One API for all AI models: how a unified LLM API works

A year or two ago, most AI products were built on a single model from a single provider. That's rarely true anymore. Teams use Claude for coding, a GPT model for general assistance, a budget model for high-volume classification, and an image or video model for media, and they switch between them as new releases arrive every few weeks. Each of those providers comes with its own account, key, billing, dashboard and, often, its own API format.

A unified LLM API solves that by putting every model behind a single endpoint. You get one API key, one balance and one request format, and you choose the model by name in each request.

The cost of juggling providers

Using several providers directly works, but the overhead grows with every one you add:

• Separate accounts and keys to create, rotate and secure for every provider and environment.
• Separate bills and prepaid balances, each of which can run out on its own schedule.
• Different request formats: OpenAI's Chat Completions and Responses, Anthropic's Messages, and provider-specific variations of each.
• Usage scattered across dashboards, which makes it hard to see what a feature really costs.
• Switching models means code changes, not just a config change, so teams stick with a model long after a better or cheaper one ships.

How a unified LLM API works

A unified API sits between your application and the model providers. Your code sends a normal request to one base URL, naming the model it wants. The gateway authenticates your key, checks your balance and limits, forwards the request to a provider that serves that model, streams the response back, and records the usage for billing.

The key design choice is the request format. The most useful unified APIs don't invent a new one; they speak the formats developers already use. If the gateway accepts OpenAI-style and Anthropic-style requests, the official SDKs work unchanged. Moving to it is a matter of changing the base URL and the key, and every model becomes reachable through code you've already written.

What you gain

• One key and one prepaid balance for every model, instead of one per provider.
• Freedom to pick the best model for each task, and to change your mind when a new one ships.
• One place to see usage and cost by model and by key.
• One set of controls: spend limits, pausing and revoking apply to every model at once.
• Easier comparisons, since running the same prompt across several models needs no extra setup.
• Often a lower price: some unified APIs sell models below their official list price, as our LLM API pricing comparison shows.

What to look for in a unified LLM API

Unified APIs differ a lot in the details, and the details decide whether one is a drop-in replacement or a new integration project. Check these before you commit:

1. Format compatibility. Does it speak both OpenAI and Anthropic formats natively, including streaming, tool calling, image inputs and prompt caching? Anything less means code changes.
2. Model coverage. Are the models you use today available, and are new releases added quickly? Does it cover images and video as well as text?
3. Transparent pricing. Is each model's price shown next to the official price, so you can see whether you're paying a markup or getting a discount?
4. Billing model. Is it pay as you go, or does it lock you into a subscription or minimum commitment?
5. Controls. Can you set spend limits per key, and see every request with its model, tokens and cost?
6. Data handling. What happens to your prompts and outputs? The best answer is that they aren't stored at all.
7. Reliability. Is there a public status page with per-model availability, and a clear error format?

The trade-offs

A unified API is another service in your request path, so it's fair to weigh the downsides. It adds a network hop, although for LLM calls that take seconds the extra latency is usually small next to the model's own generation time. Very new or provider-specific features may arrive a little later than on the provider's own API. And you're trusting the gateway with your traffic, which is why data handling and transparency matter so much in the checklist above.

For most teams the balance tips clearly in favour: the time saved managing providers, the freedom to switch models, and the lower bills outweigh the costs. If you rely on a feature only one provider offers, check that the gateway supports it before moving that traffic.

How GreenTokens does it

GreenTokens is one API for all the major AI models. It serves more than 30 text models, including Claude, GPT, Grok, DeepSeek, GLM, Kimi and MiniMax, plus image and video models, through OpenAI-compatible Chat Completions and Responses endpoints and the Anthropic Messages API. The official SDKs, Claude Code, Codex CLI, opencode and other tools work by changing the base URL.

• Prices are shown next to each model's official list price, with many models at 40 to 60% below it.
• You top up a prepaid balance with crypto; there's no subscription or minimum spend.
• Every API key can carry daily and monthly spend limits, and every request appears in your dashboard with its model, tokens and cost.
• Prompts and model outputs are never stored.
• Per-model availability and uptime are published on a public status page.

Getting started

Sign in with your email, add funds, and create an API key. Then point your OpenAI SDK at https://api.greentokens.io/v1, or your Anthropic SDK at https://api.greentokens.io, and call any model by name. The docs include copy-paste examples for text, images and video, and setup guides for popular coding tools and agents.