Testing Gemini 3.5 Flash Lite for human detection in home surveillance

A developer's benchmark of Google's Gemini 3.5 Flash Lite for human detection in a home surveillance system found the new model produced one false positive in 30 runs, while the existing Gemini 3.1 Flash Lite had zero false positives, leading the developer to discard the upgrade. The test, based on 18 real-world camera clips, showed Gemini 3.5 Flash Lite had a false positive on a dark dog at night with 0.90 confidence, and its price rose 50% to $0.30 per million input tokens and $2.50 per million output tokens, while Gemini 3.1 Flash Lite remains 40% cheaper. The developer concluded that switching models did not fix false positives; instead, asking the same model to re-check was effective, though it adds latency.

Testing Gemini 3.5 Flash Lite for human detection in home surveillance I’ve built a custom home surveillance system based around Frigate. Frigate does the person detection part and feeds Casa Segura for finer detection. It’s like a funnel for intrusion detection. Frigate’s algorithm is based on machine learning models such as TensorFlow Lite or OpenVINO, using models like MobileNet or YOLO for person detection. The system is urrently using Gemini-3.1-Flash-Lite which works well, but there is an problem with false positives, basically my dogs triggers the system detected as persons. I don’t want to hear an alarm at 3AM. That’s the main point on lowering the FP . Recently Gemini-3.5-Flash-Lite was introduced, there is 50% bump in pricing, which I find it odd, I would expect that the same tier of model continue to have the same pricing. Maybe they improved the model a lot, and that’s the justification of the price bump. I’m a bit tired of going through reading every new card of a model introduction. I was excited to test it because I’m waiting for a model that can lower the FP on zero shot. I’ve a small benchmark with clips from previous FP events, which are real world, currently the benchmark is managed by kimi k3 it was build with Claude Opus 4.5 and succesors models . 18 camera clips, verified frame by frame by “hand” 10 without humans — dogs day/night, dog lying down, dog far away; 8 with humans, including person + dogs in scene . FALSE POSITIVE When the model mistakes a dog for a person. | Model | FP 30 runs | Miss 24 runs | |---|---|---| | gemini-3.1-flash-lite production | 0 | 0 | | gemini-3.5-flash-lite the “new and improved” | 1 dark dog, far away, at night; conf 0.90 | 0 | | gemini-3.1-pro-preview the “pro” | 0 | 0 | Pricing Gemini 3.1 Pro is pretty expensive. 3.1 is still 40% cheaper than 3.5. | Model | Confidence | Price in/out $/M | |---|---|---| | gemini-3.1-flash-lite production | 0.95–1.00 | $0.25 / $1.50 | | gemini-3.5-flash-lite the “new and improved” | 0.90–0.95 | $0.30 / $2.50 | | gemini-3.1-pro-preview the “pro” | 0.95–1.00 | ~8× flash-lite | Latency by model Gemini 3.1 and 3.5 are very similar, but 3.1 wins. Gemini 3.1 Pro, is really slow. | Model | Latency | Cost 54 calls | Verdict | |---|---|---|---| | gemini-3.1-flash-lite production | ~2.9 s median | ~$0.04 est. | Kept | | gemini-3.5-flash-lite the “new and improved” | ~3 s | $0.059 measured | Discarded | | gemini-3.1-pro-preview the “pro” | ~10 s 3.4× | ~$0.30 est. | Discarded: same accuracy, 8× cost | 3 runs per clip, same prompt and transcode as production 6s, 2 fps, 768px , temperature 0.2. According to Kimi: The single FP of the 3.5 came with confidence 0.90 — exactly the kind of tail hallucination that no model eliminates. Against that, a more expensive model doesn’t win; a second query is needed. Conclusion Sadly the new model was marginally worse than the one I already had Gemini 3.1 Flash Lite ; what fixed the false positives wasn’t switching models, but asking the same model to check again. I feel that is hackish, not perfect and adds latency.