Live technical benchmark comparing token limits, input/output API pricing, and modalities.
openai/gpt-4o
meta-llama/llama-4-scout
The primary differentiator in developer workflow design is context retention capacity. OpenAI: GPT-4o offers a context window of 128,000 tokens, while Meta: Llama 4 Scout supports up to 10,000,000 tokens.
This gives Meta: Llama 4 Scout a substantial advantage of 78x larger prompt processing capacity, making it the ideal choice for loading massive codebases, long runbooks, or extensive research data.
Evaluating pricing metrics is critical for running high-frequency background cron workflows.For input queries, OpenAI: GPT-4o costs $2.50 per 1M tokens, compared to $0.10 per 1M tokens for Meta: Llama 4 Scout.
Meta: Llama 4 Scout is the more cost-effective choice for input prompts, yielding savings of 96% compared to OpenAI: GPT-4o. Similarly, output generations are cheaper on Meta: Llama 4 Scout ($0.30 vs $10.00), representing a savings of 97% on completion tokens.
Meta: Llama 4 Scout is better for large document parsing due to its larger context capacity of 10,000,000 tokens, enabling it to fit approximately 78x more content than OpenAI: GPT-4o in a single prompt.
Meta: Llama 4 Scout is more budget-friendly for prompt inputs, costing $0.10 per million tokens (a saving of 96%). For output completion tokens, Meta: Llama 4 Scout is more cost-effective ($0.30 per 1M tokens).
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