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claude / pricing
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Showing 1–24 of 24 mentions
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X X / Twitter 4d ▲ 2 positive models / architectureYesterday, I did something crazy, deleted 10,000+ lines from my project repo. ▶Majorly all documentation and otherwise redundant tests that models keep making over the time, a deep clean, you can say! I decided to do something about my bloated documentation (Including AGENTS. md) after reading a lot about how latest i… Yesterday, I did something crazy, deleted 10,000+ lines from my project repo. ▶Majorly all documentation and otherwise redundant tests that models keep making over the time, a deep clean, you can say! I decided to do something about my bloated documentation (Including AGENTS. md) after reading a lot about how latest intelligent capability improvement in models lile opus 5.5 and Astra→ that folks have been talking about taking down the 'plan mode' from their harnesses (CRAZYY!!) When I started this project in Google's Antigravity using maybe Gemini 3.5 Flash or something, I created a lot of documentation around the architecture and some nuances using the most intelligeny model of that time: Opus 4.6 at that time in Antigravity, which is still there (Hopeless team maintain or governing the decisions for model selection). For the past two to three months, I've had these documentation trying to gauge and help my models maneuver and understand the priorities I have. But I realized that maybe it is creating a lot of noise and a lot of unnecessary context for the really intelligent models like Astra that I have started using, so that they are not unintentionally restrainted to deliver the value that they possibly can. So I've deleted every documentation (because I am a solo founder, I know exactly they were and what I what in my head) and only kept an agents. md file that contains the most important rules, more like a philosophy of the project. From today, I will be using a freshly installed Codex with slight personal configuration change along with Astra-reformed Pstack. Which is a Codex version of 'PStack by Lauren Tan @poteto ' at SpaceX, and I triple verified my version with Astra adversarial judge to perform a comparison between using Lauren's skills and the new Codex plugin that I've created to see if the inspired plugin that has been created by Astra is performing with the philosophy that Lauren has made PStack with OR not. And so now it's a new time to seeview source ↗
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X X / Twitter 4d ▲ 1 positive claude / pricingCompare Opus 5.5 vs GPT 6 Sol for coding, writing, agents, and marketing. See which AI model delivers better quality, speed, and value. #OpenAI #Claude #GPT6 #Opus5.5 https://t.co/S3UFei4bKe Compare Opus 5.5 vs GPT 6 Sol for coding, writing, agents, and marketing. See which AI model delivers better quality, speed, and value. #OpenAI #Claude #GPT6 #Opus5.5 https://t.co/S3UFei4bKeview source ↗
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X X / Twitter 4d ▲ 0 positive claude / pricing🎙️ Claude Opus 5.5 Is Now Live on https://t.co/He4LVLveJn: Built for Long-Running AI Agents 🚀🤖 1️⃣📢 A New Claude Generation Arrives on https://t.co/He4LVLveJn https://t.co/He4LVLveJn has added Claude Opus 5.5, described as the first model in the new Claude 5.5 family. Its positioning is notably different from a gene… 🎙️ Claude Opus 5.5 Is Now Live on https://t.co/He4LVLveJn: Built for Long-Running AI Agents 🚀🤖 1️⃣📢 A New Claude Generation Arrives on https://t.co/He4LVLveJn https://t.co/He4LVLveJn has added Claude Opus 5.5, described as the first model in the new Claude 5.5 family. Its positioning is notably different from a general-purpose chatbot. The model is designed around long-running agentic coding and complex knowledge work, targeting workflows that require sustained reasoning and execution. 2️⃣🤖 Agentic Coding Takes Center Stage The key theme is autonomy. Rather than handling only isolated coding prompts, Claude Opus 5.5 is designed for workflows where an AI agent can work through multiple stages of a task. That makes it relevant to larger software projects, debugging cycles, codebase analysis, and other development processes that cannot be completed effectively through a single prompt. 