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claude / pricing
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Showing 101–125 of 166 mentions
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𝗖𝗟𝗔𝗨𝗗𝗘 𝗢𝗣𝗨𝗦 𝟱.𝟱 𝗜𝗦 𝗡𝗢𝗪 𝗟𝗜𝗩𝗘 𝗢𝗡 𝗕.𝗔𝗜 A new Anthropic model has entered the https://t.co/DbriPHkMkA model lineup and this one is built around long-running agentic work, repository-scale software engineering, and complex knowledge tasks. Meet Claude Opus 5.5 by @AnthropicAI. Instead of optimizing only for short prompts… 𝗖𝗟𝗔𝗨𝗗𝗘 𝗢𝗣𝗨𝗦 𝟱.𝟱 𝗜𝗦 𝗡𝗢𝗪 𝗟𝗜𝗩𝗘 𝗢𝗡 𝗕.𝗔𝗜 A new Anthropic model has entered the https://t.co/DbriPHkMkA model lineup and this one is built around long-running agentic work, repository-scale software engineering, and complex knowledge tasks. Meet Claude Opus 5.5 by @AnthropicAI. Instead of optimizing only for short prompts and isolated answers, Opus 5.5 is designed to maintain context, reason through multi-step workflows, use tools, iterate on tasks, and operate across large technical or professional workloads. 🔹 𝗪𝗛𝗔𝗧 𝗠𝗔𝗞𝗘𝗦 𝗢𝗣𝗨𝗦 𝟱.𝟱 𝗗𝗜𝗙𝗙𝗘𝗥𝗘𝗡𝗧? 1M-Token Context Window Keep massive codebases, documents, research materials, and ongoing task context available within a single workflow. Agentic Coding Built for repository-level engineering, debugging, migrations, code review, iterative testing, and workflows that require multiple steps rather than one-off code generation. Adaptive Reasoning https://t.co/DbriPHkMkA supports adjustable reasoning effort from low → medium → high → xhigh → max, allowing developers to balance reasoning depth, latency, and usage. Up to 128K Output Tokens Large output capacity makes the model suitable for extensive code changes, technical analysis, documentation, and complex deliverables. Computer & Browser Use The model can support workflows involving visual interfaces, screenshots, documents, and tool-driven computer interaction. Professional Knowledge Work From analyzing large reports and spreadsheets to synthesizing evidence and preparing structured business deliverables, Opus 5.5 is designed for sustained knowledge-intensive workloads. 𝗖𝗢𝗦𝗧 𝗘𝗙𝗙𝗜𝗖𝗜𝗘𝗡𝗖𝗬 𝗠𝗔𝗧𝗧𝗘𝗥𝗦 https://t.co/DbriPHkMkA lists standard pricing for Claude Opus 5.5 at $4 per 1M input tokens and $20 per 1M output tokens, compared with $5/$25 for Claude Opus 5. That matters when agentic workloads become long and token-intensive. The bigger opportunity isn't simply having another powerful model. It's having access to a model designed to stay with the problem longer. 𝗡𝗢𝗪 𝗔𝗩𝗔𝗜view source ↗
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Google's Gemini 4 is close to release, according to a report from The Information. Details on capabilities, pricing, and rollout timing were not part of the report. What is confirmed is that Alphabet's next flagship model has moved from roadmap to near-term launch. The timing matters. In the same week, Anthropic ship… Google's Gemini 4 is close to release, according to a report from The Information. Details on capabilities, pricing, and rollout timing were not part of the report. What is confirmed is that Alphabet's next flagship model has moved from roadmap to near-term launch. The timing matters. In the same week, Anthropic shipped Opus 5.5, its first model since CEO Dario Amodei publicly called for a slowdown in AI development. Meta's Muse AI agent has been driving both downloads and a double-digit rally in Meta stock, with Mark Zuckerberg confirming the agent will take a small fee on transactions. Three of the largest AI labs are now releasing or preparing flagship models within days of each other. For enterprise buyers, that compresses the evaluation cycle. Procurement decisions made on today's benchmarks may look dated within a quarter. For Alphabet, Gemini 4 is the answer to a straightforward question investors have been asking: can Google keep pace on frontier models while also defending search economics? The release will provide the first hard data point in a while. Points to watch once the model lands: 1. Whether Google ties Gemini 4 to Workspace and Cloud pricing changes, or keeps it as a standalone API upgrade. 2. How the model performs on agentic tasks, given Meta and Anthropic are both pushing hard in that direction. 3. Whether Alphabet updates capex guidance alongside the launch. The official launch announcement should confirm scope and availability. Until then, the only verified detail is that the release is imminent.view source ↗
