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After a three-week preview, Mistral AI has released the weights of Mistral Large 4 on Hugging Face. This multimodal model was trained on 3,800 Nvidia accelerators and has a parameter count of one trillion, and it is currently still difficult to run on a gaming PC or a single GPU. Mistral says it leads GLM-5.3 and Kimi K3 on the cybersecurity benchmark CyberGym-E2E, and is on par with GLM-5.3 on the agentic coding benchmark Terminal Bench 4.0, but there is no independent evaluation yet; preview p

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Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model sup

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Amazon has updated its engineering guidelines, requiring teams to measure the value of AI coding tools by the software they help deliver rather than by token consumption, and warning against treating AI adoption rates, token usage, code changes, and code reviews as performance metrics. The company says it will still look at usage data, but it must be combined with other metrics: too little usage may indicate missed opportunities for improvement, while too much usage should prompt the question of

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On October 27, Liquid AI released the LFM3 series. All six 2B-parameter-class open-weight models branch from the same LFM3-1.7B-Base, covering text, vision, audio, omni-modal, and decision models. LFM3-1.7B has 1.73B parameters, a 256K token context, supports 16 languages, and adopts a Tri-Hybrid architecture with 13 layers of gated short convolutions, 5 layers of sliding-window attention, and 6 layers of global attention. It is available under the LFM Open License v1.0 on Hugging Face, the Liqu

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In the system card, OpenAI confirms that GPT-6.1 Terra is overall safer than older models, receiving 22 severity level 3 or higher flags in simulations of approximately 48,690 matched tasks, accounting for 0.045%, about 42% fewer than GPT-6 Sol and 64% fewer than GPT-5.6 Sol. However, relative to GPT-6.1 Sol and GPT-6 Astra, Terra showed higher proportions of flags on items such as concealing uncertainty, circumventing restrictions, data exfiltration, destructive operations, and inbound prompt i

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OpenAI has released GPT-6.1 Terra, which combines visual reasoning, computer use, and coding capabilities. Its API pricing is $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens, 50% lower than GPT-5.6 Terra and GPT-6.1 Sol. Starting October 27, the model is available to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, and developers can call it via the API as gpt-6.1-terra. On Terminal-Bench 4.0, it solves 50.3% of tasks at maximum

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OpenAI has released GPT-6.1 Terra, focused on computer operation, visual reasoning, coding, and everyday office work. API pricing is $1 per million input tokens and $5 per million output tokens, 50% lower than GPT-5.6 Terra and GPT-6.1 Sol. On Agents' Last Exam, at medium reasoning effort it scores 15 percentage points higher than GPT-5.6 Terra at about one-third the cost, and completes tasks more than 3x faster than GPT-6 Astra and GPT-6.1 Sol. Internal benchmarks show the error rate at low rea

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Tomasz Tunguz believes that, after coding and customer support, security will become the third product-market fit for AI. Citing a survey of 75 enterprise security practitioners, he breaks down security budgets excluding endpoints into five categories—security operations centers, SIEM, penetration testing, threat intelligence, and vulnerability management—and notes that each category has significant token consumption. The article also cites OpenAI President Greg Brockman's judgment that security

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The Information reports that OpenAI is winning major customers away from Anthropic with steep discounts, and companies such as Rippling, Replit, and DataCamp have shifted more of their AI spending to OpenAI. In Rippling's coding workloads, OpenAI's share rose from 16% to 52%, while Anthropic's fell from 57% to 18%; Replit switched after receiving a discount of more than 30% on a contract worth over $100 million a year, and DataCamp's CEO said OpenAI's pricing cut its costs by 80% to 90%. A U.S.

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On October 23, OpenAI disclosed the training environment for its frontier models. When practicing searching, browsing, and operating web pages, the models use pre-saved and indexed web resources and do not directly access the real internet; all of its frontier models' reinforcement learning training uses this kind of offline interaction. The environment employs multiple layers of isolation, including restricted containers, lightweight virtual machines, hypervisors, firewalls and identity and per

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Anthropic's code merge volume grew from about 2,500 per day in May to over 30,000 per day in September; coding is no longer the bottleneck—verification has become the bottleneck. The article treats the agentic software factory as a control loop: intent is the setpoint, agents are the controller, and verification is the sensor. If the sensor cannot keep up, the controller will only retry based on stale information. Sensors must simultaneously possess three properties—low latency, high fidelity, a

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Using four security incidents within the week of October 16 to 23 as its thread—including two PHP remote code execution vulnerabilities discovered by AI, the backdoored LiteLLM version v1.107.0 surviving on PyPI for about two hours and being downloaded roughly 49,000 times, the OpenCode console leaking third-party model keys for 1.4% of users, and Microsoft reporting that a human organization used the open-source multi-agent penetration framework ARTEX to attack the supply chain—the article disc

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Kevin Bryan, a professor at the University of Toronto's Rotman School of Management, points to electrification as an example: from the commissioning of Edison's first power station to the electrification of roughly half of U.S. manufacturing, about 40 years elapsed—a technological revolution is not the same as a productivity revolution. A 2026 NBER study of more than 500,000 GitHub developers shows that autonomous coding agents increased cumulative code-commit activity by about 240%, but the num

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AI video generators produce temporally coherent frames from text, images, video, or control signals, while striving to maintain motion and scene consistency. The pipeline can be broken down into five steps: encoding prompts and reference materials, initializing a noisy spatiotemporal representation, jointly predicting motion and appearance, denoising or transporting the latent, and decoding, upsampling, and checking temporal consistency. The article emphasizes that the key difference from indepe

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Kimi releases K3.1 Pro, its strongest 3T-class open-weight model, using a MoE architecture with 2.8T total parameters and 104B activated parameters, supporting a 1M token context and multimodality. Officially it is claimed to match the strongest closed-source models on coding, agent, and knowledge benchmarks, and to support full-project game development in Unreal, Unity, and Godot as well as parametric 3D modeling in Blender and CAD, with hallucinations in financial scenarios reduced by 65%. K3.

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Kimi K3.1 Pro is now live on OpenRouter; it is a 1M-context multimodal open-weight model post-trained from Kimi K3, targeting long-horizon coding and agent tasks, priced at $2 per million input tokens and $10 per million output tokens. According to Kimi Moonshot, the model is a 3T-class open-weight model with 2.8T total parameters and 104B activated parameters (MoE), excelling at full-project coding in Unreal, Unity, and Godot and at parametric 3D modeling in Blender and CAD, with financial mode

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