AI Gateway — Client Portal

Available Balance

—

TMT (Turkmenistan Manat)

Balance is deducted per API request based on token usage. Top up via USDT (TRC-20) on the Deposit tab.

Usage by day

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API Keys

Transaction History

USDT Deposit (TRC-20)

Send USDT on the TRC-20 network to this address. Your TMT balance will be credited automatically within 1-2 minutes after confirmation.

Important: Only send USDT (TRC-20) to this address. Sending other tokens may result in permanent loss.

Available Models

All models available for use via the API. Prices are per 1 million tokens and update automatically.

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API Documentation

Everything you need to integrate AI Gateway into your application.

Base URL

Quick Start

Register an account on this portal and top up your TMT balance via the Deposit tab.
Create an API key on the API Keys tab. Copy it immediately — it is shown only once.
Make your first request — see examples below. Use the API key as a Bearer token.

Code Examples

# Replace YOUR_API_KEY with your key from the API Keys tab

curl -X POST /v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-70b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

# Streaming
curl -X POST /v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-70b",
    "messages": [{"role": "user", "content": "Hello!"}],
    "stream": true
  }'

# pip install openai
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="/v1",
)

# Regular request
response = client.chat.completions.create(
    model="llama-3.1-70b",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

# Streaming
stream = client.chat.completions.create(
    model="llama-3.1-70b",
    messages=[{"role": "user", "content": "Explain quantum physics"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

// npm install openai
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "/v1",
});

// Regular request
const response = await client.chat.completions.create({
  model: "llama-3.1-70b",
  messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);

// Streaming
const stream = await client.chat.completions.create({
  model: "llama-3.1-70b",
  messages: [{ role: "user", content: "Explain quantum physics" }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

Embeddings

For embedding models (e.g. nomic-embed-text, mxbai-embed-large) use POST /v1/embeddings. Billed by input tokens only (no output tokens).

# Single string
curl -X POST /v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nomic-embed-text",
    "input": "Text to embed"
  }'

# Batch (array of strings)
curl -X POST /v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nomic-embed-text",
    "input": ["First text", "Second text", "Third text"]
  }'

# pip install openai
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="/v1",
)

# Single string
result = client.embeddings.create(
    model="nomic-embed-text",
    input="Text to embed",
)
vector = result.data[0].embedding
print(f"Vector length: {len(vector)}")

# Batch
result = client.embeddings.create(
    model="nomic-embed-text",
    input=["First text", "Second text", "Third text"],
)
for item in result.data:
    print(f"[{item.index}] len={len(item.embedding)}")

// npm install openai
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "/v1",
});

// Single string
const result = await client.embeddings.create({
  model: "nomic-embed-text",
  input: "Text to embed",
});
console.log("Vector length:", result.data[0].embedding.length);

// Batch
const batch = await client.embeddings.create({
  model: "nomic-embed-text",
  input: ["First text", "Second text", "Third text"],
});
batch.data.forEach(item => {
  console.log(`[${item.index}] len=${item.embedding.length}`);
});

Available Models

See the Models tab for the full list with prices. Or fetch via API:

curl /v1/models \
  -H "Authorization: Bearer YOUR_API_KEY"

Authentication

Pass your API key in the Authorization header:

Authorization: Bearer gw_your_api_key_here

API keys start with gw_. Never share your key — it grants full access to your balance.

Billing

Balance is held in TMT (Turkmenistan Manat).
Each request is billed by input + output tokens at the model's rate.
Billing happens after the response completes (including streaming).
If balance is insufficient, the request returns 402 Payment Required.
Top up via USDT TRC-20 on the Deposit tab — credited within 1–2 minutes.

Error Codes

Code	Meaning	Fix
`401`	Invalid or revoked API key	Check key on API Keys tab
`402`	Insufficient balance	Top up via Deposit tab
`404`	Model not found	Check model name via `GET /v1/models`
`503`	All providers unavailable	Retry in a few minutes