Gemini 4 Argon Context Window: Limits and Specs

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Google has officially launched Gemini 4 Argon with a full context window of 1,048,576 tokens. The architecture is built to support large-scale enterprise workflows that require substantial amounts of source material to remain active in memory. In practical terms, this 1M-token boundary allows the system to ingest massive software codebases, lengthy financial reports, full legal dossiers, and extended video inputs without partitioning data across multiple external queries.

A critical shift in the architecture concerns generation capacity. Google describes a 1M unified input-output token limit via the Gemini API, highlighting the output ceiling as industry-leading compared to the previous generation limit of 64,000 output tokens. Developer access points show technical distinctions: OpenRouter lists the model with a 1,048,576-token context window alongside a 65,536-token maximum completion per individual request. Under the Gemini API pricing structure, output calculations explicitly include thinking tokens, meaning deep chain-of-thought processing shares output token volume alongside final responses.

Target Workloads and Context Performance

Google positions this massive context window specifically for long, multi-step tasks across complex domains such as software engineering, enterprise knowledge work in legal and finance, and cyber defense. According to Google, the model achieved a 91.7% score on the LVBench long video benchmark, illustrating its ability to retain and track information across visual and textual data streams within its memory limit. Google also reports an 87% score on ERQA embodied reasoning for robotics.

Independent evaluations shed light on how the model behaves when handling heavy compute tasks. In the independent Artificial Analysis Intelligence Index, where Gemini 4 Argon scored 53, the model consumed an average of 62k output tokens per task. For comparison, GPT-6 Astra consumed 27k output tokens per task while achieving the same score of 53. Long-context operations also introduce caching advantages: Google offers a cached input rate of $0.10 per 1M tokens during its introductory window ($0.20 per 1M standard), which reduces repetitive overhead when keeping large reference documents inside the active context window.

More on the model: Gemini 4 Argon hub - prices, availability, benchmarks and FAQ

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