XX / Twitter50
◈Bluesky1
listening to
Opus 5.5 model documentation
51
mentions
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+37%
25 positive
20 neutral
6 negative
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Sep 23–28, 2026 · Daily
sources
most discussed
claude / pricing
active days
6
what people keep raising
- claude / pricing 27
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Showing 26–50 of 50 mentions
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X X / Twitter 1w ▲ 2 negative claude / pricingANTHROPIC FLIPPED A SWITCH TODAY: THIRD-PARTY AGENTS CUT OFF WITH NO ANNOUNCEMENT At 3:00 AM Pacific today, Opus 5.5 answered a request arriving through a third-party agent harness, on a paid Claude Max subscription — the arrangement Anthropic's own documentation described as sanctioned as recently as yesterday. At 4:… ANTHROPIC FLIPPED A SWITCH TODAY: THIRD-PARTY AGENTS CUT OFF WITH NO ANNOUNCEMENT At 3:00 AM Pacific today, Opus 5.5 answered a request arriving through a third-party agent harness, on a paid Claude Max subscription — the arrangement Anthropic's own documentation described as sanctioned as recently as yesterday. At 4:46 PM Pacific, that same login, same model, same account, same harness was refused. HTTP 400: "Third-party apps now draw from your extra usage, not your plan limits." No announcement. No changelog. No email. Nothing on Anthropic's developer account about harnesses at all today. WHAT CHANGED The gate now keys on client identity. Requests issued by Claude Code itself go through. Requests from a third-party harness driving that same login do not. We verified it directly: minutes apart, one credential, two paths. Plain Claude Code at the terminal answered. The harness path returned 400. Our measurement: last successful Opus turn 03:00:03 PDT, first refusal 16:46:24 PDT, zero config changes on our side in between. Measured two-day Opus consumption across the whole episode: roughly $3. This was not an account running out of allowance. It was a door closing. It isn't only us. CLIProxyAPI filed the same finding today — non-Claude-Code clients now rejected on subscription accounts, 429s on Opus 5 and 5.5, the identical 400 on Haiku. Hermes opened an "Anthropic 400 error" issue today and already has a fix PR in flight. The third-party agent ecosystem got hit as a class. THE PATTERN IS THE STORY Three times in five months, same surface, three different rules, no notice: April 4, 2026 — Anthropic prohibits third-party harnesses from drawing on subscriptions. Operators are pointed at pay-as-you-go API billing. June 15, 2026 — Anthropic announces a dedicated monthly Agent SDK credit instead: $20 on Pro, $100 on Max 5x, $200 on Max 20x. Non-rollover, billed at API rates, spent before anything else, hard stop when empty unless usage credits are enabled. Theview source ↗
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X X / Twitter 1w ▲ 20 neutralhttps://t.co/KeC9ONLJLl https://t.co/KeC9ONLJLlview source ↗
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X X / Twitter 1w ▲ 0 negative claude / pricing𝗖𝗟𝗔𝗨𝗗𝗘 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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X X / Twitter 1w ▲ 1 neutralhttps://t.co/Gl76UeuTH6 https://t.co/Gl76UeuTH6view source ↗
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X X / Twitter 1w ▲ 22 neutral claude / pricing444x 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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X X / Twitter 1w ▲ 5 positive claude / pricingClaude 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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X X / Twitter 1w ▲ 22 positiveIf 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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X X / Twitter 1w ▲ 0 positive claude / pricing🧩 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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X X / Twitter 1w ▲ 0 positive claude / pricing🎬 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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X X / Twitter 1w ▲ 0 neutral claude / pricing🎬 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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X X / Twitter 1w ▲ 0 neutralhttps://t.co/lAo0BIJPRt https://t.co/lAo0BIJPRtview source ↗
