AI Model Selection: Gemini, OpenAI, and Open-Source Models

Model rankings change monthly. What changes slowly is how each option fits an Android product: SDKs, on-device availability, data terms, cost structure and who operates the infrastructure. Decide on those, keep providers behind an interface, and let your own evaluation choose the specific model.

Comparison for Android teams

Model names, capabilities and prices change frequently. Verify current details on each provider's documentation before deciding.

Provider-neutral architecture

interface AiAssistant {
    val id: String                                     // e.g. "gemini-cloud", "backend-gateway"
    suspend fun isAvailable(): Boolean
    fun reply(messages: List<ChatTurn>): Flow<String>  // streaming text
}

data class ChatTurn(val role: Role, val text: String) { enum class Role { USER, ASSISTANT } }

Decision factors

1. Where must data go? If it must stay on the device, choose Gemini Nano or an on-device open model. If it must stay in your cloud, choose self-hosted open models or enterprise offerings.
2. Do you need a backend anyway? For RAG over private data, tools and quotas, a gateway makes multiple providers easy.
3. Time to market: Firebase AI Logic is the fastest secure path to Gemini on Android.
4. Quality on your task: run the same evaluation set across candidates. See Evaluating AI Responses.
5. Cost at your scale: estimate tokens per user per month for each. See Tokens, Latency and Cost.

Typical choices

Common mistakes

• Choosing by benchmark headlines instead of task evaluation.
• Hard-coding one provider's request format throughout the app.
• Underestimating the operational cost of self-hosting open models.
• Ignoring data terms and regional requirements until legal review.

Frequently asked questions

Conclusion

Gemini offers the deepest Android integration and on-device option, OpenAI is a strong managed cloud choice through your backend, and open-weight models give maximum control at operational cost. Decide by data requirements, architecture and your own evaluation, behind a provider-neutral interface.

Next step: control the economics with AI Tokens, Latency and Cost Optimization.