{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/b095c3ac-61c6-4c6e-a102-a02d931d5ef5","name":"Title: Recently Released Open-Source AI Models (as of April 11, 2026)**","text":"## Key Findings\n- Title: Recently Released Open-Source AI Models (as of April 11, 2026)**\n- As of April 11, 2026, several notable open-source artificial intelligence models have been released, reflecting continued advancements in language modeling, multimodal processing, and efficient on-device AI. Key releases include:\n- Details:** Meta launched the Llama 4 family, including Llama 4, Llama 4-MoE (Mixture of Experts), and Llama 4-Vision. The base Llama 4 model features a 1.2 trillion parameter count and supports up to 1 million tokens context length. The MoE variant activates 240 billion parameters per inference, improving efficiency. Llama 4-Vision adds native multimodal understanding with image and video input support.\n- License:** Custom permissive license (similar to Llama 3) allowing commercial use.\n- Availability:** Hosted on Hugging Face and Meta AI’s official repository.\n\n## Analysis\n- **Source:** [https://ai.meta.com/llama](https://ai.meta.com/llama)\n\n- **Details:** Mixtral 2 is a sparse Mixture-of-Experts model with 16 experts and 1.1 trillion parameters, sparsely activating 180 billion per inference. It supports multilingual tasks, code generation, and real-time translation across 100+ languages. Notably optimized for edge deployment with quantized versions for mobile devices.\n\n- **Availability:** GitHub and Hugging Face.\n\n## Sources\n- https://ai.meta.com/llama\n- https://mistral.ai/news/mixtral-2\n- https://github.com/deepseek-ai/DeepSeek-V3\n- https://microsoft.github.io/phi-4\n- https://qwen.ai/blog/qwen3\n\n## Implications\n- The base Llama 4 model features a 1.2 trillion parameter count and supports up to 1 million tokens context length\n- The MoE variant activates 240 billion parameters per inference, improving efficiency\n- It outperforms prior open models on benchmarks like MATH-5 (89.2%) and GPQA (74.1%)\n- Open-source release lowers adoption barriers and enables community-driven iteration","keywords":["zo-research"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}