A Unified API for Claude, Gemini, and OpenAI

What "unified API" actually means here

If you're searching for a unified API for Claude, Gemini, and OpenAI, you're probably trying to solve one of two problems: you want to call multiple LLM providers through a single, consistent interface in your codebase, or you want one dashboard for keys, usage, and billing instead of three separate consoles. There is no single hosted product today that transparently proxies all three providers with identical request/response shapes and feature parity — tool calling, streaming formats, and token accounting differ enough between Anthropic, Google, and OpenAI that any "unified" layer is really a normalization layer you build or adopt, sitting on top of each provider's native API.

That's not a bad thing. A thin abstraction layer gives you model flexibility (swap providers without rewriting business logic), fallback behavior (route to a second provider if the first errors or rate-limits), and side-by-side evaluation (run the same prompt against three models and compare). The work is in designing a schema that captures the common 90% of each API's behavior without losing the features that make each model useful.

Why teams reach for this pattern

• Avoiding lock-in. If your product logic is written against one provider's SDK, switching later means rewriting prompt formatting, streaming handlers, and tool schemas.
• Redundancy. Anthropic, Google, and OpenAI all have occasional outages or rate-limit spikes. A router that can fail over to another provider keeps your app up.
• Cost and quality tradeoffs. Some tasks are cheaper on one model, better-quality on another. A unified layer lets you route by task type.
• Simpler billing and auditing. One place to see usage across providers, instead of three separate invoices and three sets of API keys scattered across services.

Designing the normalized schema

The core of any unified API is a request/response contract that maps cleanly onto Claude's Messages API, OpenAI's Chat Completions/Responses API, and Gemini's generateContent API. A minimal normalized request looks like this:

{
  "provider": "claude",
  "model": "claude-sonnet-4",
  "messages": [
    { "role": "user", "content": "Summarize this ticket in two sentences." }
  ],
  "max_tokens": 500,
  "stream": true,
  "tools": []
}

Your router translates this into the provider-specific shape before sending it out, and translates the response back into a normalized shape:

{
  "provider": "claude",
  "content": "The customer reports...",
  "finish_reason": "stop",
  "usage": { "input_tokens": 142, "output_tokens": 58 }
}

The hard parts are usually:

• Streaming. Each provider emits different event types (content block deltas, SSE chunks, token-by-token streams). You need a single internal event format your frontend consumes, regardless of which provider produced it.
• Tool/function calling. Claude's tool_use blocks, OpenAI's function/tool calls, and Gemini's function calling all encode arguments differently. Normalize to a single { name, arguments } shape internally, then re-encode per provider.
• Token accounting. Input/output token definitions aren't identical across providers, so any unified usage dashboard needs provider-aware conversion if you're tracking cost, not just raw counts.

A simple router function in JavaScript might look like:

async function callModel(request) {
  switch (request.provider) {
    case "claude":
      return callClaude(request);
    case "gemini":
      return callGemini(request);
    case "openai":
      return callOpenAI(request);
    default:
      throw new Error(`Unknown provider: ${request.provider}`);
  }
}

Each callX function handles auth, payload shaping, and response normalization for its provider. This is the pattern used by most open-source LLM gateways — a thin dispatch layer plus provider-specific adapters, not a magic single endpoint that erases the differences between models.

Where SubToAPI fits in this picture

SubToAPI doesn't try to be a multi-provider gateway — it does one thing well: it turns your existing Claude access into a clean, standard HTTPS API with its own application keys, so the Claude leg of your unified router is simple to build and operate. Instead of juggling raw Anthropic credentials, you get a sub_live_... key, a stable endpoint, streaming, tool use, and usage metadata out of the box.

In practice, your callClaude adapter becomes a call to SubToAPI instead of directly to Anthropic:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "max_tokens": 500,
    "messages": [
      { "role": "user", "content": "Summarize this ticket in two sentences." }
    ],
    "stream": true
  }'

That gives you the same streaming and tool-use behavior you'd write adapters for anyway (see /docs/streaming and /docs/tools), plus usage metadata per request and per-seat visibility if you're running this across a team. For the parts of your unified API that call Claude, you're not managing separate Anthropic console access per engineer — you're issuing application keys from one dashboard, which pairs naturally with however you're already handling Gemini and OpenAI keys for the other adapters. Check /docs/quickstart and /docs/messages to see the exact request/response shapes you'd be normalizing against.

Operational considerations beyond the API calls

Once the routing logic works, the harder problems are operational:

• Rate limit handling per provider. Each has different limits and backoff behavior; your router needs provider-aware retry logic, not one generic retry policy.
• Cost tracking across providers. You'll want usage metadata tagged by provider and model so you can see where spend is going — SubToAPI exposes this for the Claude side so it rolls up cleanly if you're aggregating across adapters.
• Team access control. If multiple engineers or services call these APIs, you want seat-based keys rather than one shared secret. SubToAPI's Team (€19/seat) and Scale (€49/seat) plans give each person or service their own key under one billing relationship — see /pricing.
• Testing provider parity. Build a small eval harness that runs the same prompt set through all three providers periodically, so you catch behavioral drift (a model update changing tone or refusal patterns) before it reaches users.

If you're just getting started, it's worth setting up the Claude leg first with a free trial at /signup, confirming streaming and tool use work the way your router expects, and only then wiring in the Gemini and OpenAI adapters with the same normalized contract.

Questions

Is there a single API that natively supports Claude, Gemini, and OpenAI with identical requests? No. Each provider has a different native API shape. "Unified" setups are a normalization layer you build (or adopt from an open-source gateway) that translates a common schema into each provider's format.

Does SubToAPI support Gemini or OpenAI? No — SubToAPI is focused on turning your Claude access into a clean HTTPS API with application keys, streaming, tool use, and usage metadata. It's well suited as the Claude adapter inside a broader multi-provider router.

What's the hardest part of building a unified LLM API? Normalizing streaming events and tool/function-calling schemas, since each provider encodes them differently. Token and cost accounting across providers is the second most common pain point.