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X X / Twitter 1w ▲ 227 positive models / architectureMy 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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X X / Twitter 1w ▲ 99 negative claude / pricingI 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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X X / Twitter 1w ▲ 23 positive claude / pricingA 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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X X / Twitter 1w ▲ 0 positiveopus 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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X X / Twitter 1w ▲ 1 positive claude / pricing⚡ 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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X X / Twitter 1w ▲ 1 neutral claude / pricing🧠 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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X X / Twitter 1w ▲ 1 positive claude / pricing💻 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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X X / Twitter 1w ▲ 1 positive claude / pricingBefore 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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X X / Twitter 1w ▲ 1 neutralhttps://t.co/F5l9mxlKnc https://t.co/F5l9mxlKncview source ↗
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X X / Twitter 1w ▲ 46 positive claude / pricing🚨 CLAUDE OPUS 5.5 IS NOW LIVE ON https://t.co/8882F71uQS — BUILT FOR THE AGENTIC ERA. 🧠⚡ The first model in Anthropic’s new Claude 5.5 family has arrived on https://t.co/8882F71uQS, targeting demanding agentic coding, autonomous workflows, Computer Use, and complex knowledge work. 🔥 1M-token context window ⚡ Up to 12… 🚨 CLAUDE OPUS 5.5 IS NOW LIVE ON https://t.co/8882F71uQS — BUILT FOR THE AGENTIC ERA. 🧠⚡ The first model in Anthropic’s new Claude 5.5 family has arrived on https://t.co/8882F71uQS, targeting demanding agentic coding, autonomous workflows, Computer Use, and complex knowledge work. 🔥 1M-token context window ⚡ Up to 128K output tokens 🤖 Built for long-running agentic coding 🖥️ Advanced Computer Use & autonomous workflows 💰 ~40% lower running cost than Opus 5, according to the launch details And it’s ready across both https://t.co/8882F71uQS API + Web Chat. Longer context. Deeper workflows. More capable agents. One gateway through https://t.co/8882F71uQS. 🚀 Try Claude Opus 5.5 on https://t.co/8882F71uQS : https://t.co/03dbOApV8F Claude Opus 5.5 Documentation : https://t.co/HjH5iAwREW @justinsuntron @BAI_AGI #TRONEcostarview source ↗
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X X / Twitter 1w ▲ 1 neutral claude / pricingClaude Opus 5.5 arriving on https://t.co/UQWvo7ebXA represents another step in the evolution of AI-assisted software development. The key phrase is “long-running agentic coding.” Traditional coding assistants primarily helped developers generate snippets, explain functions, or debug isolated problems. Agentic coding … Claude Opus 5.5 arriving on https://t.co/UQWvo7ebXA represents another step in the evolution of AI-assisted software development. The key phrase is “long-running agentic coding.” Traditional coding assistants primarily helped developers generate snippets, explain functions, or debug isolated problems. Agentic coding introduces a much broader ambition: AI systems that can reason across repositories, execute multi-step development workflows, maintain context, and continue working toward larger engineering objectives. That requires a different class of model. A 1M-token context window can provide room for substantial project information, while up to 128K output tokens expands the scope of what can be produced within a single workflow. The real transformation may not be AI writing more code. It may be AI becoming capable of participating across much larger portions of the software development lifecycle—from understanding requirements and architecture to implementation, debugging, documentation, and iteration. Claude Opus 5.5 on https://t.co/UQWvo7ebXA gives developers another environment to explore that transition. @justinsuntron #TRONEcoStar @BAI_AGIview source ↗
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X X / Twitter 1w ▲ 1 positive pricing / anthropic☕ Today's AI news in five lines. • Anthropic & OpenAI cut prices • Trump wants AI renamed 'superintelligence' • Snorkel AI raises $350m for training data • Verda is Europe's newest AI cloud unicorn • Nearly half of UK youth trust AI over people https://t.co/zFLZXvGgto ☕ Today's AI news in five lines. • Anthropic & OpenAI cut prices • Trump wants AI renamed 'superintelligence' • Snorkel AI raises $350m for training data • Verda is Europe's newest AI cloud unicorn • Nearly half of UK youth trust AI over people https://t.co/zFLZXvGgtoview source ↗
