Llama 4 Maverick Instruct

Instruction-tuned variant of Meta's Llama 4 Maverick — Meta's largest open-weightA model where the trained weights are freely downloadable — you can run it yourself without contacting the creator. Llama, Mistral, Qwen, and Gemma are open-weight. Open-weight does not mean open-source: the training data and code often stay private. The license still governs what you can do with the weights, including whether you can use them commercially. release. 17B active / 400B total MoEA model architecture that splits the model into many smaller specialized "expert" networks, only activating a handful per input rather than running the whole model every time. The practical effect: you get the knowledge capacity of a big model with the compute cost of a much smaller one. Mistral Large 3 and Mistral Small 4 are both MoE models. with 128 experts, natively multimodalA model that can handle more than one type of input or output — typically text plus images, sometimes plus audio or video. "GPT-4 Vision" and "Llama 3.2 11B Vision" are multimodal models that accept both text and images. A text-only model is called "unimodal" but nobody uses that term; text-only is the assumed default., 1M context windowThe maximum amount of text the model can "see" at once — prompt plus prior conversation plus any documents you give it. Measured in tokens (which are roughly three-quarters of a word each). A 128K context window is about 96,000 words of input — roughly a 400-page book. Larger context windows let the model work with bigger documents but cost more to run.. Scores 80.5 on MMLUA broad knowledge test covering 57 subjects from law and medicine to mathematics and history. Scores are reported as percentage correct. A score around 85% is strong for a frontier model; above 90% is state-of-the-art. MMLU is probably the most-cited benchmark in AI model comparisons, though it has known weaknesses — models can memorize the questions, and the test reflects a specific cultural and academic context. Pro.