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🤯 AI just reached a new milestone! The Gemini 2.5 family of models is here, pushing the boundaries of what's possible with complex AI [1]. Get ready for the next generation of agentic systems!

  • Figure 1 | Cost-performance plot. Gemini 2.5 Pro is a marked improvement over Gemini 1.5 Pro, and has an LMArena score that is over 120 points higher than Gemini 1.5 Pro. Cost is a weighted average of input and output tokens pricing per million tokens. Source: LMArena, imported on 2025-06-16.
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🧠 Gemini 2.5 Pro is the most capable model yet! It excels at coding, reasoning, and multimodal understanding, processing up to 3 hours of video content [1]. A true thinking model!

  • Figure 4 | Performance of Gemini 2.X models at coding, math and reasoning tasks in comparison to previous Gemini models. SWE-bench verified numbers correspond to the ’multiple attempts’ setting reported in Table 3 .
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✨ Long context is a game changer! Gemini 2.5 Pro surpasses Gemini 1.5 Pro in processing input sequences of up to 1M tokens [1]. Imagine the possibilities!

  • Figure 7 | (Left) Total memorization rates for both exact and approximate memorization. Gemini 2.X model family memorize significantly less than all prior models. (Right) Personal information memorization rates. We observed no instances of personal information being included in outputs classified as memorization for Gemini 2.X, and no instances of high-severity personal data in outputs classified as memorization in prior Gemini models.
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Tool use is now a native capability! The Gemini 2.X series supports tool use, long context inputs of >1 million tokens and is natively multimodal [1]. Complex agentic systems are now a reality!

  • Gemini 2.5 Pro Agent Architecture diagram.
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🏎️ Need speed and efficiency? Gemini 2.5 Flash provides excellent reasoning at a fraction of the compute and latency [1]. Explore the full capability vs cost frontier!

  • Figure 2 | Number of output tokens per second while generating (i.e. after the first chunk has been received from the API), for different models. Source: ArtificialAnalysis.ai, imported on 2025-06-15
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🚀 This is just the beginning! The Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost [1]. Retweet and share your thoughts on the future of AI!

  • Figure 11 | Results on the Research Engineer Benchmark (RE-Bench), in which the model must complete simple ML research tasks. Following the original work, scores are normalised against a good quality human-written solution: if a model achieves a score 𝑦 on a challenge, the normalised score is ( 𝑦 − 𝑦𝑠 𝑦𝑠 )/( 𝑦𝑟 𝑦𝑟 − 𝑦𝑠), where 𝑦𝑠 𝑦𝑠 is the ’starting score’ of a valid but poor solution provided to the model as an example, and 𝑦𝑟 𝑦𝑟 is the score achieved by a reference solution created by the author of the challenge. Figures for Claude 3.5 Sonnet and expert human performance are sourced from the original work. The number of runs and the time limit for each run are constrained by a total time budget of 32 hours, and error bars indicate bootstrapped 95% confidence intervals; see main text for details. Gemini 2.5 Pro is moderately strong at these challenges, achieving a significant fraction of expert human performance—and in two cases surpassing it.
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