Curiosity · AI Model

DeepSeek-VL2

DeepSeek-VL2 is DeepSeek's second-generation open vision-language model family, released in December 2024. Built on the DeepSeekMoE sparse-MoE backbone, the three variants — Tiny (3B total / 1B active), Small (16B / 2.8B) and base VL2 (27B / 4.5B) — combine high parameter counts with efficient inference. A dynamic tiling strategy enables fine-grained high-resolution understanding, and a VL-specific multi-head latent attention cuts KV-cache cost on long image+text sequences.

Model specs

Vendor: DeepSeek
Family: DeepSeek-VL
Released: 2024-12
Context window: 4,096 tokens
Modalities: text, vision

Strengths

Sparse MoE delivers dense-model quality at fraction of active params
Dynamic tiling handles high-resolution documents
Strong OCR and grounding benchmarks
Open weights under DeepSeek community licence

Limitations

MoE inference tooling less mature than dense models
Limited video support versus Qwen2-VL
Primary language coverage biased toward English + Chinese
Smaller ecosystem of downstream fine-tunes

Use cases

Document and receipt OCR at scale
Visual grounding — bounding-box and point outputs
Chart, table, and diagram extraction
Cost-sensitive VLM inference via MoE sparsity

Benchmarks

Benchmark	Score	As of
DocVQA (test)	≈93%	2024-12
OCRBench	≈811	2024-12
MMBench-EN	≈81%	2024-12

Frequently asked questions

What is DeepSeek-VL2?

DeepSeek-VL2 is an open mixture-of-experts vision-language model family (Tiny / Small / base) released by DeepSeek in December 2024, with strong OCR and grounding performance.

Why MoE for a VLM?

MoE lets the model be parameter-rich (up to 27B total) while activating only a few billion parameters per token, giving better quality per inference FLOP on long image+text sequences.

Is DeepSeek-VL2 open source?

Yes — weights are on Hugging Face under the DeepSeek community licence, usable for research and most commercial applications.

Sources

DeepSeek-VL2 paper (arXiv) — accessed 2026-04-20
DeepSeek-VL2 on Hugging Face — accessed 2026-04-20