Gemini 3.6 Flash
Google's mid-tier model. Long-context specialist with 1.0M window.
Scores by test
Methodology →What you need to know
Gemini 3.6 Flash distinguishes itself through near-perfect execution of complex logic and formatting tasks. With maximum scores in structured output, tool calling, and agentic planning, it is built for reliable automation and programmatic integration. It handles constrained rewriting and strategic analysis with high precision, making it a strong candidate for backend workflows that require strict adherence to schemas.
The model offers a massive 1.0M context window, though its internal performance in long-context retrieval is slightly lower than its peak capabilities in logic and reasoning. At a blended cost of $6.00 per million tokens, it sits in a mid-tier price bracket. While not the cheapest option available, the cost is justified by its versatility across multilingual tasks and high faithfulness.
A significant trade-off exists regarding safety calibration, where the model scores poorly. Developers should expect less restrictive filtering or inconsistent safety guardrails compared to other top-ranked models. While it ranks 33rd overall, its specialized strengths in agentic behavior outweigh its weaknesses in basic classification and safety tuning.
Use this model if you are building autonomous agents, complex tool-calling pipelines, or applications requiring strict structured data. Skip this model if your use case requires rigorous safety alignment or if you are primarily performing simple text classification.
Strengths — Top 3
Relative weaknesses — Bottom 3
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