Gemma 3 4B vs LLaVA-1.5
Compare Gemma 3 4B and LLaVA-1.5 side-by-side.
Compare Gemma 3 4B vs LLaVA-1.5 live
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Gemma 3 4B vs LLaVA-1.5 Comparison Table
Evals updated July 10, 2026Pricing updated July 16, 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 | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | |
| Output $/1M | $0.100 | |
| Vision Tasks | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Captioning | Demo | |
| OCR | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
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.