ByteDance Releases Lance: 3B Unified Multimodal Model Matching 7B Benchmarks

One Model, All Modalities

ByteDance Research has released Lance, a lightweight unified multimodal model with 3 billion active parameters. Unlike siloed models that specialize in a single task, Lance handles image generation, video generation, image editing, video editing, image understanding, and video QA — all within a single architecture. It is available under the Apache 2.0 license.

Core Capabilities

Lance supports text-to-image (768×768), text-to-video (up to 121 frames at 480p), instruction-based image editing, frame-aware video editing, and visual question answering for both images and video. The model was fine-tuned from Qwen2.5-VL-3B-Instruct using a staged multi-task training recipe on 128 A100 GPUs.

Benchmark Performance

Despite its compact size, Lance delivers impressive results: DPG score of 84.67 (competitive with 7B models), GenEval 0.90, GEdit 7.30 (best-in-class among unified models), and VBench 85.11 (highest among tested models for video generation). These results challenge the assumption that unified models must sacrifice quality for versatility.

Availability

Model weights and inference scripts are available on GitHub (bytedance/Lance) and Hugging Face (bytedance-research/Lance). A minimum of 40GB VRAM is required. The release has generated significant interest in r/LocalLLaMA, where it received over 600 upvotes as a compelling option for locally-run multimodal tasks.