Live technical benchmark comparing token limits, input/output API pricing, and modalities.
google/gemini-3.1-pro
meta-llama/llama-4-maverick
The primary differentiator in developer workflow design is context retention capacity. Gemini 3.1 Pro offers a context window of 2,000,000 tokens, while Meta: Llama 4 Maverick supports up to 1,048,576 tokens.
This gives Gemini 3.1 Pro a substantial advantage of 2x 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, Gemini 3.1 Pro costs $1.25 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 88% compared to Gemini 3.1 Pro. Similarly, output generations are cheaper on Meta: Llama 4 Maverick ($0.60 vs $5.00), representing a savings of 88% on completion tokens.
Gemini 3.1 Pro is better for large document parsing due to its larger context capacity of 2,000,000 tokens, enabling it to fit approximately 2x 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 88%). For output completion tokens, Meta: Llama 4 Maverick is more cost-effective ($0.60 per 1M tokens).
Deploy custom, seat-locked DRM software libraries and SEM runbooks designed specifically for these frontier AI models on AIMD.