Results-driven Machine Learning Engineer with 7+ years of experience leading teams and delivering advanced AI solutions that increased revenue by up to 40%. Proven track record in enhancing business performance through consultancy and expertise in NLP, Computer Vision, LLM models and end-to-end ML pipelines. Skilled in managing critical situations and collaborating with cross-functional teams to implement scalable, impactful solutions. Kaggle Grandmaster and top performer in global competitions, dedicated to staying at the forefront of AI advancements.
After diving into the latest benchmark results, it’s clear that Meta’s new Llama 4 lineup (Maverick, Scout, and Behemoth) is no joke.
Here are a few standout highlights🔍:
Llama 4 Maverick hits the sweet spot between cost and performance - Outperforms GPT-4o in image tasks like ChartQA (90.0 vs 85.7) and DocVQA (94.4 vs 92.8) - Beats others in MathVista and MMLU Pro too and at a fraction of the cost ($0.19–$0.49 vs $4.38 🤯)
Llama 4 Scout is lean, cost-efficient, and surprisingly capable - Strong performance across image and language tasks (e.g. ChartQA: 88.8, DocVQA: 94.4) - More affordable than most competitors and still beats out larger models like Gemini 2.0 Flash-Lite
Llama 4 Behemoth is the heavy hitter. - Tops the charts in LiveCodeBench (49.4), MATH-500 (95.0), and MMLU Pro (82.2) - Even edges out Claude 3 Sonnet and Gemini 2 Pro in multiple areas
Meta didn’t just show up, they delivered across multimodal, coding, reasoning, and multilingual benchmarks.
And honestly? Seeing this level of performance, especially at lower inference costs, is a big deal for anyone building on LLMs.
Curious to see how these models do in real-world apps next.
