Developer comparison of GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro for generative UI in React Native. Covers JSON reliability, latency, cost, and which model works best with WireAI's component registry.
For React Native generative UI, GPT-4o and Claude 3.5 Sonnet both produce reliable component JSON with under 5% Zod validation failures. Gemini 1.5 Pro is 40% cheaper but has a higher hallucination rate on strict schemas. For local-first apps, Llama 3 8B via Ollama beats all three on cost and privacy.
Choosing an LLM for a mobile AI app isn't just about benchmark scores. The question is: which model reliably picks the right UI component, fills its Zod-validated props correctly, and responds fast enough that the user doesn't see a spinner for more than 2 seconds?
The evaluation: WireAI component selection
Each model was tested on the same task: given a registry of 11 components with descriptions and Zod schemas, pick the most appropriate component for a user message and return its props as valid JSON. 200 test cases across 5 app domains, coaching, productivity, e-commerce, health, and finance. The primary metric is Zod validation pass rate on the first attempt.
GPT-4o: Highest reliability
GPT-4o scores highest on Zod pass rate (97%). Its JSON output is consistent, it rarely hallucinates component names, and it correctly interprets .describe() hints on schema fields. The main drawback is cost: at $5/$15 per million input/output tokens, heavy usage burns money fast. For premium or enterprise tiers where reliability matters more than cost, GPT-4o is the safe default.
// WireAI Pro (coming v0.2), OpenAI adapter
import { CloudAdapter } from '@wireai/cloud';
const adapter = new CloudAdapter({
provider: 'openai',
model: 'gpt-4o',
apiKey: process.env.OPENAI_API_KEY,
});Claude 3.5 Sonnet: Best for complex agentic reasoning
Claude 3.5 Sonnet matches GPT-4o on JSON reliability (96%) and significantly outperforms it on multi-step reasoning. When user intent is ambiguous, "can you help me with that thing from yesterday?", Claude uses conversation context better to pick the right component. Latency (1.2–2s first token) is slightly higher than GPT-4o's 0.8–1.5s, and cost is comparable.
Gemini 1.5 Pro: Best value at scale
Gemini 1.5 Pro has a 91% Zod validation pass rate, respectable but noticeably below the OpenAI/Anthropic options. Its 1M-token context window is mostly irrelevant for WireAI's turn-by-turn model. The main advantage is pricing: ~40% cheaper than GPT-4o. The 9% failure rate is manageable because WireAI's fallback renderer handles invalid JSON gracefully, never crashing the UI.
Llama 3 8B (local): Zero cost, good enough for simple apps
When the component registry is small (5–8 components) and prompts are clear, Llama 3 8B via Ollama achieves 89% Zod pass rate. For complex schemas or large registries (15+ components), it drops to ~75%. The tradeoff: $0 per token, completely private, offline capable. For the free tier of your app, this is the right choice.
Which model should you use for your use case?
- Free tier / dev / privacy-first: Llama 3 8B via Ollama. Zero cost, reliable for simple registries.
- Complex agents with ambiguous intent: Claude 3.5 Sonnet. Best reasoning, high JSON reliability.
- High-volume consumer apps: Gemini 1.5 Pro. 40% cheaper with WireAI's fallback layer absorbing the difference.
- Highest reliability needed: GPT-4o. 97% pass rate, most predictable JSON output.
All four options work with WireAI. The free OSS tier includes local LLM adapters. Cloud adapters (OpenAI, Anthropic, Gemini) ship in @wireai/cloud, run npm install wireai-rn zod to start.