Mobile users expect immediate, context-aware responses, but on-device models are constrained by memory and thermal limits. Offloading inference to a remote API unlocks frontier reasoning, code generation, and multimodal understanding, yet it introduces new variables: token cost scaling, cold-start latency, and SDK lock-in. Oxlo.ai removes these barriers with a fully OpenAI-compatible inference platform that charges a flat rate per request regardless of prompt length. You get access to more than 45 open-source and proprietary models, zero cold starts on popular endpoints, and a simple base URL change to migrate existing code.
Architecture: Direct Client or Proxied Backend
There are two common patterns for mobile LLM integration. The first is a direct HTTPS call from your iOS or Android client to the inference provider. This is fastest to prototype because you control the request lifecycle in Swift, Kotlin, or Dart. The second is a proxied backend: your mobile app speaks to your own server, and the server calls the LLM API. This keeps secrets out of the app binary and lets you cache or log requests.
Because Oxlo.ai exposes the standard OpenAI API schema at https://api.oxlo.ai/v1, both patterns are trivial to adopt. If you already use an OpenAI SDK on your backend, you only need to change the baseURL and API key. If you are calling directly from a mobile client, any HTTP client works as long as it sends the correct Authorization header and JSON payload.
Direct Integration from Mobile
For early prototypes or offline-first apps with occasional sync, a direct REST call keeps the stack simple. Below is a cURL example that translates directly into Swift URLSession, Kotlin Ktor, or Flutter http.
curl https://api.oxlo.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OXLO_API_KEY" \
-d '{
"model": "llama-3.3-70b",
"messages": [
{"role": "system", "content": "You are a concise assistant."},
{"role": "user", "content": "Explain how threading works in Android."}
],
"stream": true,
"max_tokens": 512
}'Notice the stream: true parameter. On mobile, streaming is essential. Rather than blocking the UI while the model generates a full response, you can render tokens as they arrive. Oxlo.ai supports streaming responses on all chat models with no cold starts, so the first chunk arrives immediately after the request is accepted.
Backend Proxy with OpenAI SDK Drop-In
In production, you should route traffic through a backend to hide your API key. Oxlo.ai is a fully OpenAI SDK-compatible drop-in replacement. A Node.js proxy requires only two lines of changed configuration.
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: process.env.OXLO_API_KEY,
baseURL: 'https://api.oxlo.ai/v1',
});
const stream = await client.chat.completions.create({
model: 'qwen3-32b',
messages: [{ role: 'user', content: req.body.message }],
stream: true,
});
for await (const chunk of stream) {
res.write(chunk.choices[0]?.delta?.content || '');
}
res.end();
The same pattern works in Python. Your mobile app then talks to your own endpoint, and your server talks to Oxlo.ai. This separation also lets you implement retries, rate limiting, and user-specific quotas without touching client code.
Choosing a Model for Mobile Use Cases
Oxlo.ai hosts models across seven categories, so you can match capability to battery and UX constraints on the client side. Because inference runs remotely, you are selecting for quality and latency, not on-device footprint.
- General chat and reasoning: Llama 3.3 70B is the workhorse for Q&A and instruction following.
- Multilingual and agent workflows: Qwen 3 32B handles non-English inputs and tool-heavy dialogues.
- Deep reasoning and complex coding: DeepSeek R1 671B MoE or DeepSeek V4 Flash deliver step-by-step logic. DeepSeek V4 Flash also offers a 1M context window for large codebases.
- Vision: Gemma 3 27B and Kimi VL A3B process image inputs for accessibility, shopping, or document scanning.
- Audio: Whisper Large v3 / Turbo / Medium transcribes voice memos, while Kokoro 82M provides text-to-speech for read-aloud features.
You can switch models by changing a single string in the payload. A/B testing Llama 3.3 70B against Qwen 3 32B for your audience requires no client deploy, only a backend config change.
Structured Output and Native Actions
Mobile UIs rarely display raw markdown. You usually need JSON to populate SwiftUI or Jetpack Compose components. Oxlo.ai supports JSON mode, which constrains the model to valid JSON.
{
"model": "deepseek-v3.2",
"messages": [
{"role": "user", "content": "Extract name, date, and total from this receipt."}
],
"response_format": {"type": "json_object"}
}For deeper integration, function calling lets the model invoke native app capabilities. Define a schema for calendar events, map searches, or smart-home actions, and the model will return a structured tool call rather than plain text. Your client executes the action and returns the result in a follow-up message. This is ideal for agentic assistants that need to interact with the phone's ecosystem.
Cost Control with Request-Based Pricing
Token-based billing creates unpredictable costs on mobile. A user might paste a long document, upload a high-resolution image, or maintain a multi-turn conversation that grows with every message. With token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale, your bill scales with input length.
Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. For long-context and agentic workloads, this can be 10-100x cheaper than token-based alternatives. You can send full conversation history, RAG context chunks, and detailed system prompts without watching token counters. Plans start with a Free tier at $0 per month for 60 requests per day across 16-plus models, including a 7-day full-access trial. Paid tiers include Pro at $80 per month for 1,000 requests per day and Premium at $350 per month for 5,000 requests per day with priority queueing. Enterprise plans offer custom unlimited volume on dedicated GPUs. For exact rates, see https://oxlo.ai/pricing.
Adding Vision and Audio
Modern phones have powerful cameras and microphones, and Oxlo.ai exposes endpoints that let you use them. Send base64-encoded images or URLs to the chat completions endpoint with a vision model such as Kimi K2.6, which supports advanced reasoning, agentic coding, and a 131K context window alongside image input. For image generation, call /v1/images/generations with Oxlo.ai Image Pro, Ultra, Flux.1, or Stable Diffusion 3.5.
For audio, /v1/audio/transcriptions accepts voice memos via Whisper Large v3, Turbo, or Medium. Convert responses to speech with /v1/audio/speech and Kokoro 82M. Because these are standard REST endpoints, you pipe them through the same backend proxy you use for chat.
Security and Key Management
Never ship an Oxlo.ai API key inside your mobile binary. Reverse engineering can extract strings from compiled apps. Instead, store the key in your backend environment and authenticate mobile users with your own session tokens. If you are prototyping without a backend, use the Free tier key and rotate it frequently, but treat this as a temporary stage. Oxlo.ai also supports multi-turn conversations, so you can maintain state on the server and avoid exposing conversation history to the client.
Shipping Your Integration
Integrating an LLM into a mobile app is now a matter of standard HTTP and JSON. You do not need custom SDKs, token cost calculators, or cold-start workarounds. Oxlo.ai gives you an OpenAI-compatible endpoint, flat request pricing, and a catalog of more than 45 models spanning chat, code, vision, image generation, audio, embeddings, and object detection. Point your client or proxy to https://api.oxlo.ai/v1, pick a model that fits your use case, and ship.
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