OpenAI launches GPT-6.1 Terra, a model that matches GPT-6 Astra's computer operation abilities at one-tenth the token cost, with improved visual reasoning, reduced hallucinations, and stronger coding performance across benchmarks like Agents' Last Exam, MMMU-Pro, and Terminal-Bench.
Anthropic's new Claude Haiku 5.5 surpasses GPT-6 Luna and Chinese rivals on benchmarks while matching Luna's pricing and undercutting DeepSeek-V4.1 Flash, though tokenizer changes increase token usage and API breaking changes require migration effort.
Cua provides a complete open-source computer-use stack with five modules—cross-platform driver, cloud desktops, local macOS/Linux VMs, a specialized decision model, and an evaluation benchmark—enabling end-to-end GUI agent training, evaluation, and deployment without piecing together fragmented tools.
OpenAI's GPT-6.1 Sol delivers near-GPT-6 Astra performance on coding and computer-use benchmarks at one-fifth the cost, with improved factual accuracy and safety, available now for Plus, Pro, Business, Enterprise, and Edu users via ChatGPT Work, Codex, and API.
The author explains how OpenAI's GPT-6 model family with computer-use capabilities and halved pricing eliminates the need for tools like Navicat and reduces Spring to a template, shifting backend developers from code writing to problem definition and model output auditing.
Manus 2.0 introduces a rebuilt agent architecture (Cascade) cutting token use 23.2%, task time 28.2%, and cost 32%, adds Cloud Computer for persistent environments, Automations for event-triggered workflows, Manus Studio with Video Editor and Game Dev, Computer Use for remote control, and launches Cue, a personal agent app with digital identity and team collaboration.
The article analyzes how GPT-6 Astra's Computer-Use capability shifts AI from chat to action, requiring product managers to redesign workflows for agent handoff, establish human-AI collaboration principles (visibility, pause points, takeover), and build vertical data flywheels to train domain-specific agents.
Sebastian Raschka analyzes GPT-6 Astra's breakthroughs in 3D rendering and computer use, explains looped transformer architectures from Universal Transformers to Mixture-of-Recursions, and argues that shorter reasoning traces stem from model capability gains, not recurrent depth obscuring chain-of-thought.
Kyle Jeong analyzes why GPT-6 Astra's computer use is fast — code-based Playwright control and accessibility tree observation — then achieves a 2x speedup by switching to Stagehand's batched action prediction, cutting round-trip latency in remote browser environments.
OpenAI has suspended new subscriptions and upgrades for its $200/month ChatGPT Pro tier after the Astra model's exceptional performance triggered unprecedented demand, exceeding available compute capacity; existing users remain unaffected, but no timeline for resuming sales has been given.
The author reviews GPT-6 Astra's computer-use capability for Blender 3D modeling, Image 2.5's sketch-to-image and template features, ChatGPT's computer history and skill recording via demonstration, and shares quota-management tips for ChatGPT Pro users.
GPT-6 Astra introduces agent capabilities that operate software environments, browse the web, run tests, and maintain cross-window context, shifting AI coding evaluation from token pricing to task completion cost, with benchmarks showing 57.7% on Terminal-Bench 4.0 and 97.6% on FrontierMath Tier 4, plus pricing at $10/$50 per million tokens.
OpenAI's GPT-6 Astra integrates reasoning, computer operation, and continuous execution into a single model, scoring 72.6% on OSWorld 2.0, 57.9% on Terminal-Bench 4.0, and 99.9% on ARC-AGI-3 with a Provider Adapter, while demonstrating autonomous tax filing, CRM updates, code migration, and zero-day vulnerability discovery — all with new cross-context memory and safety boundaries.
The article examines the 2025‑2026 surge of sub‑10B parameter models, outlines edge‑side AI agents, details multimodal interaction layers, and explains how Computer Use enables AI to operate computers, offering a roadmap for integrating these technologies across mobile, desktop, and IoT scenarios.
The article breaks down Codex's three distinct computer‑control surfaces—@Computer, @Chrome, and @Browser—explaining their installation, trigger words, use‑case boundaries, performance trade‑offs, and a priority rule so users can pick the most suitable option for each task.
OpenAI's recent Codex update adds true Computer Use support on Windows, letting the model see the screen, click buttons, type, and control desktop applications, with a simple @Paint or @computer prompt, though it runs in the foreground and requires ChatGPT Plus or higher.
OpenAI’s Codex 26.527 update brings the Computer Use agent to Windows, enabling AI to view the screen, simulate mouse clicks and keyboard input, plus remote control via mobile, while outlining usage scenarios, platform differences, permission requirements, and security safeguards.
OpenAI has expanded Codex beyond code generation, enabling it to see and manipulate Windows graphical interfaces and be remotely orchestrated from the ChatGPT mobile app, signaling a shift toward AI agents that operate within real computer workstreams.
The article examines four recent AI coding tool events—a source‑map leak, a computer‑use preview, an OpenAI plugin, and an Apple AI mis‑push—to argue that the AI race is moving from pure model superiority toward competition over workflows, interfaces, and system‑level integration.
Anthropic’s Claude Code now includes a Computer Use capability that lets the AI directly control macOS applications—writing, compiling, launching, clicking UI elements, debugging visual bugs, and performing end‑to‑end UI tests without any code, while requiring specific macOS permissions and operating in a research preview with several limitations.
Anthropic’s latest Claude update adds true computer‑control, scheduling, and Claude Code Desktop, enabling users to issue a single mobile command that drives desktop applications, runs code, processes files, and automates tasks, effectively turning the model into an autonomous agent that works beyond the chat window.
OpenAI's GPT‑5.4 combines a 1 M‑token context window, native computer‑use, and benchmark‑leading performance—outperforming humans on 83 % of tasks and cutting token usage by 47 %—while showcasing demos that let designers generate games, websites, and 3D assets in a single prompt.
OpenAI's newly released GPT-5.4 integrates reasoning, coding, computer use, and agent tool calls, achieving a 75% success rate on OSWorld-Verified tasks—surpassing the human baseline—while its Tool Search feature reduces agent token consumption by 47% and supports up to 1 million tokens for long‑running workflows.
OpenAI's GPT-5.4 launch introduces three model tiers, a 1 million‑token context window, native computer‑use abilities, higher factual accuracy and a new Tool Search feature, reshaping enterprise AI capabilities and intensifying competition with Anthropic and Google.
Anthropic’s Claude Sonnet 4.6, released on Chinese New Year, boosts computer‑use ability, supports a 1 million‑token context window, adds dynamic web‑search filtering, and improves benchmark scores (OSWorld 72.5%, SWE‑bench 79.6%, GPQA 89.9%) while keeping the same price, earning high praise from industry leaders.
Anthropic released Sonnet 4.6 just 12 days after Opus 4.6, delivering near‑Opus capabilities across coding, computer use, long‑context reasoning, and agent planning with a 1 M‑token window, while keeping the lower Sonnet price, prompting mixed community debate and rapid ecosystem adoption.
EvoCUA, a native computer‑use agent from Meituan, combines a verifiable data‑synthesis engine, a ten‑thousand‑level sandbox infrastructure, and an experience‑driven learning paradigm to overcome data‑scaling and feedback challenges, achieving a 56.7% success rate on the OSWorld benchmark and surpassing previous open‑source models.
The article examines the emerging Computer Use paradigm—LLMs that can see and control a computer screen—detailing its technical foundations, three implementation approaches, performance trade‑offs, and why it could become a dominant design pattern for future AI agents.
