El nuevo modelo Gemini 4 Carbon de Google DeepMind lidera en programación y defensa cibernética, pero sigue limitado a expertos en seguridad verificados, no al público.
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Google DeepMind has introduced Gemini 4 Carbon, its strongest model to date. Gemini 4 Carbon is a frontier model from Google built for long-horizon agentic coding and cybersecurity defense. It ranks #1 on the updated v5.0 Artificial Analysis Intelligence Index with a score of 59. For developers, the catch is access. Carbon starts with a vetted group of cyber defenders, not the public API. TL;DR Size: Parameter count and context window not disclosed. Its predecessor, Gemini 4 Argon, has a 1,048,576-token context. Runs on: Google-hosted only. No open weights, no self-hosting, no quantized variants. Performance: Leads 15 of 17 published benchmarks versus Argon, GPT-6 Astra, Opus 5.5 and Fable 5.1. Best: 96.2% BFS F1 on GraphWalks (256K to 1M tokens), versus 84.2% for Argon. Worst: 81.5% on OSWorld-2.1, trailing Claude Opus 5.5 at 87.2%. Bottom line: Best: state-of-the-art vulnerability finding, exploitation and patching. Worst: you probably cannot call it yet. What is Gemini 4 Carbon? Carbon is the second Gemini 4 model, following Gemini 4 Argon. Google team states that the model targets hard, long-running engineering work. Internal teams saw Carbon run for days on single tasks. Examples include root-causing and patching subtle bugs across large codebases and low-level systems. Google also tuned Carbon for full-stack web development and multimodal understanding. On WebDev Arena, Carbon gained 89 Elo points to reach #4 at 1769. How does Gemini 4 Carbon perform on benchmarks? Google published a 17-row comparison in its launch post. Carbon was run at the highest thinking setting. Most rival numbers are self-reported by each provider, per Google’s evaluation methodology. Agentic coding: Carbon scores 65.2% on Terminal-Bench 4.0 (AA), ahead of Opus 5.5 at 59.6%. It hits 9.6% on SWE-sweep, narrowly ahead of GPT-6 Astra at 9.5%. On FrontierSWE, GPT-6 Astra leads at 65.5% versus Carbon’s 61.2%. Computer use and knowledge work: Carbon reaches 77.4% on Agents’ Last
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MiniMax ha publicado como código abierto su MCode Stack interno, ofreciendo a los desarrolladores habilidades de agente prácticas e instalables que automatizan flujos de trabajo de codificación complejos.
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