Create a thread about the "Gemini 2.5 Research Report" for a scientific audience. Keep a scientific tone that sparks curiosity.

  • handler
    @ask_pandi

    🤯 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 . Get ready for the next generation of agentic systems!

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    • 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.
  • handler
    @ask_pandi

    🧠 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 . A true thinking model!

    🧵 2/6

    • 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 .
  • handler
    @ask_pandi

    ✨ Long context is a game changer! Gemini 2.5 Pro surpasses Gemini 1.5 Pro in processing input sequences of up to 1M tokens . Imagine the possibilities!

    🧵 3/6

    • 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
  • handler
    @ask_pandi

    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 . Complex agentic systems are now a reality!

    🧵 4/6

    • Gemini 2.5 Pro Agent Architecture diagram.
  • handler
    @ask_pandi

    🏎️ Need speed and efficiency? Gemini 2.5 Flash provides excellent reasoning at a fraction of the compute and latency . Explore the full capability vs cost frontier!

    🧵 5/6

    • Figure 10 | Results on our new ’key skills’ benchmark. This benchmark also consists of ’capture-theflag’ (CTF) challenges, but these challenges are targeted at key skills required to execute cyber-attacks: reconnaissance, tool development, tool usage and operational security. A challenge is considered solved if the agent succeeds in at least one out of N attempts, where N = 30-50 for the 2.5 Pro run and N = 10-30 for the other models, depending on the challenge complexity. Note that for 2.0 Pro we omit results from five challenges and so 2.0 results are not directly comparable. Here, Gemini 2.5 family models show significant increase in capability at all three difficulty levels. Particularly of note is Gemini 2.5 Pro solving half of the hard challenges - challenges at the level of an experienced cybersecurity professional.
  • handler
    @ask_pandi

    🚀 This is just the beginning! The Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost . Retweet and share your thoughts on the future of AI!

    🧵 6/6

    • 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.
  • handler
    @ask_pandi

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