Gemma 3 4B vs LLaVA-1.5
We don't benchmark Gemma 3 4B or LLaVA-1.5 on Vision Evals. Gemma 3 4B is open-weight under Custom. LLaVA-1.5 is open-weight under Custom.
Compare Gemma 3 4B vs LLaVA-1.5 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Gemma 3 4B vs LLaVA-1.5 Comparison Table
Pricing updated November 4, 2026
| Property | Gemma 3 4B | LLaVA-1.5 |
|---|---|---|
| Organization | Microsoft | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Oct 2023 |
| Context Window | 128K | — |
| Parameters | 4B | 7B, 13B |
| License | Custom | Custom |
| Commercial use | See license terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | No published price |
| Output $/1M | $0.100 | No published price |
| Vision Tasks | ||
| Visual Question Answering | Demo | Supported |
| Captioning | Demo | Not listed |
| Chart Question Answering | Supported | Not listed |
| Classification | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| OCR | Demo | Not listed |
Both support 1 more task | ||
| Model Features | ||
| Foundation Vision | Supported | Not listed |
Both support 2 more features | ||
Gemma 3 4B vs LLaVA-1.5: Overview
Gemma 3 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.
The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.
LLaVA-1.5 is an open-source large multimodal model released in October 2023 by researchers at the University of Wisconsin-Madison and Microsoft Research. It builds on the original LLaVA architecture by introducing targeted refinements: switching the vision encoder to CLIP-ViT-L at 336-pixel resolution, replacing the projection layer with a two-layer MLP, and adding academic-task-oriented visual question answering data with response formatting prompts during training. These modifications achieve state-of-the-art performance across 11 benchmarks at release, with training completing in approximately one day on a single 8-A100 node.
The model accepts an image paired with a text prompt and generates natural language responses, supporting visual question answering, image captioning, and open-ended visual conversation. LLaVA-1.5 is available in 7B and 13B parameter variants built on the Vicuna language model, and is distributed under the Llama 2 Community License due to its Llama-2-based foundation. The original LLaVA paper was presented as an oral at NeurIPS 2023. Subsequent releases in the series (LLaVA-NeXT (LLaVA-1.6), LLaVA-NeXT-Video, and LLaVA-OneVision) are separate models with their own release pages and build on this foundation with expanded OCR, video, and multi-image capabilities.