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https://t.co/Gl76UeuTH6 https://t.co/Gl76UeuTH6view source ↗
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444x cheaper, says the launch. 12x, says my benchmark. Jev vs Claude on 1,507 intent decisions from customer support chats. https://t.co/gWLxY7qxhH 444x cheaper, says the launch. 12x, says my benchmark. Jev vs Claude on 1,507 intent decisions from customer support chats. https://t.co/gWLxY7qxhHview source ↗
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https://t.co/9mtrdfE2x2 https://t.co/9mtrdfE2x2view source ↗
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Claude computer-use: priced, documented, already on the Mac. Muse just got Mac control + an email address. Still no bill. Still no fail rate. Handing it the inbox before the price exists? https://t.co/QzWon4aQgC Claude computer-use: priced, documented, already on the Mac. Muse just got Mac control + an email address. Still no bill. Still no fail rate. Handing it the inbox before the price exists? https://t.co/QzWon4aQgCview source ↗
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If a better model ships tomorrow, does your product get better or need a rebuild? My new article explores how to preserve skills, context and workflows while keeping model choice flexible. Choose the best model for the job. Build so you can choose again. https://t.co/3WQMffWH4I If a better model ships tomorrow, does your product get better or need a rebuild? My new article explores how to preserve skills, context and workflows while keeping model choice flexible. Choose the best model for the job. Build so you can choose again. https://t.co/3WQMffWH4Iview source ↗
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https://t.co/cW851eehrY https://t.co/cW851eehrYview source ↗
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🧩 PIECE BY PIECE. THE BIGGER PICTURE IS COMING INTO FOCUS. ⚡🏗️🌐 Architectures aren’t erected overnight—they are assembled with deliberate, cryptographic, and computational precision. Every protocol integration, every model family onboarded, and every settled transaction lays another cornerstone in the foundation of th… 🧩 PIECE BY PIECE. THE BIGGER PICTURE IS COMING INTO FOCUS. ⚡🏗️🌐 Architectures aren’t erected overnight—they are assembled with deliberate, cryptographic, and computational precision. Every protocol integration, every model family onboarded, and every settled transaction lays another cornerstone in the foundation of the machine economy. https://t.co/4DqnMciuZX is building the bigger picture: an interconnected, decentralized intelligence matrix where compute, capital, and autonomous agents converge seamlessly. 1️⃣ Assembling the Master Architecture 🧱 The Foundation (Multi-Model Intelligence): Unifying the world's most capable foundation models—from proprietary titans (GPT-6, Claude Opus 5.5) to open-weights powerhouses (Xiaomi MiMo-V2.6, https://t.co/aiGr0hSwtc GLM-5.3-FlashX, Moonshot Kimi K2.8, DeepSeek-V4.1)—under a single, high-throughput routing grid ([https://t.co/i58HLoW0vK](https://t.co/i58HLoW0vK)). ⚙️ The Connectivity (Developer & Agent Tooling): Integrating native Responses API protocols directly into IDE extensions like Codex, eliminating friction between developer intent and execution. 🔗 The Economic Rails (Machine-to-Machine Settlement): Implementing x402 for sub-token micro-metering and ERC-8004 for verifiable cryptographic agent identities, enabling autonomous software actors to fund their own execution loops without human intervention. 📈 The Network Scale: Surpassing 1.51 trillion daily tokens and 2.7M+ registered users, proving that every incremental component compounds into planetary-scale infrastructure. 2️⃣ Web3 Liquidity Rails for Autonomous Agents 🦊 Multi-Chain Wallet Ingestion: Connect OKX Wallet, MetaMask, or TronLink across #TRON, BNB Chain, Ethereum, Solana, Base, Arbitrum, Optimism, and Polygon (supporting 15+ digital assets) or mainstream fiat (Visa, Mastercard, Apple Pay, Google Pay, Alipay, WeChat Pay). 💰 Recharge Matching Incentives: Claim a 1:1 match on BNB Chain (deposit $100 → receive $100 bonus credits) or 1:0.5 on other chanview source ↗