3️⃣📚 1M-Token Context Window A 1-million-token context window gives the model substantial room to work with large amounts of information in one workflow. For developers, that can be useful when dealing with: 💻 Large codebases 📄 Extensive documentation 🧩 Multiple project files 🔍 Long-running research tasks 🤖 Agent memory and context Large context does not automatically guarantee better output, but it expands the amount of information an agent can potentially consider at once. 4️⃣📝 Up to 128K Output Tokens The model also supports up to 128K output tokens. Combined with the large context window, this is particularly relevant for tasks requiring lengthy reasoning or substantial generated output rather than short conversational responses. The architecture is therefore aimed at workloads where both input and output can become unusually large. 5️⃣💰 Lower Running Cost Than Opus 5 https://t.co/He4LVLveJn states that Claude Opus 5.5 can deliver comparable performance to Claude Fable 5.1 on most tasks while costing approximately 40% less to run than Opus 5. If that cost relationshipview source ↗
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X X / Twitter 4d ▲ 2 positive models / architectureThis is why LLMS will NEVER be conscious!!! We nailed the toughest function of all. Full character swap or just face swap. It was soooo hard to nail. This at low res. Scaling up is easy since it's already managed. But this was so tough; now it's unlimited. And I even added a new function through my main agent that can… This is why LLMS will NEVER be conscious!!! We nailed the toughest function of all. Full character swap or just face swap. It was soooo hard to nail. This at low res. Scaling up is easy since it's already managed. But this was so tough; now it's unlimited. And I even added a new function through my main agent that can sharpen even more I did not ask it to generate a sad face at all. This is a frame of a video. But look at the man's face. Fully consistent with the video frame itself. It was the toughest of all. The first above is the original. Build 7 is live and finished. The docs and test kit are installed on your laptop, and the employees' briefing is updated (attached above). 2 things I have proven. Continuous learning is only possible through client-side harnesses. Otherwise, all compute starts from a generalized training baseline. Every context compaction is every new session. You see it yourself. This was really tough; it looked so fake. In such a childish way. 2. I also proved that a native open-weight TRANSFORMER model is wich my clipping studio tool is built on. It does not come across as sophisticated at all. I swear it comes like the old Will Smith video that went viral. Because it is one of the last truly open weight model that you don't have to pay for a license and wich is not banned in US & EU like some are. Everything after was paid. But I believed I could build it to the next level. I even invented functions genuinely. Like patching things from other professional software tools. Tried many ways to arrive at my goal. But I will probably use this function like never. I do not know why I would do a face swap if I can just have consistent characters. And have nailed the motion physics already in an earlier phase. And emotional expression. But since I have it in my tool as a perfectionist, it did not feel complete. Everything else came together. And even after these images, what you see now. I have built a large range of FUNCTIONALITIES purely forview source ↗
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X X / Twitter 5d ▲ 9 positive claude / pricingClaude Opus 5.5 on xhigh reasoning. Ran all day, 10 hours or so. --- Tools --- I didn't tell Claude to do any of this, it just used the API keys I put in its environment: - Generated images using gpt-image-2.5-flare via API - Generated 3D models using Hunyuan via API, and some from Poly Haven (CC0) - Got the iPhone D… Claude Opus 5.5 on xhigh reasoning. Ran all day, 10 hours or so. --- Tools --- I didn't tell Claude to do any of this, it just used the API keys I put in its environment: - Generated images using gpt-image-2.5-flare via API - Generated 3D models using Hunyuan via API, and some from Poly Haven (CC0) - Got the iPhone Duo model out of Xcode somehow and rigged it in Blender - Did all the motion graphics in Python - Prompted and generated the background music using Lyria 3 Pro via API - Generated the voice clips and sound effects using ElevenLabs via API --- Cost --- Raw API cost would've been ~$265. Actual cost was 10% of the weekly quota (20x Max plan), plus $16 in external API costs. I believe the Max plan gets something like 6x the Pro plan's overall usage, so this should fit in the $20 Pro plan, but you'd hit the 5h limit a few times. Annoying, but you can make it work. Claude usage - