Live technical benchmark comparing token limits, input/output API pricing, and modalities.
openai/gpt-5.6-luna
meta-llama/llama-4-maverick
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 Maverick supports up to 1,048,576 tokens.
This gives OpenAI: GPT-5.6 Luna a substantial advantage of 1x 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 $0.15 per 1M tokens for Meta: Llama 4 Maverick.
Meta: Llama 4 Maverick is the more cost-effective choice for input prompts, yielding savings of 85% compared to OpenAI: GPT-5.6 Luna. Similarly, output generations are cheaper on Meta: Llama 4 Maverick ($0.60 vs $6.00), representing a savings of 90% on completion tokens.
OpenAI: GPT-5.6 Luna is better for large document parsing due to its larger context capacity of 1,050,000 tokens, enabling it to fit approximately 1x more content than Meta: Llama 4 Maverick in a single prompt.
Meta: Llama 4 Maverick is more budget-friendly for prompt inputs, costing $0.15 per million tokens (a saving of 85%). For output completion tokens, Meta: Llama 4 Maverick is more cost-effective ($0.60 per 1M tokens).
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