One API key for GPT-5 and Claude instead of two

Managing a separate OpenAI key for GPT-5 and an Anthropic key for Claude doubles your secret sprawl, retry logic, and billing reconciliation. A one API key GPT-5 Claude setup collapses both integrations into a single OpenAI-compatible client, which is easier to operate once you understand the tradeoffs. This guide gives an ordered path from dual-provider plumbing to a unified calling pattern, with code and the places where it bites.

The direct two-key baseline

Typically you install two SDKs: openai and anthropic. You load OPENAI_API_KEY and ANTHROPIC_API_KEY, instantiate clients, and write near-identical wrappers for chat completions.

import os
from openai import OpenAI
from anthropic import Anthropic

oai = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
anth = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

gpt_resp = oai.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "Summarize this"}]
)
claude_resp = anth.messages.create(
    model="claude-3-5-sonnet",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Summarize this"}]
)

That works, but you now maintain two auth flows, two rate-limit backoffs, and two usage exports. For a single-model prototype it’s fine; for a system that routes between models it’s tax.

Why collapse to one API key GPT-5 Claude

A gateway that speaks the OpenAI protocol in front of multiple providers removes the second SDK and the second secret. You get one HTTP client, one retry policy, and one token ledger. Gateways such as n4n.ai expose one OpenAI-compatible endpoint fronting 240+ models, including GPT-5 and Claude, with automatic fallback when a provider is rate-limited or degraded. The upside is operational simplicity; the cost is a thin abstraction over provider-native features.

Step 1: Provision a gateway credential

Sign up with a gateway that supports both model families. Create a project key, store it as GATEWAY_KEY in your secret manager. Do not commit it. Rotate it like any other production secret.

export GATEWAY_KEY=sk-gw-xxxxxxxx

Step 2: Point the OpenAI client at the gateway

The OpenAI Python library accepts a base_url. Set it to the gateway’s /v1 endpoint. Now every call goes through the gateway, authenticated with the one API key GPT-5 Claude credential.

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.n4n.ai/v1",
    api_key=os.environ["GATEWAY_KEY"],
)

If you prefer raw HTTP, the equivalent curl works:

curl https://api.n4n.ai/v1/chat/completions \
  -H "Authorization: Bearer $GATEWAY_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-5","messages":[{"role":"user","content":"Hi"}]}'

Step 3: Route by model name

The gateway maps provider model IDs to a unified namespace. You pass model="gpt-5" or model="claude-3-5-sonnet" in the same method. No branch on provider.

def complete(prompt: str, model: str):
    return client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )

complete("Translate to French", "gpt-5")
complete("Translate to French", "claude-3-5-sonnet")

Keep a constant map of logical names to gateway model strings so marketing renames don’t scatter through your code.

Step 4: Handle provider-specific parameters

Unified does not mean identical. Claude expects a system role message; OpenAI-compatible gateways translate that fine, but prompt caching needs provider headers. The gateway forwards cache-control hints if you pass them through extra_headers.

client.chat.completions.create(
    model="claude-3-5-sonnet",
    messages=[
        {"role": "system", "content": "You are terse."},
        {"role": "user", "content": "Long doc..."}
    ],
    extra_headers={"anthropic-cache-control": "ephemeral"}
)

GPT-5 structured outputs may require response_format with json schema; the gateway passes that to OpenAI unchanged. Test each model’s edge parameters explicitly—don’t assume parity.

Step 5: Implement fallback and degradation logic

Even with gateway auto-fallback, you should own a policy. If a completion fails with 429 or 503, retry on a secondary model. Because you already use one API key GPT-5 Claude, switching models is a string change.

from openai import APIError

def robust_complete(prompt, primary="gpt-5", fallback="claude-3-5-sonnet"):
    try:
        return complete(prompt, primary)
    except APIError as e:
        if e.status_code in (429, 503):
            return complete(prompt, fallback)
        raise

Honor client routing directives if your gateway supports them; some let you pin a provider region via header.

Common pitfalls and tradeoffs

Latency. A gateway adds a hop. Measure p99 before committing. If you are already on the provider’s edge network, direct may be 10–20 ms faster.

Feature lag. New provider capabilities (e.g., GPT-5 real-time audio) surface on the native API first. The gateway normalizes; you may wait for support.

Error normalization. Provider-specific error bodies get mapped to OpenAI-style errors. You lose some debug detail unless the gateway echoes original metadata. Log the x-gateway-upstream header if present.

Cost transparency. Per-token metering should match provider prices, but confirm the gateway doesn’t add hidden margin. Pull usage from responses and reconcile weekly.

Rate limits aggregate. One key means one rate pool. A Claude spike can starve your GPT-5 quota if the gateway enforces shared limits.

When to stay direct

If you depend on Anthropic’s latest beta, or need OpenAI’s enterprise private networking, skip the gateway. Same if your compliance team forbids third-party proxying of prompts. Direct SDKs also give you native SDK types, which some codegen tools prefer.

Migration checklist

1. Inventory every OpenAI and Anthropic call in your codebase.
2. Stand up a gateway project and issue a GATEWAY_KEY.
3. Replace OpenAI() and Anthropic() constructors with a single OpenAI(base_url=...).
4. Swap model strings to gateway names; centralize them in a config.
5. Move provider-specific headers to extra_headers.
6. Add a fallback wrapper around completions.
7. Wire gateway usage into your billing dashboard.
8. Load-test both models through the gateway before flip.

A one API key GPT-5 Claude architecture is not free, but for most multi-model products it cuts integration toil sharply. Do the migration behind a flag, keep direct calls as escape hatch, and you keep both simplicity and control.