2026-06-03 · flo2 blog

Unified LLM API: One Interface for Every Model

A unified LLM API is a single interface that sits in front of many model providers — OpenAI, Anthropic, Gemini, Groq, Mistral, and the rest — and exposes them all through one consistent request and response shape. Instead of wiring up a separate SDK, auth scheme, and payload format for each vendor, you call one endpoint and select the model with a string. It's the difference between "integrate seven providers" and "change model and ship." If you've ever wanted one API for all LLMs, this is the pattern that delivers it.

This post explains the problem a unified API solves, what the abstraction actually standardizes, the concrete benefits for a production codebase, how a gateway delivers it under the hood, and a small before/after example so you can see the code shrink.

The problem: every provider is its own integration

Each LLM vendor ships its own SDK, and underneath each SDK is a different contract. Adopting a second or third model means re-learning four things that have nothing to do with your actual product:

Multiply that by every model you want to evaluate and the cost is real. Worse, it's a quiet form of lock-in: once your application is written against one vendor's exact payload and SDK, "let's try a cheaper model" turns into a refactor instead of a config change. That friction is what stops teams from shopping around — and the model landscape moves fast enough that being unable to switch is a genuine liability.

The solution: one interface, change the model string

A unified LLM API removes the per-provider integration by standardizing the contract. In practice the de facto standard is the OpenAI Chat Completions shape — messages in, choices[].message.content out, bearer-token auth, SSE streaming — because the entire tooling ecosystem (the official SDKs, LangChain, LlamaIndex, the Vercel AI SDK, instructor) already targets it. A single API many models can ride on top of that one schema.

With that abstraction in place, the only thing that varies between models is the model field:

This is exactly the principle behind an OpenAI-compatible API: agree on the wire format once, and every provider that speaks it becomes interchangeable. A multi-model API is just that idea applied across many vendors at the same time.

Why it's worth it: four concrete wins

Standardizing on one interface isn't only about typing less. It changes what your team can do operationally:

How it's delivered: a gateway normalizes the formats

You can hand-roll a thin adapter layer yourself, but the production-grade version of a unified API is a gateway (or proxy) that sits between your app and the providers and does the normalization for you. It accepts a request in a standard shape, translates it to whatever the target provider expects, calls that provider with the right key, and translates the response back. For the full mental model of that layer — routing, fallback, caching, observability — see what is an LLM gateway.

The detail that matters for adoption is which front doors the gateway speaks. flo2 exposes both the OpenAI Chat Completions API and the Anthropic Messages API. So an OpenAI-SDK client and an Anthropic-SDK client can each keep their native format and still route to the same set of models — no rewriting an Anthropic app into Chat Completions shape just to reach a Llama model, and vice versa. The gateway handles the translation on both sides.

Before and after

Here's the "before": two providers, two SDKs, two payload shapes, two response shapes, two key variables. The logic to support both is mostly format-shuffling.

# BEFORE — provider-specific code paths
from openai import OpenAI
from anthropic import Anthropic

if provider == "openai":
    client = OpenAI(api_key=OPENAI_KEY)
    r = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
    )
    text = r.choices[0].message.content
elif provider == "anthropic":
    client = Anthropic(api_key=ANTHROPIC_KEY)
    r = client.messages.create(
        model="claude-3-5-sonnet-latest",
        max_tokens=1024,
        messages=[{"role": "user", "content": prompt}],
    )
    text = r.content[0].text   # different shape, different parsing

And the "after": one client, one base URL, one key. To target a different model — OpenAI, Anthropic, Gemini, Groq — you change the model string and nothing else. The response shape stays constant.

# AFTER — one unified interface, route by model name
from openai import OpenAI

client = OpenAI(
    base_url="https://flo2.com/v1",
    api_key="your_flo2_key",
)

r = client.chat.completions.create(
    model="auto",  # or "gpt-4o-mini" / "claude-3-5-sonnet" / "llama-3.3-70b"
    messages=[{"role": "user", "content": prompt}],
)
text = r.choices[0].message.content

The "after" works with your existing OpenAI SDK and any framework built on it. Pass a concrete model name to pin a provider, or pass auto and let flo2 pick a model that fits the task. Because you bring your own provider keys and pay each vendor directly, flo2 adds zero token markup — the unified interface is a routing convenience, not a reseller margin.

What a good unified API adds beyond the interface

The single endpoint is the foundation; the value compounds with what the layer does on top of it. With flo2 specifically, one OpenAI- and Anthropic-compatible key gives you smart routing to the cheapest or fastest model that fits, fallback across providers when one fails, request racing to cut tail latency, A/B comparisons with a judge to measure model–task fit, response caching, and true per-call cost accounting across every provider in one view. None of that is reachable when each model is a separate hard-coded integration — it only becomes possible once everything sits behind one interface.

Takeaways

If you're tired of maintaining one code path per provider, route every model through a single endpoint with flo2 — it's free during Beta and adds no markup on top of provider pricing. New to the underlying concepts? Start with an OpenAI-compatible API and what is an LLM gateway.

One key, every model — zero markup.
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