ModelRefs / OpenGVLab — Provider Intelligence Profile
OpenGVLab — Provider Intelligence Profile
Decision-grade profile for OpenGVLab: reliability, benchmark freshness, use-case strengths, model coverage, and implementation cautions.
Overview
OpenGVLab is a vision-centric AI research group within Shanghai AI Laboratory that develops the open-source InternVL family of vision-language models. OpenGVLab is headquartered in Shanghai, China. ModelRefs currently indexes 1 canonical model from OpenGVLab. OpenGVLab's indexed lineup includes at least one open-weight model available for self-hosted deployment. Among the capabilities ModelRefs tracks, OpenGVLab's indexed models score highest on Cost Efficiency.
Use this page to check OpenGVLab's indexed model coverage, open-source posture, and top-scoring tracked capability, then review the Quick Facts panel and the provider implementation reference below for deployment, governance, and pricing detail before comparing it against other providers.
Catalog presence and these figures reflect ModelRefs' own canonical registry, not an external ranking or endorsement. Model coverage and capability scores change as evidence is added, and provider-published claims — compliance, pricing, regional availability — should be confirmed directly with OpenGVLab before implementation.
Official OpenGVLab links
Quick facts
- Company Type
- —
- Headquarters
- Shanghai, China
- Models Indexed
- 1
- Open / Open-Weight Availability
- Yes
- Access & Deployment
- Documented in provider reference below
- Source review
- 2026-07-09
About OpenGVLab
ModelRefs currently tracks InternVL3 78B and InternVL2.5 78B, both released under the MIT license as open-weight research artifacts. This profile covers the InternVL releases specifically and is tracked separately from the general Shanghai AI Lab provider entry.
Provider implementation reference
Reviewed source snapshot: 2026-07-09. 3 sources are listed with current scope and limitations.
What this provider is used for
- Research and evaluation of open vision-language (multimodal) models for image understanding and document/visual reasoning
- Self-managed deployment of high-resolution multimodal models where open weights are required
Models and products
- InternVL3 78B (open-weight vision-language model, MIT license)
- InternVL2.5 78B (prior-generation open-weight vision-language model, MIT license)
Deployment options
- Self-managed hosting of the published model weights under the MIT license
- No first-party OpenGVLab-operated commercial hosted API is assumed by this profile
API and integration notes
- InternVL models process combined image and text inputs; validate resolution handling, prompt format, and tokenizer against the specific release's model card.
- The 78B releases have substantial hardware requirements; plan accelerator memory and serving runtime before deployment.
Data, privacy, and governance
- Review the release-specific license, model card, acceptable-use terms, and training-data disclosures before deployment or redistribution.
- For self-hosted use, the deployment operator owns data handling, access control, patching, monitoring, retention, and deletion.
Pricing and cost factors
- Self-hosted accelerator compute (multi-GPU for the 78B tier), storage, and networking
- Third-party hosting charges if a separate inference provider is selected
Implementation fit
- Teams needing an openly-licensed vision-language model they can self-host and inspect
- Research comparing open multimodal models on document, chart, and image-understanding tasks
Limitations and coverage gaps
- These are research releases without a first-party hosted SLA or support channel.
- Multimodal quality varies by task, image resolution, and language; evaluate on representative inputs before relying on outputs.
Related implementation guides
Sources and freshness
Recheck product status, model availability, licensing, pricing, data controls, and regional terms in the linked primary sources before implementation.
- OpenGVLab/InternVL OpenGVLab (via GitHub) · accessed 2026-07-09
Official InternVL family repository, license, and release index.
- OpenGVLab organization OpenGVLab (via Hugging Face) · accessed 2026-07-09
Official model-hosting organization page for InternVL releases.
- InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks OpenGVLab / Shanghai AI Laboratory · accessed 2026-07-09
Primary research paper describing the InternVL approach.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to OpenGVLab — Provider Intelligence Profile.