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  2. Google DeepMind

    Google DeepMind releases Gemini 3.8 Flash and 3.8 Flash Cyber

    Google DeepMind has released Gemini 3.8 Flash and Gemini 3.8 Flash Cyber, aimed at trusted defenders, calling them its most capable reasoning and coding models to date, with speed and cost comparable to 3.7 Flash. 3.8 Flash surpasses most larger frontier models on DeepSWE v1.1, scores 54.9% on HLE-Verified, and performs more reasoning steps and iteratively calls tools on complex tasks. 3.8 Flash Cyber reaches frontier-level performance on CyberGym, with an internal vulnerability-discovery benchmark success rate exceeding 70% across 20 programming languages, and a CWE-Bench patch pass@1 of 47.2%.

    Google DeepMind has released Gemini 3.8 Flash and Gemini 3.8 Flash Cyber, aimed at trusted defenders, calling them its most capable reasoning and coding models to date, with speed and cost comparable to 3.7 Flash. 3.8 Flash surpasses most larger frontier models on DeepSWE v1.1, scores 54.9% on HLE-Verified, and performs more reasoning steps and iteratively calls tools on complex tasks. 3.8 Flash Cyber reaches frontier-level performance on CyberGym, with an internal vulnerability-discovery benchm

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    #lab#Model releases#Agents#English text is being prepared.

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  2. Ant Ling: Developer BlogSelected

    Ant Ling Open-Sources Ling-3.0-tiny-base and Ling-3.0-flash-base

    Ant Ling has officially open-sourced two Base Models, Ling-3.0-tiny-base and Ling-3.0-flash-base, and simultaneously released the weights from three stages—pre-training, mid-training, and WSM merging—for a total of 6 checkpoints. Ling-3.0-tiny-base has 7.9B total parameters and 1.3B activated parameters, while Ling-3.0-flash-base has 124B total parameters and 5.1B activated parameters; both can be used for continued pre-training, supervised fine-tuning, preference optimization, reinforcement learning post-training, and distillation. The official notes state that the Base Models have not completed instruction alignment and are not recommended for direct deployment as end-user-facing chat services without post-training.

    Ant Ling has officially open-sourced two Base Models, Ling-3.0-tiny-base and Ling-3.0-flash-base, and simultaneously released the weights from three stages—pre-training, mid-training, and WSM merging—for a total of 6 checkpoints. Ling-3.0-tiny-base has 7.9B total parameters and 1.3B activated parameters, while Ling-3.0-flash-base has 124B total parameters and 5.1B activated parameters; both can be used for continued pre-training, supervised fine-tuning, preference optimization, reinforcement lea

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    #engineering#Model releases#Open-source ecosystem#English text is being prepared.

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