WeVisOne-VL 4B
A solid pick for self-hosting and full data control.
Overview
WeVisOne-VL is a 4.8B-parameter vision-language model from Tencent, built on Qwen3-VL with a lightweight 9.6M-parameter pixel decoder that unifies object detection, OCR, counting, pointing and referring segmentation in a single pass. It performs well on structured visual perception but is limited to one image per turn, video grids are rejected by the pixel decoder, and it produces no explicit reasoning trace. Released under Apache 2.0, it can be self-hosted so that all data stays on the user's own infrastructure, regardless of the vendor's country.
Skill profile
Not disclosed
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- your data must stay in the EU
Ideal use cases
- Document OCR extraction
- Open-vocabulary object detection
- Referring image segmentation
- Automated object counting
- Product image matting
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/wevisone-vl-4b)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
2/8 documented · 0/8 primary sources
| API pricingopen weights, no API pricing | missing | |
| Context window | missing | |
| Arena Elo | missing | |
| Benchmarks (MMLU, GPQA, SWE-bench…) | missing | |
| Licence | nAIvigate assessment | |
| Data & complianceself-hosted: derived | derived | |
| Adoption (Hugging Face) | missing | |
| Hardware requirements (quantizations)computed from model size | missing |
Run it locally
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve tencent/WeVisOne-VL-4B \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |