Multi-Language API with Arabic Support – LLM Resayil

Introduction

Developers building chatbots, virtual assistants, or any user‑facing application today must think beyond English. A truly global product needs to converse fluently in the languages of its audience, and Arabic is one of the fastest‑growing markets on the web. LLM Resayil answers that need with an OpenAI‑compatible API that supports Arabic out of the box while also handling many other languages. In this guide we’ll explore why multilingual support matters, what Resayil offers, how to integrate it, pricing, and how it stacks up against the biggest providers.

Quick Comparison

| Feature | LLM Resayil | OpenAI | |---|---|---| | OpenAI‑compatible endpoints | ✅ | ✅ (native) | | Anthropic‑compatible endpoints | ✅ | ❌ | | Arabic language support | ✅ | ✅ (limited) | | General multi‑language support | ✅ | ✅ | | Streaming responses | ✅ | ✅ | | Function calling | ✅ | ✅ | | Vision models | ✅ | ✅ | | Thinking (reasoning) models | ✅ | ✅ | | Tool use / function execution | ✅ | ✅ | | Pay‑per‑use credits | ✅ | ✅ | | Hosting location | USA | USA | | Number of active models | 39 | 4 (GPT‑4, GPT‑3.5, etc.) | | Billing currency | USD | USD | | Payment methods | Stripe, PayPal | Stripe, credit card | | Integrations | n8n, LangChain, LiteLLM, OpenAI SDK, Anthropic SDK, Python, JavaScript, cURL | OpenAI SDK, Python, JavaScript, cURL |

What LLM Resayil Offers

Resayil’s API is built to be a drop‑in replacement for the OpenAI and Anthropic ecosystems. All standard endpoints – /v1/chat/completions, /v1/models, /v1/messages, and the token‑count helper – behave exactly as developers expect. The platform ships with 39 active models ranging from chat‑optimized (e.g., deepseek-v4-flash) to vision‑enabled (e.g., qwen3-vl:235b-instruct) and reasoning‑focused (e.g., cogito-2.1:671b). This variety lets you pick the right balance of speed, cost, and capability for each use case.

Key capabilities include:

• Arabic language support – Arabic text is tokenized and processed natively, giving you higher quality outputs for Arabic‑speaking users.
• Multi‑language handling – The same endpoints accept prompts in many languages, making it easy to build a single backend that serves a global audience.
• Streaming – Real‑time token delivery works for any language, so users see responses appear instantly.
• Function calling & tool use – You can define functions that the model may invoke, enabling structured data retrieval or external API calls while still responding in the user’s language.
• Vision – Image‑understanding models let you add OCR or visual context to multilingual conversations.
• Thinking models – Larger reasoning‑oriented models give you deeper chain‑of‑thought capabilities when complex logic is required.

All of these features are accessible through the same familiar HTTP interface, and the API is fully compatible with existing OpenAI and Anthropic SDKs.

What OpenAI Offers

OpenAI provides a mature suite of models (GPT‑4, GPT‑3.5‑Turbo, etc.) that are widely adopted across the industry. Their API includes streaming, function calling, and vision (via GPT‑4‑Vision). OpenAI’s SDKs are robust and well‑documented, and the platform supports a large ecosystem of third‑party tools.

However, OpenAI’s native Arabic performance, while improving, is not marketed as a dedicated focus. Multi‑language handling works, but developers often need to add custom preprocessing for right‑to‑left scripts. Additionally, Anthropic‑compatible endpoints are not available directly from OpenAI, requiring separate integration if you wish to use Claude models.

Why LLM Resayil Wins for Multilingual Chatbots

If your primary audience includes Arabic speakers or you need a single API that works equally well across many languages, Resayil gives you a clear advantage:

1. Dedicated Arabic support – Tokenization and model training are tuned for Arabic, reducing common errors like broken word boundaries.
2. One‑stop compatibility – Use the same codebase for OpenAI‑style calls and Anthropic‑style calls without switching providers.
3. Broad model catalog – With 39 models, you can start with a lightweight chat model and later switch to a larger reasoning or vision model without changing your integration code.
4. Transparent pay‑per‑use pricing – No hidden tiers; you pay only for the tokens you consume, billed in USD via Stripe or PayPal.
5. US‑based hosting – All requests are served from the United States, ensuring low latency for developers based there.

These factors make Resayil a pragmatic choice for developers who need reliable Arabic output and the flexibility to experiment with different model types.

Concrete Benefits You Get

• Speed to market – Drop‑in OpenAI‑compatible endpoints mean you can replace a prototype with Resayil in minutes.
• Cost control – Pay‑per‑use credits let you monitor usage per language and optimize budgets.
• Tooling ecosystem – Ready‑made integrations with n8n, LangChain, and LiteLLM let you build workflows without writing boilerplate HTTP code.
• Future‑proofing – As new models are added to the catalog, they become instantly available through the same endpoint URLs.
• Developer familiarity – Use the same Python (openai) or JavaScript (openai) SDKs you already know, or switch to the Anthropic SDK when needed.

