One API for GPT-5, Claude Opus 4.8, Gemini 3, and Llama 4

Wiring your app to a unified api gpt-5 claude gemini llama cuts integration overhead from weeks to an afternoon. This guide lays out the exact steps to stand up a single OpenAI-compatible endpoint, route across providers, and handle degradation without rewriting your client.

1. Standardize on one OpenAI-compatible client

Don’t write four SDK integrations. Point the OpenAI Python client at a gateway that speaks the Chat Completions shape and proxies to each backend. The unified api gpt-5 claude gemini llama pattern means your code sends the same JSON regardless of which model answers.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.n4n.ai/v1",  # one endpoint, 240+ models
    api_key="sk-your-key",
)

resp = client.chat.completions.create(
    model="claude-opus-4-8",
    messages=[{"role": "user", "content": "Summarize this PR"}],
    max_tokens=256,
)
print(resp.choices[0].message.content)

If you later switch model to gpt-5 or gemini-3, the request and response schemas stay identical. That is the entire win.

2. Map model IDs to capability tiers

Each provider exposes different context limits, tool-call syntax, and latency profiles. Encode those differences in a local registry so your routing layer stays dumb.

MODEL_REGISTRY = {
    "gpt-5":            {"tools": True,  "tier": "flagship"},
    "claude-opus-4-8":  {"tools": True,  "tier": "flagship"},
    "gemini-3":         {"tools": True, "tier": "long_ctx"},
    "llama-4-70b":      {"tools": False, "tier": "open"},
}

Treat tools: False as a hard constraint. Llama variants often need a different function-calling shim; don’t assume the gateway magically normalizes it. If you need tools on Llama, wrap with a prompt-based extractor or use a fine-tuned endpoint. Check each vendor’s docs for exact context windows before you trust a long prompt.

3. Route with explicit client directives

A good gateway lets you express preferences without changing the model string. Send routing hints in a vendor extension field or header. n4n.ai honors client routing directives and forwards provider cache-control hints, so you keep control over which backbone serves a given call.

{
  "model": "claude-opus-4-8",
  "messages": [{"role": "user", "content": "Draft a migration plan"}],
  "routing": {
    "prefer": ["claude-opus-4-8"],
    "fallback": ["gpt-5", "gemini-3"],
    "cache_control": {"ttl": 300}
  }
}

The gateway tries your preferred model first. If it hits a 429 or timeout, it shifts to the next entry. You get one error surface instead of four.

Avoid implicit routing

Never rely on the gateway’s “auto” mode in production without testing tail latency. Explicit fallback lists beat black-box selection when SLA matters.

4. Handle fallback and degradation in code

Automatic fallback at the gateway covers provider outages, but your client still needs to handle partial failures—malformed streaming chunks, content policy rejects, or empty completions.

def complete(model, msgs, retries=2):
    last_err = None
    for attempt in range(retries):
        try:
            r = client.chat.completions.create(
                model=model, messages=msgs, timeout=8
            )
            if not r.choices:
                raise ValueError("empty choices")
            return r
        except Exception as e:
            last_err = e
            # gateway already tried fallbacks; this is client-side retry
            continue
    raise last_err

Set timeout aggressively. A unified api gpt-5 claude gemini llama call should fail fast and let your orchestrator decide next steps, not hang for 60 seconds.

5. Normalize streaming and tool calls

Streaming works over SSE for all four backends, but chunk shapes differ subtly. GPT-5 and Claude emit tool_calls delta objects; Gemini streams inline function text; Llama may emit raw JSON in content. Write one normalizer.

def stream_text(model, msgs):
    stream = client.chat.completions.create(
        model=model, messages=msgs, stream=True
    )
    buf = ""
    for chunk in stream:
        delta = chunk.choices[0].delta
        if hasattr(delta, "content") and delta.content:
            buf += delta.content
        # ignore tool_call deltas for simplicity in this example
    return buf

If you use tools, test each model’s arguments parsing separately. Claude Opus 4.8 is strict about JSON; Gemini 3 may return trailing commas in some builds. Validate before you call the function.

6. Meter usage per token

Per-token metering is non-negotiable when you blend expensive flagship models with cheap open weights. The response usage object gives you what you need.

resp = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "system", "content": "Be terse."},
              {"role": "user", "content": "Hello"}],
)
print(resp.usage.prompt_tokens, resp.usage.completion_tokens)

Pipe these to your own analytics keyed by model. A unified api gpt-5 claude gemini llama bill will otherwise become a black box. Track prompt vs completion tokens separately; completion cost dominates at scale.

7. Common pitfalls and tradeoffs

System prompt drift

Claude expects system prompts in a dedicated field; Llama 4 often behaves better with the system text injected as the first user turn. Write a pre-send transform per model family.

def adapt_messages(model, msgs):
    if model.startswith("llama"):
        sys = next((m for m in msgs if m["role"]=="system"), None)
        if sys:
            msgs = [{"role":"user","content":sys["content"]}] + \
                   [m for m in msgs if m["role"]!="system"]
    return msgs

Cache-control mismatches

Provider cache hints are not portable. A cache_control hint on Claude does nothing on GPT-5. Send hints only when the gateway forwards them; otherwise you waste tokens re-sending long contexts.

Temperature scaling

A temperature of 0.7 on Gemini 3 feels hotter than on GPT-5. Calibrate per model in your registry. Don’t share a single sampling config across the unified api gpt-5 claude gemini llama set.

Latency vs cost routing

Fallback chains save uptime but can quietly route a cheap Llama call to GPT-5 when Llama is degraded, blowing your budget. Cap fallback tiers by cost class.

FALLBACK_POLICY = {
    "llama-4-70b": ["llama-4-70b", "gemini-3"],  # never jump to flagship
    "claude-opus-4-8": ["claude-opus-4-8", "gpt-5"],
}

8. Ship a thin abstraction

Wrap the client in a service that takes (prompt, tier, opts) and returns text. Keep model strings out of business logic.

class LLMGateway:
    def __init__(self, client): self.client = client
    def ask(self, tier, msgs, **kw):
        model = self.pick(tier)
        msgs = adapt_messages(model, msgs)
        return complete(model, msgs, **kw)
    def pick(self, tier):
        return {"flagship":"claude-opus-4-8","long":"gemini-3"}[tier]

That’s the whole integration. You now run a unified api gpt-5 claude gemini llama surface with explicit routing, fallback, and metering—without four vendor SDKs rotting in your dependency tree.

Final note on operations

Log the resolved model on every response. When an incident hits, you need to know whether Claude or Gemini served the user. The gateway’s response headers usually carry x-resolved-model; capture it.

Build the abstraction once. Let the gateway handle the 240+ model matrix; your team handles product.