthropic surface)

{
  "model": "claude-opus-4-7",
  "max_tokens": 4096,
  "system": [
    {
      "type": "text",
      "text": "<800K-token reference document>",
      "cache_control": { "type": "ephemeral" }
    }
  ],
  "messages": [
    { "role": "user", "content": "Find every mention of the constraint X and list them with line numbers." }
  ]
}

When to pick Claude Opus 4.7

Pick Claude Opus 4.7 when output quality dominates the decision:

• High-stakes coding and refactors — architecture, multi-file refactor, migration planning, long-form engineering deliverables.
• Agent workflows that must hold state — multi-step planning, reliable tool use, long agent runs where context drift kills the workflow.
• Large-context analysis — full codebases, long research packs, multi-document review, audit work.

Route lighter traffic (classification, short replies, tight loops) to cheaper Claude or GPT models on the same key.

Tips

• Set max_tokens generously. Anthropic enforces it strictly — the model still stops at the natural end of its response, but a low cap will truncate before the real ending.
• Stream by default for chat UX. Streaming cuts perceived latency dramatically.
• Cache the stable parts of long prompts. A 500K-token RAG context on top of a 1KB user question can pay the cache-read rate on every subsequent call instead of the standard input rate. Big savings on multi-turn agents.
• Tune temperature or top_p, not both. Mixing them produces results that are hard to reason about.
• Use the native /v1/messages surface for Anthropic-only features. cache_control, native multi-block content, full tool-use spec — all of those work through /v1/messages without needing translation.

Related

• Authentication
• Quickstart
• Errors catalog