How to combine GPT-5, Claude, and Gemini in one pipeline

Building a system that uses multiple frontier models is not about wiring up three separate SDKs. To combine GPT-5 Claude Gemini pipeline stages into one coherent workflow, you need a single inference layer that speaks one protocol and lets you swap models per step. This article walks through a concrete Python implementation using an OpenAI-compatible gateway and explicit stage boundaries.

Step 1: Configure a single client

Each model vendor ships its own SDK, auth scheme, and response shape. That fragmentation breaks pipelines the moment you add a third model. Point one OpenAI-compatible client at a unified endpoint and address every model by string ID.

n4n.ai exposes one OpenAI-compatible endpoint that addresses 240+ models, so you configure a single base_url and reuse the official openai package.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.n4n.ai/v1",
    api_key=os.environ["N4N_API_KEY"],
)

Store the key in environment variables. Never hardcode credentials in pipeline code—you will rotate them.

Step 2: Define stage-to-model mapping

A multi-model workflow fails when models overlap responsibilities. Assign each stage a strength: GPT-5 for task decomposition, Claude for code synthesis, Gemini for cross-checking output against multimodal or long-context ground truth.

PIPELINE = {
    "plan":   {"model": "gpt-5", "temperature": 0.2},
    "code":   {"model": "claude-3-5-sonnet", "temperature": 0.3},
    "verify": {"model": "gemini-1.5-pro", "temperature": 0.1},
}

Keep this config declarative. You will swap model IDs without touching call logic. When you combine GPT-5 Claude Gemini pipeline roles this way, each step stays independently testable.

Step 3: Implement a stage caller with fallback

Network errors and rate limits are normal at production scale. Wrap the completion call so a single stage can fail over to a secondary model instead of killing the run.

from openai import RateLimitError

def call_stage(stage, messages, fallback_model=None):
    cfg = PIPELINE[stage]
    try:
        return client.chat.completions.create(
            model=cfg["model"],
            messages=messages,
            temperature=cfg["temperature"],
            response_format={"type": "json_object"} if stage == "plan" else None,
        )
    except RateLimitError:
        if fallback_model:
            return client.chat.completions.create(
                model=fallback_model,
                messages=messages,
                temperature=cfg["temperature"],
            )
        raise

If you need provider pinning, pass extra headers; n4n.ai honors client routing directives and forwards provider cache-control hints, so you can force a specific backend or enable prompt caching on supported models. That matters when Gemini’s cached context saves cost on repeated verification passes.

Step 4: Chain stages with structured handoffs

The plan stage must emit parseable JSON. Claude consumes that JSON and returns code. Gemini reviews the code. Keep the handoff strict: validate the plan before sending it onward.

import json

def run_pipeline(user_req: str):
    plan_resp = call_stage(
        "plan",
        [{"role": "user", "content": f"Decompose task: {user_req}. Return JSON with 'steps'."}],
    )
    plan = json.loads(plan_resp.choices[0].message.content)

    code_msgs = [
        {"role": "system", "content": "Implement the given plan as Python."},
        {"role": "user", "content": json.dumps(plan)},
    ]
    code_resp = call_stage("code", code_msgs, fallback_model="gpt-5")
    code = code_resp.choices[0].message.content

    verify_msgs = [
        {"role": "user", "content": f"Review this code for bugs:\n{code}"},
    ]
    verify_resp = call_stage("verify", verify_msgs)
    review = verify_resp.choices[0].message.content

    return {"plan": plan, "code": code, "review": review}

The fallback_model="gpt-5" on the code stage means if Claude is degraded, GPT-5 covers synthesis. This combine GPT-5 claude gemini pipeline pattern tolerates partial provider outages.

Step 5: Meter usage per stage

Token cost is the only real metric for multi-model efficiency. The gateway returns standard usage objects. Log them per call to spot stage imbalance.

def log_usage(stage, resp):
    u = resp.usage
    print(f"[{stage}] prompt={u.prompt_tokens} completion={u.completion_tokens} total={u.total_tokens}")

# Wrap run_pipeline or instrument call_stage to log_usage after each return.

Per-token metering lets you see that Gemini verification may consume more tokens than expected on long code blocks, prompting a context-trimming step.

Step 6: Verify the pipeline end-to-end

Success means the pipeline returns a dict with non-empty plan, code, and review, and logs usage without raising. Validate with a smoke test:

def test_pipeline_smoke():
    out = run_pipeline("Parse a CSV and sum column B")
    assert isinstance(out["plan"], dict)
    assert "def " in out["code"]
    assert len(out["review"]) > 20

Run it against the live gateway with a tiny task first. If you cannot hit the network in CI, mock call_stage with unittest.mock and assert the chaining logic. Either way, the contract is: structured plan in, reviewed code out.

Pitfalls to avoid

• JSON drift: GPT-5 may add keys. Validate with pydantic instead of blind json.loads in production.
• Temperature leakage: Verification should be near-deterministic. Keep Gemini at 0.1.
• Hidden latency: Sequential calls add up. Run plan and verify concurrently only if verify can consume a stubbed code draft—usually not worth it.

When you combine GPT-5 Claude Gemini pipeline stages behind one client, you cut integration debt and gain per-stage fallback. The workflow above is runnable today with any OpenAI-compatible router that supports model string routing.