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MiniCPM‑V 4.6 (1.3B) from OpenBMB is presented as a mobile/edge‑optimized…

2026-05-11 · 16:42 UTC ·@ginacostag_ ·1 min read

Brief

MiniCPM‑V 4.6 (1.3B) from OpenBMB is presented as a mobile/edge‑optimized multimodal model: LLaVA‑UHD v4 reportedly cuts vision encoding costs 55%, enabling native edge deployment. The team claims TTFT 75.7 ms (2.2× faster than Qwen3.5‑0.8B at 3136²), ~1.5× token throughput on an RTX 4090, and superior benchmark scores while using only 2.5% of Qwen’s token budget.

Source evidence

Speed and cost matter more than hype when teams actually need things running every day reliably.

⭐ Star the repo: github.com/OpenBMB/MiniCPM-V
📥 Like and Download the Model: huggingface.co/openbmb/MiniC…

OpenBMB (@OpenBMB)

1/5 MiniCPM-V 4.6 (1.3B) is now live 🚀🚀
High-res visual processing, optimized for consumer-grade and mobile hardware. We’ve leveraged the latest LLaVA-UHD v4 technique to cut vision encoding costs by 55%, enabling native edge deployment with extreme efficiency.
🔥 Beats Gemma4-E2B-it and Qwen3.5-0.8B across key multimodal and Artificial Analysis benchmarks — scoring higher than Qwen3.5-0.8B using just 2.5% of its token budget.
⚡ TTFT (75.7ms) 2.2x Faster than Qwen3.5-0.8B even with 3136² high-res images.
🏗️ ~1.5x Token Throughput compared with Qwen3.5-0.8B on a single RTX 4090.
Try the model here:
🤗 Hugging Face:
huggingface.co/openbmb/MiniC…
💻 GitHub:
github.com/OpenBMB/MiniCPM-V
🔭 Modelscope:
modelscope.cn/models/OpenBMB…
🌐 Web Demo:
huggingface.co/spaces/openbm…
📱 App Demo:
github.com/OpenBMB/MiniCPM-V…

Video

— https://nitter.net/OpenBMB/status/2053857238805311824#m