Claude Opus 4.8 API Key Setup: Endpoint, Model ID, Pricing, and Python Example

A Claude Opus 4.8 API key is useful only when the key, endpoint, model ID, request body, and response parser all match the route you are calling. A valid key can still fail if the application sends it to /chat/completions, uses a catalog path instead of the API model ID, or parses the response as an OpenAI choices object.

This guide focuses on the OurToken route for Claude Opus 4.8. It shows how to get an API key, copy the exact base URL and full endpoint, send a cURL request, call the same endpoint from Python, calculate token cost, and troubleshoot common 401, 404, model_not_found, and Messages API parsing errors. It is intentionally not a generic Claude overview; the goal is to get one real request working through the current Claude Opus 4.8 API page.

The current OurToken model page lists Claude Opus 4.8 as a Messages API route with a 200K context window, up to 32K output tokens, vision support, function calling, extended thinking support, and this configuration:

Base URL:     https://api.ourtoken.ai/v1
Full URL:     https://api.ourtoken.ai/v1/messages
Model ID:     claude-opus-4-8
Auth header:  Authorization: Bearer YOUR_API_KEY

The request and response shape follows the Anthropic Messages API reference, while authentication follows the OurToken model page: Authorization: Bearer YOUR_API_KEY. Do not assume Anthropic-native x-api-key headers are accepted unless you verify that route with a live OurToken key.

Verification status: This article was checked against the public OurToken Claude Opus 4.8 model page and Anthropic Messages API reference on 2026-08-11. The examples are documentation-verified and require your own OurToken API key for live execution.

Claude Opus 4.8 API Key Setup

The setup has three separate values. Keep them separate in your configuration so a future model change does not require editing every request call.

Exact values to copy

Create the credential from the OurToken API Keys page, then store it in a server-side environment variable. The API Keys page may redirect to login before you can manage credentials.

On macOS or Linux:

export OURTOKEN_API_KEY="paste-your-key-locally"

On Windows PowerShell:

$env:OURTOKEN_API_KEY = "paste-your-key-locally"

Do not paste the key into frontend JavaScript, a public repository, a shared notebook, a screenshot, or a bug report. Treat the key like a production password.

Do not use these model names

The exact model ID is claude-opus-4-8. If you see model_not_found, check this value first before changing code.

Endpoint, Model ID, and First Request

Claude Opus 4.8 on OurToken uses the Messages API route. That is different from the OpenAI-compatible /v1/chat/completions examples used by many non-Claude routes.

Application server
  -> Authorization: Bearer OURTOKEN_API_KEY
  -> POST https://api.ourtoken.ai/v1/messages
  -> model = claude-opus-4-8
  -> Messages API response blocks
  -> usage + cost logging

The route uses a top-level system field when you need system instructions. Do not send a system prompt as a message with role: "system". The Messages API requires max_tokens, so include it even in a smoke test.

cURL smoke test

Start with a tiny request. It proves the key, endpoint, model ID, and body shape before you test a real coding or research prompt.

curl -sS https://api.ourtoken.ai/v1/messages \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${OURTOKEN_API_KEY}" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 256,
    "messages": [
      {
        "role": "user",
        "content": "Give me a short checklist for reviewing a pull request."
      }
    ]
  }'

A successful response should contain a top-level type of message, a role of assistant, a content array, the model ID, a stop reason, and a usage object. The exact generated words will vary.

{
  "id": "msg_redacted",
  "type": "message",
  "role": "assistant",
  "model": "claude-opus-4-8",
  "content": [
    {
      "type": "text",
      "text": "1. Check the intent.\n2. Review risky code paths.\n3. Confirm tests and rollback."
    }
  ],
  "stop_reason": "end_turn",
  "usage": {
    "input_tokens": 24,
    "output_tokens": 28
  }
}

This is an illustrative response shape, not a captured private account response. The important point is the parser: read assistant text from content[0].text, not from choices[0].message.content.