How to Integrate LLM Resayil for Multilingual Applications

1. Choose Your SDK

• Python – Install the standard OpenAI package (pip install openai) and point it at Resayil’s base URL.
• JavaScript/Node – Use the openai npm package (npm install openai).
• cURL – Direct HTTP calls work for any language.
• Anthropic SDK – Works the same way; just change the base URL.

2. Set the Base URL

All requests must be sent to https://llm.resayil.io/v1. Example in Python:

import openai
openai.api_base = "https://llm.resayil.io/v1"
openai.api_key = "YOUR_RESAYIL_API_KEY"

3. List Available Models

curl https://llm.resayil.io/v1/models

The response will include the 39 model slugs (e.g., deepseek-v4-flash, qwen3-vl:235b-instruct). Pick one that matches your use case.

4. Make a Chat Completion Request

Below is a Python example that asks a model to respond in Arabic:

response = openai.ChatCompletion.create(
    model="deepseek-v4-flash",
    messages=[
        {"role": "system", "content": "You are a helpful assistant that replies in Arabic."},
        {"role": "user", "content": "ما هو الطقس اليوم في دبي؟"}
    ],
    stream=False  # set True for streaming
)
print(response.choices[0].message.content)

The same request works in JavaScript, cURL, or via LangChain by swapping the SDK import.

5. Enable Function Calling (Optional)

If you need the model to call a function—say, to fetch live weather data—define the function schema and set function_call="auto". The model will return a function_call object that you can execute and then feed the result back to the model.

functions = [{
    "name": "get_weather",
    "description": "Retrieve weather for a city",
    "parameters": {
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"]
    }
}]

resp = openai.ChatCompletion.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "ما هو الطقس في القاهرة؟"}],
    functions=functions,
    function_call="auto"
)

6. Use Vision Models

For image‑plus‑text queries, call the /v1/chat/completions endpoint with a vision model and include a base‑64 encoded image in the content field. The response will contain a textual answer in the language of the prompt.

7. Integrate with n8n or LangChain

Both platforms have built‑in HTTP request nodes. Point them to https://llm.resayil.io/v1/chat/completions and supply the model slug. The same workflow can be duplicated for different languages by changing the system prompt.

Pricing Model for Multi‑Language Usage

Resayil uses a pay‑per‑use credit system. Every token generated or consumed counts against your credit balance. Billing is done in USD and you can replenish credits via Stripe or PayPal. There are no monthly subscription fees or hidden minimums—simply add credits when you need them and the API will deduct usage automatically. Detailed pricing per model can be fetched from the /v1/pricing endpoint.

Getting Started with LLM Resayil’s Multi‑Language API

1. Create an account at the portal: https://llm.resayil.io and obtain an API key.
2. Check health – GET https://llm.resayil.io/v1/health should return {"status":"ok"}.
3. List models – GET https://llm.resayil.io/v1/models to see the full catalog.
4. Make a test request – Use the Python snippet above or a simple cURL command:

curl https://llm.resayil.io/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "deepseek-v4-flash",
        "messages": [{"role": "user", "content": "Hello, how are you?"}]
      }'

5. Explore advanced features – streaming, function calling, and vision by adding the appropriate flags as shown in the earlier sections.
6. Monitor usage – call /v1/messages/count_tokens to see how many tokens each request consumes, helping you stay within budget.

You’re now ready to build multilingual chatbots, assistants, or any AI‑powered experience that speaks Arabic and many other languages.

Frequently Asked Questions

Q: What languages does LLM Resayil support? A: Resayil supports multiple languages with a specific emphasis on Arabic. The exact list isn’t publicly enumerated, but you can test any language by sending a prompt in that language; the models are built to handle a broad linguistic range.

Q: Is LLM Resayil compatible with OpenAI and Anthropic SDKs? A: Yes. The API follows the OpenAI specification and also provides Anthropic‑compatible endpoints, allowing you to use existing Python, JavaScript, and cURL SDKs without code changes.

Q: Can I use streaming with multi‑language requests? A: Absolutely. Streaming is supported for both the chat completions and messages endpoints, delivering token‑by‑token output in any language you send.

Q: How does billing work for multi‑language API calls? A: Billing is based on a pay‑per‑use credit system measured in tokens. All charges are in USD and you can pay via Stripe or PayPal.

Q: Does LLM Resayil support function calling and tool use in multiple languages? A: Yes. Function calling and tool use are available features, and they operate alongside the API’s multilingual capabilities.

Ready to add Arabic‑ready, multilingual intelligence to your app? Register now, explore the pricing page, and dive into the documentation to get started.