Live technical benchmark comparing token limits, input/output API pricing, and modalities.
openai/gpt-5.6-luna
meta-llama/llama-4-scout
The primary differentiator in developer workflow design is context retention capacity. OpenAI: GPT-5.6 Luna offers a context window of 1,050,000 tokens, while Meta: Llama 4 Scout supports up to 10,000,000 tokens.
This gives Meta: Llama 4 Scout a substantial advantage of 10x 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-5.6 Luna costs $1.00 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 90% compared to OpenAI: GPT-5.6 Luna. Similarly, output generations are cheaper on Meta: Llama 4 Scout ($0.30 vs $6.00), representing a savings of 95% 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 10x more content than OpenAI: GPT-5.6 Luna in a single prompt.
Meta: Llama 4 Scout is more budget-friendly for prompt inputs, costing $0.10 per million tokens (a saving of 90%). For output completion tokens, Meta: Llama 4 Scout is more cost-effective ($0.30 per 1M tokens).
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