One Unified API for Claude, GPT & Gemini: How to Call All Three Models in 2026

A unified API for Claude, GPT and Gemini is a single OpenAI-compatible endpoint (one base URL, one API key) that routes requests to any of the three model families by simply changing the model parameter — no separate Anthropic SDK, OpenAI SDK, or Google SDK integration required. This matters in 2026 because most serious AI products no longer bet on one model; they route tasks to whichever model is best (or cheapest) for the job, and maintaining three parallel client integrations for that is a waste of engineering time.

Why teams stopped using a single model

Through 2025 and into 2026, the gap between Claude, GPT and Gemini narrowed a lot on raw benchmarks, but each still has a clear edge somewhere: Claude Opus 4.8 and the newer Sonnet 5 line remain the default for long-context coding and agentic tool use, GPT-5.5 is often cheaper for high-volume simple completions, and Gemini 3 Pro tends to win on native multimodal (video/audio) tasks and huge context windows. Once a product needs more than one of these strengths, hardcoding a single vendor SDK becomes a liability — every prompt-routing experiment or fallback plan turns into a multi-week integration project.

What "one API, three models" actually looks like

The pattern that has become standard is: an OpenAI-compatible /v1/chat/completions endpoint sits in front of the relay, and you pick the model per-request. The client code barely changes between providers — only the model string does:

import openai

client = openai.OpenAI(
    api_key="YOUR_KEY",
    base_url="https://your-relay.example/v1"
)

# Claude for a long agentic coding task
resp = client.chat.completions.create(
    model="claude-sonnet-5",
    messages=[{"role": "user", "content": "Refactor this function..."}]
)

# GPT for a cheap, high-volume classification job
resp2 = client.chat.completions.create(
    model="gpt-5.5-mini",
    messages=[{"role": "user", "content": "Classify this ticket..."}]
)

# Gemini for a video/multimodal query
resp3 = client.chat.completions.create(
    model="gemini-3-pro",
    messages=[{"role": "user", "content": "Describe this video frame..."}]
)

Same base_url, same key, same request shape. This is exactly how OpenAI-compatible relays (and tools like LiteLLM if you self-host) abstract the differences between provider APIs.

Three ways to get a unified endpoint

What to check before picking a relay

Not all "unified API" providers are equal. Before committing, verify:

• Real model coverage — does it actually proxy the current Claude, GPT and Gemini releases (not stale/deprecated versions)?
• Prompt caching pass-through — Claude's prompt caching can cut input-token cost by 70-90% on repeated system prompts; a relay that doesn't support the cache headers throws that saving away.
• Payment friction — many official APIs require a US-issued credit card, which blocks a large share of developers outside the US.
• Latency and streaming stability — a middleman that breaks SSE streaming mid-response is worse than no middleman.
• Pricing transparency — pay-as-you-go with visible per-model rates, not an opaque credit-bundle scheme.

This is the gap Safa API is built to close. It exposes Claude, GPT and Gemini through one OpenAI-compatible endpoint and one API key, at lower per-token prices than calling the official APIs directly. It fully supports Claude's prompt caching, so repeated system prompts and long context windows stay cheap. There's no credit card required to sign up, and — useful for developers in China and other regions where card payments are awkward — Alipay is accepted directly. It plugs straight into Claude Code, Cline, Cursor and SillyTavern by just changing the base URL, so switching from a single-vendor setup to a unified one usually takes under five minutes.

Frequently Asked Questions

Does a unified API slow down responses compared to calling providers directly?

A well-run relay adds only a few milliseconds of routing overhead; the model inference time itself dominates latency either way. The bigger risk is a poorly built proxy that buffers streaming responses instead of passing them through in real time — check for stable SSE streaming before committing to a provider.

Can I still use prompt caching and function calling through a unified endpoint?

Yes, as long as the relay explicitly forwards the caching headers and tool-use/function-calling fields rather than stripping them. This varies by provider, so it's worth testing a real cached request before relying on it in production.

Is a unified API cheaper than paying each provider separately?

Often yes, since a relay can offer volume-based discounted rates and avoids the fixed costs (minimum spend, credit card currency conversion fees) tied to three separate provider accounts — on top of the convenience of one bill and one key.