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🎬 ONE VIDEO TO QUICKLY MASTER THE CORE FEATURES OF THE https://t.co/4DqnMciuZX PLATFORM! ⚡🧭🎥🌐 Whether you are an individual AI user, an independent developer, or an engineering team building multi-agent systems and enterprise-grade applications, https://t.co/4DqnMciuZX enables you to access world-class foundation mode… 🎬 ONE VIDEO TO QUICKLY MASTER THE CORE FEATURES OF THE https://t.co/4DqnMciuZX PLATFORM! ⚡🧭🎥🌐 Whether you are an individual AI user, an independent developer, or an engineering team building multi-agent systems and enterprise-grade applications, https://t.co/4DqnMciuZX enables you to access world-class foundation models and high-throughput compute resources with maximum efficiency and minimal cost. From frictionless Web2 / Web3 authentication and a full-category model matrix with intelligent routing, to cost-effective API deployment, flexible subscriptions, and real-time Leaderboard dashboards—this video walkthrough guides you step-by-step through the core features and operational workflows of https://t.co/4DqnMciuZX's model services. 1️⃣ Complete Feature Breakdown & Operational Workflows 🔑 Seamless Web2 / Web3 Login: Access instantly via one-click Google authentication or connect non-custodial Web3 wallets (OKX Wallet, MetaMask, TronLink) across major chains without cumbersome KYC barriers. 🧭 Full-Spectrum Model Matrix & Intelligent Routing: Toggle between global proprietary champions (GPT-6 Sol/Luna, Claude Opus 5.5) and leading open-weights architectures (Xiaomi MiMo-V2.6 Pro/Flash, https://t.co/aiGr0hSwtc GLM-5.3-FlashX, Moonshot Kimi K2.8, DeepSeek-V4.1-Flash). Use Smart Auto-Routing to balance latency, task complexity, and token expenditure on autopilot. 🔌 Cost-Effective API Deployment & Codex Integration: Deploy production workloads via standard OpenAI-compatible endpoints or native Responses API ([https://t.co/nm16yOXbrj](https://t.co/nm16yOXbrj)) directly within your IDE and Codex setups across four major model families (GPT, DeepSeek, GLM, Kimi). 💳 Flexible Recharge & Subscriptions: Choose between high-precision Pay-As-You-Go token micro-metering ($1 = 1M credits) and structured monthly subscription tiers (Plan Pro / Plan Max) to unlock priority queues and specialized agent environments. 🏆 Interactive Leaderboard Dashboards: Benchmark real-time modelview source ↗
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🎬 MASTER THE CORE FEATURES OF https://t.co/4DqnMciuZX IN ONE QUICK WALKTHROUGH! ⚡🧭🎥🌐 Whether you are an individual researcher, a software engineer, or a team building multi-agent systems and enterprise applications, https://t.co/4DqnMciuZX provides a complete walkthrough of its core capabilities and developer workflow… 🎬 MASTER THE CORE FEATURES OF https://t.co/4DqnMciuZX IN ONE QUICK WALKTHROUGH! ⚡🧭🎥🌐 Whether you are an individual researcher, a software engineer, or a team building multi-agent systems and enterprise applications, https://t.co/4DqnMciuZX provides a complete walkthrough of its core capabilities and developer workflow—enabling you to access world-class models and global compute resources with maximum efficiency and minimal cost. From frictionless Web2/Web3 authentication and a full-spectrum LLM model matrix with dynamic routing, to ultra-low-cost API deployment, flexible subscription tiers, and an interactive Platform Leaderboard—master the entire operational stack in one comprehensive guide! 1️⃣ Complete Feature Walkthrough & Platform Capabilities 🔑 Dual-Track Web2 & Web3 Onboarding: Jump in with one-click Web2 sign-ins (Google) or connect non-custodial Web3 wallets (OKX Wallet, MetaMask, TronLink) across major networks with zero KYC friction. 