Single API for Claude and OpenAI Models: What Works

If you're searching for a single API for Claude and OpenAI models, you're probably maintaining two codebases that do the same job: send a prompt, get a completion, handle streaming, track tokens. The two providers use different request shapes, different auth headers, different error formats, and different rate-limit behavior, and keeping both integrations in sync is tedious work that adds no value to your product.

There are two practical ways to solve this. You either write your own abstraction layer that normalizes both APIs into one internal interface, or you use a gateway service that already did that work and exposes one consistent endpoint. This article covers both approaches, when each makes sense, and what to watch for with usage tracking, tool calling, and streaming once multiple model providers are in play.

Why unification is harder than it looks

On paper, Claude and OpenAI both do the same thing: you send messages, you get text back. In practice the details diverge enough that a thin wrapper isn't enough:

• Message format: OpenAI's chat completions use a messages array with role/content strings. Claude's Messages API separates system from the message list and supports structured content blocks (text, image, tool_use, tool_result) natively.
• Streaming events: OpenAI streams delta chunks over SSE with a fairly flat shape. Claude's streaming emits typed events (message_start, content_block_delta, message_delta, etc.) that carry more structure but require different parsing logic.
• Tool calling: Both support function/tool calling, but the schema for defining tools, the shape of the tool call response, and how you send results back differ enough that your tool-handling code can't be shared as-is.
• Usage metadata: Token counts are reported differently (input/output split, cache tokens on Claude, slightly different naming on OpenAI), which matters if you're doing per-request cost tracking.
• Error codes and rate limits: Rate-limit headers, retry-after semantics, and error payload structure aren't the same, so your retry logic needs provider-aware branches.

None of this is a hard blocker, but it means a "single API" really has to normalize all five of these, not just the happy-path request/response.

Option 1: Build your own abstraction

If you only need basic chat completion and don't rely heavily on tool use or vision inputs, a small internal abstraction is reasonable:

async function complete({ provider, model, messages, system }) {
  if (provider === "claude") {
    const res = await fetch("https://api.anthropic.com/v1/messages", {
      method: "POST",
      headers: {
        "x-api-key": process.env.ANTHROPIC_API_KEY,
        "anthropic-version": "2023-06-01",
        "content-type": "application/json",
      },
      body: JSON.stringify({ model, system, messages, max_tokens: 1024 }),
    });
    const data = await res.json();
    return data.content[0].text;
  }

  if (provider === "openai") {
    const res = await fetch("https://api.openai.com/v1/chat/completions", {
      method: "POST",
      headers: {
        Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
        "content-type": "application/json",
      },
      body: JSON.stringify({
        model,
        messages: [{ role: "system", content: system }, ...messages],
      }),
    });
    const data = await res.json();
    return data.choices[0].message.content;
  }
}

This works, but you now own: two API keys, two billing dashboards, two sets of rate limits to monitor, and the maintenance burden of updating the wrapper every time either provider changes their schema. If you add streaming and tool calling to this function, it roughly doubles in size and complexity, and you're responsible for testing both branches whenever either vendor ships a change.

Option 2: Use a gateway that already normalizes Claude

The alternative is to put a gateway in front of the model you actually want to call and let it expose one HTTPS interface with consistent auth, consistent JSON shapes, and consistent streaming events — so your application code doesn't need provider-specific branches at all.

SubToAPI does this for Claude specifically: it turns your existing Claude access into a standard HTTPS API with sub_live_... application keys, so every one of your services calls the same endpoint shape regardless of which model behind it you're hitting.

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Summarize this ticket"}]
  }'

The practical benefit for teams running both Claude and OpenAI isn't that SubToAPI merges the two vendors into one call — it doesn't claim to be a multi-vendor router. The benefit is narrower and more useful in practice: it removes the Claude-specific integration pain (key management, streaming event parsing, usage metadata, team seats) so that the Claude side of your dual-provider setup is as simple as a single REST call, matching the simplicity you already get from OpenAI's API. That leaves you with one lightweight internal switch — "which endpoint do I call" — instead of two full SDK integrations to maintain.

Where this matters most:

• Team billing: Instead of sharing one Anthropic key across a team and tracking spend manually, each developer or service gets its own sub_live_... key with usage visible per key in one dashboard.
• Streaming: Server-sent events come through in a predictable format, documented at /docs/streaming, so you don't need custom event-type handling just for Claude.
• Tool use: Function calling against Claude follows the same request shape every time, documented at /docs/tools, which keeps your tool-handling code shorter even if you're also running OpenAI function calls elsewhere.

Deciding which approach fits

If you're a solo developer testing which provider gives better results for a specific task, a thin wrapper like the one above is fine — you want flexibility more than polish. If you're running either provider in production with a team, multiple projects, or need per-seat cost visibility, the maintenance cost of hand-rolled wrappers adds up fast, and offloading the Claude side to a dedicated API layer is usually the better trade.

Start with /docs/quickstart if you want to see how little code is needed to get Claude behind a standard REST interface, and check /pricing if you're comparing the cost of building this yourself against a managed layer. Plans start at €9/month for solo use, with team seats at €19 and €49 for scale, and a free trial at /signup.

FAQ

Is there one API that natively routes between Claude and OpenAI automatically? Not in a vendor-neutral way that either company officially supports. Most "single API" setups are either a custom abstraction layer you write, or a gateway for one provider (like SubToAPI for Claude) paired with that provider's own SDK for the other.

What's the biggest maintenance cost of running both APIs directly? Keeping request/response normalization, streaming parsers, and retry logic in sync across two schemas that change independently. Usage and cost tracking is usually the second-biggest pain point.

Does SubToAPI let me call OpenAI models through it? No — SubToAPI is focused on turning your Claude access into a clean HTTPS API with keys, streaming, tool use, and usage metadata. It doesn't proxy OpenAI; it's meant to simplify the Claude half of a multi-provider setup. See /docs/messages for the request format.