VisionPsy-Nano-460M-Flash is a new 460M vision-language model that uses only 64 visual tokens per image, cutting first-token latency to 0.3s on iPhone while retaining roughly 99% of the full model's benchmark score. The optimization trades off some OCR and fine-detail quality.
A new 460M vision model called VisionPsy-Nano-460M-Flash is taking a slightly different approach to on-device VLM speed. Instead of making the language model much smaller, it reduces how much visual information reaches it. For a 512x512 image: - VisionPsy Flash: 64 visual tokens - LFM2.5-VL-450M: 256 - Qwen3.5-0.8B: 256 - nanoVLM-460M: 1,088 - SmolVLM2-500M: 1,088 The team’s Q4_0 GGUF benchmarks report the following time to first token using the fastest tested backend on each phone: - Pixel 9: 6.1 seconds - Galaxy S23: 5.9 seconds - Galaxy S25 Ultra: 2.6 seconds - iPhone 15: 0.3 seconds For comparison, Qwen3.5-0.8B took 21.4s, 19.9s, 8.4s and 0.7s on the same devices. SmolVLM2 and the original nanoVLM were dramatically slower because they processed 1,088 image tokens. The mechanism is surprisingly simple. The full model upsamples images before splitting them into tiles. Flash preserves the native resolution instead, with a minimum of 512x512. That avoids creating extra visual tokens from pixels that were introduced only by upscaling. They report that Flash keeps around 99% of the full model’s normalized benchmark score: - Full model: 62.3 - Flash: 61.4 But the trade-off is not evenly distributed. OCR-heavy and fine-detail tasks such as TextVQA, OCRBench and ScienceQA lose more quality than normal scene understanding. Some important caveats: - These are the model creator’s own benchmarks, not independent results - The 0.3s number is time to first token, not a complete answer - It is designed for one image per query - Context is limited to 8K - The official llama.cpp instructions currently use a patched fork - Several evaluation changes are still waiting to be merged into VLMEvalKit For a one-sentence image description, they report complete response times of 10.6s on Pixel 9, 7.4s on the S23, 4.2s on the S25 Ultra and 0.7s on the iPhone 15. I think the interesting question is whether 64 visual tokens are genuinely enough for useful camera VQA, or whether the missing detail becomes obvious as soon as you point it at a receipt, dense screenshot or small text. Has anyone tested the Q4_0 GGUF independently? A comparison using the same normal photo, receipt and screenshot across Flash, LFM2.5-VL and Qwen3.5 would be much more useful than another aggregate benchmark. Model: https://huggingface.co/qvac/VisionPsy-Nano-460M-Flash GGUF: https://huggingface.co/qvac/VisionPsy-Nano-460M-Flash-GGUFs Code and official device benchmarks: https://github.com/tether-ai-research/qvac-visionpsy-nano
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