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X X / Twitter 1w ▲ 2 neutral claude / pricingMorning Update Anthropic launches Claude Opus 5.5 for long-running agentic work Anthropic released Claude Opus 5.5, its new flagship model for long-running agentic coding and knowledge work. Official documentation prices it at $4 per million input tokens and $20 per million output tokens. Anthropic says the model impr… Morning Update Anthropic launches Claude Opus 5.5 for long-running agentic work Anthropic released Claude Opus 5.5, its new flagship model for long-running agentic coding and knowledge work. Official documentation prices it at $4 per million input tokens and $20 per million output tokens. Anthropic says the model improves capability while reducing the compute needed for typical Opus workloads. OpenAI adds GPT-6 Sol and Luna as faster, more affordable GPT-6 models OpenAI launched GPT-6 Sol and GPT-6 Luna, extending the GPT-6 family beyond Astra. OpenAI says the two models bring Astra-generation gains in professional work, factuality, coding, computer use and alignment into faster, more affordable options. Sol is positioned specifically for complex coding and agentic workflows. GitHub pushes Copilot further toward agentic engineering GitHub used its first Copilot Day to preview more autonomous developer workflows. One notable research preview is Hydrofusion, designed to choose the right model for a task automatically instead of making developers decide each time. GitHub also said monthly commits have more than doubled since April, from 1.4 billion to 2.9 billion. Apple’s new Mac hardware puts more emphasis on local AI Early M5 Ultra and M6 Mac mini testing points to another jump in on-device AI performance. Apple-quoted figures for M5 Ultra include up to 4.3× faster local AI than the prior generation, while the new Mac mini increases CPU and GPU core counts versus the previous base model. Real-world gains will vary by workload. Xiaomi’s MiMo v2.6 adds another open-model contender MiMo v2.6 is being presented as a highly open AI release, with model weights and training code said to be available. Early claims put it near much larger closed models on some coding and agent tasks, but those comparisons remain unconfirmed. If the results hold up, it would add more pressure to the AI price-performance race. Want to find out more about any of these topics? Search them oview source ↗
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X X / Twitter 1w ▲ 1 positive claude / pricing𝗖𝗟𝗔𝗨𝗗𝗘 𝗢𝗣𝗨𝗦 𝟱.𝟱 𝗜𝗦 𝗡𝗢𝗪 𝗟𝗜𝗩𝗘 𝗢𝗡 𝗕.𝗔𝗜: 𝗪𝗛𝗔𝗧 𝟭𝗠 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 𝗠𝗘𝗔𝗡𝗦 𝗙𝗢𝗥 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪𝗦 🤖 https://t.co/h8toKyKnAQ has added Claude Opus 5.5 from @AnthropicAI to both its Web Chat and API. The important part isn't simply another model appearing in a model list. It is what the model's capabilities enable developers and users… 𝗖𝗟𝗔𝗨𝗗𝗘 𝗢𝗣𝗨𝗦 𝟱.𝟱 𝗜𝗦 𝗡𝗢𝗪 𝗟𝗜𝗩𝗘 𝗢𝗡 𝗕.𝗔𝗜: 𝗪𝗛𝗔𝗧 𝟭𝗠 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 𝗠𝗘𝗔𝗡𝗦 𝗙𝗢𝗥 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪𝗦 🤖 https://t.co/h8toKyKnAQ has added Claude Opus 5.5 from @AnthropicAI to both its Web Chat and API. The important part isn't simply another model appearing in a model list. It is what the model's capabilities enable developers and users to do. Claude Opus 5.5 is designed for long-running agentic coding and complex knowledge work, where the model may need to reason across large amounts of information, maintain context and execute multi-step workflows. 𝗧𝗛𝗘 𝟭𝗠-𝗧𝗢𝗞𝗘𝗡 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 𝗟𝗔𝗬𝗘𝗥 A 1M-token context window changes how large tasks can be approached. Instead of repeatedly splitting a large project into smaller conversations, developers can potentially provide substantially more context within a single workflow. Think about: → Large codebases → Extensive technical documentation → Long research materials → Multi-file software projects → Complex agent instructions Context does not automatically make an AI system correct. But more usable context can reduce the need to constantly remove information from a workflow. 𝗧𝗛𝗘 𝟭𝟮𝟴𝗞 𝗢𝗨𝗧𝗣𝗨𝗧 𝗟𝗔𝗬𝗘𝗥 The model also supports up to 128K output tokens. That matters for tasks where the result itself can be extensive — from detailed code generation and analysis to long-running agent workflows. The broader shift is from: Ask → Answer toward: Plan → Reason → Execute → Review → Continue That is much closer to how agentic systems operate. 𝗖𝗢𝗦𝗧 𝗜𝗦 𝗣𝗔𝗥𝗧 𝗢𝗙 𝗧𝗛𝗘 𝗧ECHNOLOGY According to the announcement, Claude Opus 5.5 delivers capabilities comparable to Claude Fable 5.1 on most tasks while costing roughly 40% less to run than Opus 5. If those economics hold in practical workloads, the implication is significant for developers running repeated or long-context operations. Better capability is useful. Better capability at a sustainable cost is even more important when building production systems. 𝗪𝗛𝗘𝗥𝗘 𝗕.𝗔𝗜 𝗙𝗜𝗧𝗦 𝗜𝗡 https://t.co/h8toKyKnAQ's value isview source ↗