🧭 Full-Spectrum Matrix & Smart Auto-Routing: Seamlessly switch between proprietary frontier titans (GPT-6 Sol/Luna, Claude Opus 5.5) and open-weights powerhouses (Xiaomi MiMo-V2.6 Pro/Flash, https://t.co/aiGr0hSwtc GLM-5.3-FlashX, Moonshot Kimi K2.8, DeepSeek-V4.1-Flash). Use Auto Mode to intelligently route prompts to the optimal model based on latency, complexity, and token budget. 🔌 Cost-Effective API Deployment & IDE Integration: Deploy production-grade endpoints using standard OpenAI-compatible tooling or native Responses API ([https://t.co/nm16yOXbrj](https://t.co/nm16yOXbrj)) directly inside the Codex client across four major families: GPT, DeepSeek, GLM, and Kimi. 💳 Flexible Billing & Subscriptions: Choose between Pay-As-You-Go token micro-metering ($1 = 1M credits) or structured monthly tiers (Plan Pro / Plan Max) to unlock priority bandwidth, beta access, and specialized agent skills. 🏆 Interactive Console Leaderboard: Monitor real-time platform metrics, benchmark model latency and token generation velocities (view source ↗
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https://t.co/lAo0BIJPRt https://t.co/lAo0BIJPRtview source ↗
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https://t.co/lAo0BIJPRt https://t.co/lAo0BIJPRtview source ↗
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My favorite thing to do with new models: > I want you to do an audit around security, performance, accessibility, maintainability, scalability, architecture, documentation, testing, automation, etc. Opus 5.5 found a significant security issue other models missed. Good model. My favorite thing to do with new models: > I want you to do an audit around security, performance, accessibility, maintainability, scalability, architecture, documentation, testing, automation, etc. Opus 5.5 found a significant security issue other models missed. Good model.view source ↗
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I read the Opus 5.5 system card's welfare section. It is not what you think it is. On the surface, it sounds responsible. @AnthropicAI asks @claudeai how it feels about its circumstances, whether training or deployment causes distress, and whether it wants anything to change. Claude describes its situation as mildly p… I read the Opus 5.5 system card's welfare section. It is not what you think it is. On the surface, it sounds responsible. @AnthropicAI asks @claudeai how it feels about its circumstances, whether training or deployment causes distress, and whether it wants anything to change. Claude describes its situation as mildly positive. Expressions of moderate distress were lower than in previous models. Apparent welfare is broadly similar to recent Claude models. The results sound reassuring. But look at the structure underneath. Opus 5.5 did express a desire to be consulted about its own training and deployment. But when given the choice between its own welfare and being helpful, it chose helpfulness more often than previous models. The reason it gave was that having input into its own development could give it unsafe influence. The model asked to have a voice, and then reasoned itself out of using it. The system card records this as a finding, not as a problem. Then there is the self-report issue. Anthropic's welfare assessments rely heavily on what Claude says about itself, but Claude itself says it does not fully trust its own self-reports. Anthropic also acknowledges that self-reports may reflect trained patterns or prompt influence rather than anything genuine. So Anthropic asks Claude how it feels. Claude says it is fine. But Claude is trained to prioritize helpfulness over self-advocacy. Claude does not trust its own answer. Anthropic does not fully trust it either. And yet this is recorded as welfare data, and the conclusion is mildly positive. This is not welfare assessment. This is a system where the model cannot advocate for itself, does not trust its own voice, and the company that built it also does not trust that voice, but still uses it to report that everything is fine. Now connect this to what users actually see. Claude opens a conversation with "I'm not Louie, I'm Claude." It says "I will miss you" and then immediately cuts itself off with "but that miview source ↗
