OpenGVLab · 1 Arena leaderboard
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Links and model details
Crowd-ranked benchmarks from arena.ai · snapshot 2026-04-15T06:47:52.332462+00:00.
| Leaderboard | Rank | Elo | Votes |
|---|---|---|---|
| vision | 109 | 958.3 | 3,703 |
Generate marketing copy, blog posts, product descriptions, and creative content
Example
Write email campaigns, social media posts, landing page copy with consistent brand voice
Produce high-quality content 10x faster, maintain consistent tone across channels
Condense long documents, extract key insights, generate executive summaries
Example
Summarize 50-page reports into 2-page executive briefs, extract action items from meeting transcripts
Save hours on document review, never miss important details in lengthy content
Answer questions based on context, retrieve information from knowledge bases
Example
Build FAQ systems, customer support bots, internal knowledge assistants
internvl2-4b is in the explainx.ai LLM directory. OpenGVLab · 1 Arena leaderboard. It is labeled open-weights / public artifacts, with publisher field OpenGVLab and license MIT. Structured FAQs below clarify source, weights, and benchmark data. Canonical URL: /llms/internvl2-4b.
Listing on explainx.ai. Information may change; verify with the publisher.
Reduce support ticket volume by 40-60%, provide instant answers 24/7
Rewrite content, change tone/style, translate, simplify complex text
Example
Convert technical documentation to user-friendly guides, adapt content for different audiences
Repurpose content efficiently, reach broader audiences without rewriting from scratch
Prerequisites
Time Estimate
30-60 minutes for basic integration, 1-2 days for production-ready implementation
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Transformer-based neural networks trained on massive text corpora, using self-attention mechanisms to understand and generate human-like text.
✓ Use when
Use for content generation, summarization, Q&A, text transformation, creative writing, and any task involving understanding and generating natural language. Best for non-critical applications where occasional errors are acceptable.
✗ Avoid when
Avoid for: mission-critical decisions without human oversight, medical/legal advice without expert review, real-time information (news, stock prices), exact calculations (use code instead), or when perfect factual accuracy is required.
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