InternVL2 and LLaVA are two of the most widely benchmarked open-source vision-language models, but they represent different design philosophies and perform differently across task categories. LLaVA (Large Language and Vision Assistant) established the blueprint for efficient multimodal models through its simple visual instruction tuning approach. InternVL2 is a more recent series from Shanghai AI Lab that pushes performance significantly higher, particularly on document understanding and high-resolution tasks, at the cost of higher computational requirements. Choosing between them — or between their various size variants — comes down to your hardware, the specific task types you need to handle, and whether you need the most capable model or the most efficient one.
Architecture Differences
LLaVA’s architecture is deliberately simple: a visual encoder (CLIP ViT), a lightweight projection layer (an MLP that maps visual features to the LLM’s embedding space), and a standard LLM backbone (originally Vicuna, now typically Llama 2/3 or Mistral). The visual encoder and projection layer are kept relatively frozen while the LLM is fine-tuned on visual instruction data. This simplicity makes LLaVA models easy to understand, easy to fine-tune, and computationally efficient — the visual encoding step is fast because CLIP is a small model. InternVL2 takes a more integrated approach: it uses InternViT, a much larger visual encoder (up to 6 billion parameters) trained specifically for detailed visual understanding, combined with the InternLM2 language model backbone. The larger vision encoder is what gives InternVL2 its advantage on fine-grained visual tasks — it captures more visual detail — but also makes it more expensive to run.
Benchmark Performance
On standard multimodal benchmarks as of mid-2026, InternVL2 consistently outperforms LLaVA models at comparable parameter counts. On MMBench (a broad multimodal evaluation), InternVL2-8B scores approximately 79–80% versus LLaVA-1.6-7B at approximately 68–70%. On DocVQA (document understanding), InternVL2-8B reaches approximately 91% versus LLaVA-1.6’s approximately 77% — a substantial gap that reflects the architectural advantage of InternVL’s larger, higher-resolution visual processing. On OCRBench (text reading from images), InternVL2 is again significantly ahead. Where the gap narrows is on tasks that primarily require conversational ability and general scene understanding rather than fine detail — here LLaVA models, backed by strong LLM backbones, remain competitive. The practical implication: if your use case involves documents, charts, screenshots, or text in images, InternVL2 is the stronger choice by a meaningful margin.
Resolution Handling
One of InternVL2’s key architectural advantages is dynamic high-resolution processing. LLaVA 1.6 introduced a tiling approach (splitting high-resolution images into tiles) that improved over its predecessor’s fixed low resolution, but InternVL2 takes this further with a more sophisticated dynamic resolution system that adapts the number of tiles to the image content and size. For a dense document image with small text, InternVL2 allocates more visual tokens to capture fine detail; for a simple scene description, fewer tokens suffice. This adaptive allocation means InternVL2 is both more accurate on high-resolution content and more efficient than a naive fixed-high-resolution approach. For applications processing document scans, receipts, technical diagrams, or any image where small details matter, this resolution advantage translates directly to accuracy.
Figure 1 — InternVL2 vs LLaVA: benchmark comparison at 7–8B scale
Size Variants and Hardware Fit
Both model families offer multiple sizes. LLaVA-1.6 is available at 7B and 13B (Llama backbone) and 34B (Yi backbone). At 4-bit quantisation, the 7B model runs on 6–8GB VRAM, the 13B on 10–12GB, and the 34B on 20–22GB. InternVL2 is available at 2B, 4B, 8B, 26B, 40B, and 76B. The 8B model requires approximately 18GB VRAM at 4-bit quantisation — notably more than LLaVA-7B despite similar parameter counts, because InternVL’s larger visual encoder adds VRAM overhead. InternVL2-4B is the sweet spot for constrained hardware: it requires only around 8–10GB VRAM while retaining most of InternVL2-8B’s document and OCR advantages over LLaVA. For tasks that fit in 8–10GB VRAM and need strong document understanding, InternVL2-4B is currently the best option available.
