Google Unveils Gemini 3.1 Flash-Lite: Performance Surges, Price Jumps Threefold
Google DeepMind has officially launched the preview of Gemini 3.1 Flash-Lite, introducing the fastest and most cost-efficient model in the Gemini 3 series. Building on the Gemini 2.5 Flash-Lite foundation, this iteration sustains speeds exceeding 360 tokens per second with an average response time of 5.1 seconds, while delivering a substantial upgrade in intelligence. According to the Artificial Analysis Intelligence Index, the model’s score has risen by 12 points to 34, and it has shown strong human preference on the Arena.ai leaderboard with an Elo rating of 1432.

In key areas like multimodal capabilities and scientific reasoning, Gemini 3.1 Flash-Lite excels, scoring 86.9% on the GPQA Diamond test and achieving 76.8% accuracy in the MMMU-Pro benchmark. Its performance surpasses heavyweights such as Claude Opus 4.6 and Kimi K2.5. Notably, the model allows developers to adjust the "depth" of reasoning, enabling flexible adaptation across scenarios ranging from simple translation tasks to complex UI development.

However, these gains in performance and speed come with notable cost adjustments. The price for one million input tokens of Gemini 3.1 Flash-Lite has increased to $0.25, while the output price has jumped from $0.40 to $1.50, nearly tripling previous costs.
This pricing strategy highlights the cost pressures model providers face when balancing fast inference with high-precision logic. With the model now available for testing on Google AI Studio and Vertex AI, the lightweight model market is shifting from simple "low-price competition" to a new era of "high-performance logic accessibility."
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Google DeepMind has officially launched the preview of Gemini 3.1 Flash-Lite, introducing the fastest and most cost-efficient model in the Gemini 3 series. Building on the Gemini 2.5 Flash-Lite foundation, this iteration sustains speeds exceeding 360 tokens per second with an average response time of 5.1 seconds, while delivering a substantial upgrade in intelligence. According to the Artificial Analysis Intelligence Index, the model’s score has risen by 12 points to 34, and it has shown strong human preference on the Arena.ai leaderboard with an Elo rating of 1432.

In key areas like multimodal capabilities and scientific reasoning, Gemini 3.1 Flash-Lite excels, scoring 86.9% on the GPQA Diamond test and achieving 76.8% accuracy in the MMMU-Pro benchmark. Its performance surpasses heavyweights such as Claude Opus 4.6 and Kimi K2.5. Notably, the model allows developers to adjust the "depth" of reasoning, enabling flexible adaptation across scenarios ranging from simple translation tasks to complex UI development.

However, these gains in performance and speed come with notable cost adjustments. The price for one million input tokens of Gemini 3.1 Flash-Lite has increased to $0.25, while the output price has jumped from $0.40 to $1.50, nearly tripling previous costs.
This pricing strategy highlights the cost pressures model providers face when balancing fast inference with high-precision logic. With the model now available for testing on Google AI Studio and Vertex AI, the lightweight model market is shifting from simple "low-price competition" to a new era of "high-performance logic accessibility."
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