The gemini-2.0-flash-exp model supports internet connectivity, but the official version requires additional request parameters to enable it. Aihubmix has integrated this by automatically calling the official API's online functionality when the model name is requested with the "search" parameter.
Gemini 2.0 Flash Exp Search vs GPT 5.6 Luna
Compare Gemini 2.0 Flash Exp Search from Google and GPT 5.6 Luna from OpenAI on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.
GPT-5.6 Luna is designed for cost-sensitive, high-volume workloads. It roughly corresponds to the nano model tier used in earlier GPT-5 families.
Pricing & Specifications
Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.
Promotional prices show the discounted rate; see each model page for promotion windows.
Activity Past 30 Days
Daily traffic served through AIHubMix — how demand for each model is trending.
Tokens / day
Requests / day
Performance Past 3 Days
Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.
Throughput (tok/s)
TTFT (s)
Uptime (%)
Cost calculator
Estimate your monthly bill for the same workload on each model.
Monthly = daily × 30. Discounted rates applied where a promotion is active.
FAQ
Which is cheaper: Gemini 2.0 Flash Exp Search, GPT 5.6 Luna?
Gemini 2.0 Flash Exp Search: $0.4/M output tokens; GPT 5.6 Luna: $6/M. Use the cost calculator above to estimate your own workload.
What inputs and capabilities does each model support?
Gemini 2.0 Flash Exp Search accepts text and image input and supports web search, tool calling, function calling, structured outputs and long context; GPT 5.6 Luna accepts text and image input and supports tool calling, thinking and structured outputs.
Can I call Gemini 2.0 Flash Exp Search and GPT 5.6 Luna with the same API key?
Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.
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