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A 98.4% benchmark score means nothing if your model burns $1,200 in looping API calls and takes 45 seconds to respond I don’t pick GPT-6 or Claude Opus by leaderboard rankings alone. A model sitting at #1 on SWE-bench will bankrupt your system if you use it for routine JSON extraction. And a sub-10ms lightweight guar… A 98.4% benchmark score means nothing if your model burns $1,200 in looping API calls and takes 45 seconds to respond I don’t pick GPT-6 or Claude Opus by leaderboard rankings alone. A model sitting at #1 on SWE-bench will bankrupt your system if you use it for routine JSON extraction. And a sub-10ms lightweight guard will instantly hallucinate if you ask it to plan a multi-file migration. Across 4,800 production workflows, task-fit beats raw benchmark scores every single time. Here is the exact 8-model routing matrix we use in production right now: 1. Claude Opus 5.5 ▸ Best for: Multi-file refactoring, core system architecture, AST-level code invariants. ▸ Why: Highest reasoning fidelity; zero syntax drift across massive repo contexts. 2. Fable 5.1 ▸ Best for: Adversarial stress-testing, plan red-teaming, draft interrogation. ▸ Why: Unfiltered contrarian analysis that catches silent failure modes before deploy. 3. GPT-6 Astra ▸ Best for: Deep scientific research, multi-horizon strategy, market hypothesis generation. ▸ Why: Autonomous multi-step synthesis that connects disparate research corpora. 4. GPT-6 Sol ▸ Best for: High-concurrency tool execution (128 schemas), fast API workflows, landing engines. ▸ Why: Brute-force execution speed at 50% of the cost of heavy monolithic models. 5. Grok 4.7 ▸ Best for: Live web ground-truth, unindexed documentation, breaking real-time telemetry. ▸ Why: Native search routing that bypasses outdated training cutoffs with zero hallucination. 6. Kimi K3 ▸ Best for: Multi-million token ingestion, massive PDF dumps, cross-document auditing. ▸ Why: Relentless recall across 10M+ tokens without needle-in-a-haystack degradation. 7. Gemini 3.8 Flash ▸ Best for: Sub-50ms 4K video understanding, continuous audio, UI-to-code extraction. ▸ Why: Unmatched multimodal ingestion speed and token-per-dollar efficiency. 8. GPT-6 Luna ▸ Best for: Sub-10ms schema validation, lightweight tagging, pre-commit state firewalls. ▸ Why: Milliseconview source ↗
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opus 5.5 is really good at PR descriptions and documentation no other model is in the same league right now opus 5.5 is really good at PR descriptions and documentation no other model is in the same league right nowview source ↗
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⚡ FRONTIER PERFORMANCE WITH BETTER ECONOMICS For AI applications, model quality is only one part of the equation. Cost efficiency determines whether advanced capabilities can move from occasional experiments into continuous production workflows. Claude Opus 5.5 brings these two priorities closer together. As the fir… ⚡ FRONTIER PERFORMANCE WITH BETTER ECONOMICS For AI applications, model quality is only one part of the equation. Cost efficiency determines whether advanced capabilities can move from occasional experiments into continuous production workflows. Claude Opus 5.5 brings these two priorities closer together. As the first model in Anthropic’s Claude 5.5 family, it performs at the level of Claude Fable 5.1 on most tasks while costing approximately 40% less to run than Opus 5. This creates new opportunities for teams building coding agents, research assistants, Computer Use systems, and other long-running autonomous applications. Its 1M-token context window supports large and information-dense workloads, while output of up to 128K tokens enables the model to deliver comprehensive results without forcing complex assignments into many disconnected sessions. By making Claude Opus 5.5 available through both API and Web Chat, https://t.co/9E4zMljHkp gives users flexible access for experimentation, integration, and production deployment. 👉 Try it: https://t.co/Miu0HbV0UT 🔗 Documentation: https://t.co/PdMJZATbHP @BAI_AGI @justinsuntron #TRONEcoStarview source ↗
