Skip to content
Only Models#Safety / alignment
  1. Hugging Face Blog69

    IBM releases LLMON preview models built on Granite 4.1 3B and 8B

    IBM released two experimental preview models, granite-4.1-3b-LLMON-preview and granite-4.1-8b-LLMON-preview, derived from Granite 4.1 base checkpoints and trained to consume and generate LLMON, an LLM-native markup language that marks instructions, data and requirements with reserved special-token delimiters. On the OPI benchmark the models avoid 47% more indirect prompt injection attacks than the best frontier model tested and 95% more than the best open-source model, while performing on par with Granite 4.1 Instruct on standard benchmarks. The release includes a GitHub toolkit with lossless JSON-to-LLMON converters, and the authors note the preview is for exploration, not production.

    Why it matters: IBM trained a preview model on Granite 4.1 that can read and write LLMON tags, using explicit instruction-data separation to reduce the indirect prompt injection success rate on the OPI benchmark to near zero while maintaining general capabilities.

  1. Google DeepMind88

    Google DeepMind releases Gemini 4 Carbon for agentic coding, cybersecurity and scientific reasoning

    Google DeepMind made Gemini 4 Carbon available to developers, enterprises, scientists and consumers, positioning it above Gemini 4 Argon for tasks that need deeper reasoning and more tokens. Carbon ranks #1 on CursorBench 4.0, Perplexity's WANDR and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is priced at $2 per million input tokens and $10 per million output tokens, the same introductory price as Argon. Google also previewed /swarm in Google Antigravity, which turns a single prompt into a coordinated team of agents, and reported that Carbon blocked 99% of attack trajectories in simulated cyberattack testing.

    Why it matters: Google's release notes detail how Gemini 4 Carbon trades higher token usage for deeper reasoning, ranking first on CursorBench 4.0, WANDR and AutomationBench at under half the per-task cost of the next external model.

  1. Mistral AI86

    Mistral releases open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter multimodal model

    Mistral AI released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that entered public preview earlier this month and was trained from scratch in Europe. The company says three additional weeks of reinforcement learning lifted it to the top of open-weight coding, with 41% on Terminal-Bench 4.0 and 48% on the Artificial Analysis Coding Agent Index while using 47% fewer tokens per task than GLM-5.3. It adds Low, Medium and High reasoning modes, scores 51% on the Artificial Analysis Cyber Index, and is priced at $0.70 input, $0.07 cached input and $2.10 output per million tokens under a modified MIT license.

    Why it matters: Mistral opens the weights of its 1-trillion-parameter flagship, pairing coding and defensive-security benchmark gains with three reasoning modes and per-token pricing.

  2. Google DeepMind82

    Google DeepMind announces Gemini 4 Carbon, a frontier model focused on cybersecurity and long-horizon tasks

    Google DeepMind announced Gemini 4 Carbon, its strongest model yet, with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the v5.0 Artificial Analysis Intelligence Index at 59. It scores 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching, and reaches #4 (1768) on WebDev Arena. Carbon launches at the same introductory price as Argon, $2 per million input tokens and $10 per million output tokens, and is first available to a subset of cyber defenders through the Fairwind Program before wider release.

    Why it matters: Gemini 4 Carbon's benchmark results in vulnerability discovery and patching show how frontier models are being positioned for cybersecurity defense work.

You are all caught up