Llama 4 Multimodal Review: Meta's Open-Source Vision Model
Review of Llama 4's multimodal capabilities — how Meta's open-source model compares to GPT-5 and Gemini 3 for vision tasks.
Open-Source Multimodal
Llama 4 is Meta's first natively multimodal open-source model. It processes text and images (audio/video support coming) and can be self-hosted without API costs. For organizations with data privacy requirements, this is a game-changer.
The model comes in 8B, 70B, and 405B parameter variants, offering a range of capability-cost tradeoffs.
Vision Performance
Llama 4 405B achieves: 85% on MMMU (vs Gemini 3 Pro's 91%), 89% on DocVQA (vs Claude 4's 94%), and 82% on ChartQA (vs GPT-5's 87%). These numbers represent 85-90% of flagship performance — remarkable for an open-source model.
The 70B variant retains about 80% of the 405B's vision quality while being significantly more practical to self-host.
Self-Hosting Advantages
Self-hosting Llama 4 means: complete data privacy (nothing leaves your servers), no per-token costs (fixed infrastructure cost), unlimited customization (fine-tuning on your data), and no vendor lock-in.
Hardware requirements: 70B variant needs 2x A100 80GB GPUs. 405B needs 8x A100s or equivalent. The 8B variant runs on a single consumer GPU for development.
Limitations
No native audio or video processing (text + image only currently). Vision quality trails flagship models by 10-15%. Limited tool-use capabilities compared to GPT-5 and Claude 4. Community support is good but commercial support options are limited.
Fine-tuning on domain-specific images (medical, satellite, industrial) can close the quality gap significantly.
Verdict
Llama 4 is the best choice for organizations that need multimodal AI with data sovereignty. For most users, API-based models (GPT-5, Gemini 3 Pro) offer better quality at lower total cost. But for high-volume, privacy-sensitive, or customization-heavy use cases, Llama 4 is excellent.
Score: 8.4/10. Compare with other models on Vincony.com.