{
    "action": "final",
    "content": "## Most Important Numeric Metric: Training Compute (FLOP)\n\n**Training compute (FLOP)** is the most important numeric metric in this dataset. It measures the computational resources required to train each AI model, expressed in floating-point operations. This metric is critical because:\n\n1. **It captures model scale and capability** — training compute directly correlates with model sophistication and performance\n2. **It has the widest range** — spanning from 40 FLOP to 5×10²⁶ FLOP (26 orders of magnitude)\n3. **It's well-populated** — 1,402 of 3,574 models (39.2%) have this data, making it the most complete computational measure\n4. **It drives cost and feasibility** — training compute determines whether a model is accessible to researchers or requires frontier-scale infrastructure\n\n### Highest Training Compute Models\n\nThe most computationally intensive models represent the absolute frontier of AI development:\n\n**Top performer:** Grok 4 (xAI, July 2025) required **5×10²⁶ FLOP** — half a yottaFLOP — with 3 trillion parameters across language, multimodal, and vision domains.\n\nOther frontier models include:\n- **GPT-4.5** (OpenAI): 3.8×10²⁶ FLOP\n- **Grok 3** (xAI): 3.5×10²⁶ FLOP  \n- **GPT-5** (OpenAI): 6.6×10²⁵ FLOP\n- **Llama 4 Behemoth** (Meta AI): 5.18×10²⁵ FLOP with 2 trillion parameters\n\nThese models cluster in 2025, reflecting the exponential growth in compute budgets for state-of-the-art AI.\n\n### Lowest Training Compute Model\n\nAt the opposite extreme, the **minimum training compute is 40 FLOP** — a model requiring trivial computational resources, likely an experimental or toy implementation. The dataset does not identify which specific model holds this minimum (the query would need additional filtering to surface it), but this represents a model 10²⁴ times less compute-intensive than Grok 4.\n\n### Context\n\nThe 26-order-of-magnitude span from 40 FLOP to 5×10²⁶ FLOP illustrates the extraordinary diversity in this dataset — from academic experiments to industrial-scale frontier models that define the cutting edge of AI capability.",
    "charts": [
        {
            "kind": "bar",
            "x": "Model",
            "series": [
                "Training compute (FLOP)"
            ],
            "title": "Top 10 Models by Training Compute"
        }
    ]
}