PowerShell smoke test

$payload = @{
  model = "claude-opus-4-8"
  max_tokens = 256
  messages = @(
    @{
      role = "user"
      content = "Give me a short checklist for reviewing a pull request."
    }
  )
} | ConvertTo-Json -Depth 5

curl.exe "https://api.ourtoken.ai/v1/messages" `
  -H "Content-Type: application/json" `
  -H "Authorization: Bearer $env:OURTOKEN_API_KEY" `
  --data-raw $payload

If cURL fails, fix the raw request first. If raw HTTP works but an SDK or app integration fails, the bug is likely in SDK headers, request path construction, response parsing, or model configuration.

Claude Opus 4.8 Python API Example

Use a Python HTTP client when you need exact control over the Authorization: Bearer header. The official Anthropic SDK is designed for Anthropic-native headers, while the current OurToken page documents Bearer authentication. Unless you have verified the SDK with a real OurToken key, httpx is the safer first example.

Install the dependency:

python -m pip install --upgrade httpx

Python request with Bearer auth

import os

import httpx

BASE_URL = "https://api.ourtoken.ai/v1"
API_KEY = os.environ["OURTOKEN_API_KEY"]

payload = {
    "model": "claude-opus-4-8",
    "max_tokens": 512,
    "system": "You are a concise senior software engineering assistant.",
    "messages": [
        {
            "role": "user",
            "content": "Explain how to review a database migration safely.",
        }
    ],
}

response = httpx.post(
    f"{BASE_URL}/messages",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}",
    },
    json=payload,
    timeout=60.0,
)
response.raise_for_status()
data = response.json()

text_blocks = [
    block["text"]
    for block in data.get("content", [])
    if block.get("type") == "text" and "text" in block
]
print("\n".join(text_blocks))

usage = data.get("usage", {})
print(
    {
        "model": data.get("model"),
        "stop_reason": data.get("stop_reason"),
        "input_tokens": usage.get("input_tokens", 0),
        "output_tokens": usage.get("output_tokens", 0),
        "cache_creation_input_tokens": usage.get("cache_creation_input_tokens", 0),
        "cache_read_input_tokens": usage.get("cache_read_input_tokens", 0),
    }
)

The parser intentionally loops through text blocks. Claude responses can include different typed blocks when tools or extended features are enabled. A production parser should not assume the first block is always text after you add tools.

Basic response assertions

assert data["type"] == "message"
assert data["role"] == "assistant"
assert data["model"] == "claude-opus-4-8"
assert isinstance(data.get("content"), list)
assert data.get("usage", {}).get("input_tokens") is not None

Use assertions in a smoke test, not as your production error strategy. In production, convert these into structured validation errors, log the route, and return a safe application-level message.

Multi-turn request

The Messages API is stateless. Send the conversation history again for a follow-up. Preserve the assistant content as blocks rather than flattening it into plain text, especially if you later enable tools.

messages = [
    {
        "role": "user",
        "content": "List three risks in a database migration.",
    }
]

first = httpx.post(
    f"{BASE_URL}/messages",
    headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
    json={"model": "claude-opus-4-8", "max_tokens": 256, "messages": messages},
    timeout=60.0,
)
first.raise_for_status()
first_data = first.json()

messages.append({"role": "assistant", "content": first_data["content"]})
messages.append({"role": "user", "content": "Turn that into a release checklist."})

second = httpx.post(
    f"{BASE_URL}/messages",
    headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
    json={"model": "claude-opus-4-8", "max_tokens": 512, "messages": messages},
    timeout=60.0,
)
second.raise_for_status()
print(second.json()["content"][0]["text"])

Do not store a full production transcript indefinitely unless your privacy policy, customer agreement, and data-retention plan allow it. For debugging, usually log request IDs, model IDs, stop reasons, token counts, and redacted prompt versions rather than raw customer content.

Pricing, Cache Tokens, and Production Controls

The current Claude Opus 4.8 model page lists the route at 40% of the official comparison price. These prices were checked on 2026-08-11 and should be confirmed on the live model page before budgeting.