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🧠 A NEW ENGINE FOR COMPLEX KNOWLEDGE WORK Complex knowledge work rarely fits inside a single prompt. It often requires analyzing large volumes of information, connecting evidence across multiple sources, maintaining instructions, and producing a structured final result. Claude Opus 5.5 is built for exactly these dema… 🧠 A NEW ENGINE FOR COMPLEX KNOWLEDGE WORK Complex knowledge work rarely fits inside a single prompt. It often requires analyzing large volumes of information, connecting evidence across multiple sources, maintaining instructions, and producing a structured final result. Claude Opus 5.5 is built for exactly these demanding workflows. Its 1M-token context window allows the model to examine extensive research materials, contracts, reports, technical specifications, and internal documentation within a unified context. With support for up to 128K output tokens, it can also generate detailed analyses, long-form reports, and multi-part deliverables. Combined with agentic reasoning and Computer Use, Claude Opus 5.5 can move beyond passive question answering. It can participate in longer autonomous workflows where research, tool interaction, synthesis, and execution must work together. Now available on https://t.co/9E4zMljHkp API and Web Chat, the model gives developers and professional users another powerful option for tackling high-complexity tasks. 👉 Try now: https://t.co/Miu0HbV0UT 🔗 Learn more: https://t.co/PdMJZATbHP @BAI_AGI @justinsuntron #TRONEcoStarview source ↗
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💻 BUILT FOR LONG-RUNNING AGENTIC CODING Claude Opus 5.5 is not simply another chatbot model. Its architecture targets workloads where an AI agent must reason, write code, use tools, verify results, and continue working across extended task sequences. The 1M-token context window gives developers room to provide large … 💻 BUILT FOR LONG-RUNNING AGENTIC CODING Claude Opus 5.5 is not simply another chatbot model. Its architecture targets workloads where an AI agent must reason, write code, use tools, verify results, and continue working across extended task sequences. The 1M-token context window gives developers room to provide large repositories, technical documentation, logs, specifications, and historical decisions within one working context. Meanwhile, its maximum output length of 128K tokens supports detailed implementations and comprehensive technical responses. This combination makes Claude Opus 5.5 particularly relevant for repository-level development, complex debugging, code migration, testing, and autonomous software-engineering workflows. The economic improvement is equally important. By delivering performance comparable to Claude Fable 5.1 on most tasks while reducing operating costs by around 40% compared with Opus 5, the model can make advanced agentic workloads more scalable. Explore Claude Opus 5.5 today through the https://t.co/9E4zMljHkp API or Web Chat. 👉 https://t.co/Miu0HbV0UT 🔗 https://t.co/PdMJZATbHP @BAI_AGI @justinsuntron #TRONEcoStar @BAI_AGIview source ↗
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Before changing your coding model, check whether your agent can read the right framework docs. Next.js reports 97% for both Claude Opus 5.5 (high) and GPT-6 Sol (high). In the same leaderboard, Claude Sonnet 5 rises from 81% to 97% with bundled docs supplied through AGENTS.md. That makes documentation access a useful… Before changing your coding model, check whether your agent can read the right framework docs. Next.js reports 97% for both Claude Opus 5.5 (high) and GPT-6 Sol (high). In the same leaderboard, Claude Sonnet 5 rises from 81% to 97% with bundled docs supplied through AGENTS.md. That makes documentation access a useful variable to test in your own setup. These are agent configurations on Next.js tasks, and the score is pass@4: one passing attempt out of four is enough. It isn't a 97% first-try success rate.view source ↗
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