LLaVA Variants Worth Knowing
The LLaVA family has branched significantly. LLaVA-1.5 is the most widely cited in benchmarks but has been superseded. LLaVA-1.6 (also called LLaVA-NeXT) added the tiling approach and improved substantially. LLaVA-NeXT-Video extends the architecture to video understanding. LLaVA-OneVision (released 2024) is the most capable LLaVA-family model, trained on a much larger and more diverse dataset with improved multi-image and video support — it significantly closes the gap with InternVL2 on several benchmarks. If you are evaluating LLaVA against InternVL2, compare against LLaVA-OneVision rather than LLaVA-1.6, as the older models are substantially weaker. LLaVA-OneVision-7B is competitive with InternVL2-8B on scene understanding while maintaining LLaVA’s VRAM efficiency advantage.
Multilingual Performance
InternVL2 has a notable advantage in multilingual visual understanding, particularly for Chinese-language text in images — which is expected given its origin at Shanghai AI Lab. The model handles Chinese OCR and document understanding substantially better than LLaVA models trained primarily on English data. For applications that need to process documents, screenshots, or images containing non-Latin script — Chinese, Japanese, Korean, Arabic — InternVL2’s multilingual visual training is a practical differentiator. LLaVA models can handle Latin-script text in images reliably but struggle with complex non-Latin scripts, especially at small sizes. This multilingual advantage is one more reason InternVL2 is the stronger choice for document processing use cases across diverse languages.
Running Both with Ollama
Both model families are available through Ollama, making local deployment straightforward. Pull commands: ollama pull llava for LLaVA 1.6 7B (default), ollama pull llava:13b for the larger variant, and ollama pull internvl2 for InternVL2 (check the Ollama model library for the current default size). Both support the same image input API — pass base64-encoded images in the message content alongside text. For benchmarking your own use case, running both models on a representative sample of your actual images is more informative than comparing published benchmark numbers, since benchmark composition may not match your specific task distribution.
Which to Choose
The decision is straightforward given the benchmarks. If your primary tasks are document analysis, OCR, chart reading, screenshot understanding, or any task requiring fine-grained text extraction from images — choose InternVL2 (8B if hardware allows, 4B if constrained). If your primary tasks are scene description, visual Q&A about natural images, or conversational image analysis where VRAM is limited and document-level accuracy is not the priority — LLaVA-OneVision-7B is more hardware-efficient while remaining competitive. If you need the best possible open-weights multimodal performance at any hardware cost, InternVL2-76B leads the open-source leaderboards by a significant margin as of mid-2026.
InternVL2’s lead over LLaVA on document and OCR tasks is large enough to be practically decisive for most enterprise use cases. LLaVA’s hardware efficiency and strong LLM backbone keep it relevant for deployment-constrained scenarios and conversational multimodal applications. For any new project, evaluate InternVL2-8B first — if it fits your hardware budget, its accuracy advantages make it the default recommendation for the multimodal task range where open-weights models are now competitive with GPT-4V and Gemini Pro Vision.
Fine-Tuning Considerations
Both architectures support fine-tuning for domain-specific tasks. LLaVA’s simpler architecture makes fine-tuning more accessible: the projection layer and LLM can be fine-tuned with LoRA on a modest GPU setup, and the training pipeline is well-documented with community support. InternVL2 fine-tuning is more involved — the larger visual encoder adds memory requirements — but the InternVL team provides official fine-tuning scripts and the process is well-documented. For domain adaptation tasks like fine-tuning on medical images, industrial defect detection, or custom document formats, both models are viable starting points. LLaVA’s lower fine-tuning cost makes it preferable for iterative experimentation; InternVL2’s stronger base performance means you need less domain-specific data to reach a given quality target, which can offset the higher training cost. The choice depends on how much labelled domain data you have and whether base-model accuracy or fine-tuning efficiency is the binding constraint for your specific adaptation task.
The open-weights multimodal landscape is moving fast — InternVL2 itself superseded InternVL 1.5 within months, and newer architectures are announced regularly. The benchmark comparisons here reflect the mid-2026 state; check the Open VLM Leaderboard on Hugging Face for current rankings before committing to either architecture for a long-lived production system. The architectural principles — larger visual encoders for fine-grained tasks, adaptive resolution tiling, multilingual visual training — are stable trends that will persist across model generations even as specific checkpoint rankings shift.