A request with 100,000 input tokens and 8,000 output tokens costs:

Input:   100,000 / 1,000,000 * $2.00  = $0.200
Output:    8,000 / 1,000,000 * $10.00 = $0.080
Total:                                      $0.280

If cache fields are present in the usage object, calculate each category once:

cost =
  input_tokens / 1,000,000 * input_rate
  + cache_read_input_tokens / 1,000,000 * cached_input_rate
  + cache_creation_input_tokens / 1,000,000 * cache_write_rate
  + output_tokens / 1,000,000 * output_rate

For the Messages API fields shown on the OurToken page, cache_read_input_tokens and cache_creation_input_tokens are separate usage fields. Do not charge the same cached tokens twice. This is different from some OpenAI-compatible prompt_tokens objects where cached tokens are nested inside a total input value. The OpenAI-compatible prompt caching guide explains the broader caching pattern, but the field names and billing math should follow the current Claude route.

Python cost helper

RATES = {
    "input": 2.00,
    "output": 10.00,
    "cached_input": 0.20,
    "cache_write": 2.50,
}


def estimate_cost_usd(usage: dict) -> float:
    return round(
        usage.get("input_tokens", 0) / 1_000_000 * RATES["input"]
        + usage.get("cache_read_input_tokens", 0) / 1_000_000 * RATES["cached_input"]
        + usage.get("cache_creation_input_tokens", 0) / 1_000_000 * RATES["cache_write"]
        + usage.get("output_tokens", 0) / 1_000_000 * RATES["output"],
        8,
    )

Use dated rates in code only for reporting snapshots or local experiments. A billing dashboard should pull current prices from your own accounting source or be updated whenever the live model page changes.

Troubleshooting 401, model_not_found, and API errors

Do not retry malformed requests unchanged. Retry helps with transient failures such as 429, 529, some 5xx errors, and network timeouts. It does not repair a bad endpoint, bad model ID, or missing max_tokens field.

Production checklist

[ ] API key is stored server-side only
[ ] Raw cURL request works before SDK integration
[ ] Base URL is https://api.ourtoken.ai/v1
[ ] Full endpoint is /v1/messages
[ ] Model ID is claude-opus-4-8
[ ] max_tokens is set on every request
[ ] Parser reads content blocks, not choices
[ ] Usage and cost are logged per feature
[ ] 400/401/404 are not retried blindly
[ ] 429/529/5xx retries are bounded with jitter
[ ] Prompts and Authorization headers are redacted in logs

A strong production rollout starts with one small use case. For example, a code-review assistant can send a diff, test output, and a concise review policy, then ask Claude Opus 4.8 for risks and suggested checks. With a 200K context window, the route can handle larger code or document context, but that also makes token logging mandatory. Large prompts can be useful; invisible large prompts are just quiet budget leaks.

For teams using multiple routes, keep model selection on the server. A client should not be allowed to send arbitrary model IDs. Start with one Claude Opus 4.8 route, measure cost per successful task, then decide whether simpler work should move to a lower-cost model from the OurToken model directory.

Conclusion and FAQ

A working Claude Opus 4.8 setup on OurToken is compact: create an API key, call https://api.ourtoken.ai/v1/messages, send Authorization: Bearer YOUR_API_KEY, set model to claude-opus-4-8, include max_tokens, and parse the Messages API content array. Start with cURL, repeat the same request in Python, then add streaming, tools, or long-context prompts after the basic request succeeds.

When you are ready to test live traffic, create or manage a credential through OurToken API Keys, confirm the current endpoint and pricing on the Claude Opus 4.8 API page, and use OurToken Docs for broader integration guides.

FAQ

How do I get a Claude Opus 4.8 API key?

Create an OurToken account and generate a key from the OurToken API Keys page. Use that key with Authorization: Bearer when calling api.ourtoken.ai.

What is the Claude Opus 4.8 API endpoint?

The current full endpoint is https://api.ourtoken.ai/v1/messages. The base URL is https://api.ourtoken.ai/v1.

What is the Claude Opus 4.8 model ID?

Use claude-opus-4-8. Do not use the display name, a dotted version, or a catalog-style path.

Can I use a free Claude Opus 4.8 API key?

Do not assume a permanent free tier. Check the current OurToken dashboard or promotions for any account credits, then budget using the live model page.

Can I use the OpenAI Python SDK?

Not for the recommended Claude Opus 4.8 Messages API route. Use raw HTTP with httpx, or verify another SDK path against a live key before production.

Why does my request return model_not_found?

The usual cause is a model string mismatch. Confirm the request body uses "model": "claude-opus-4-8" and that the route is available to your account.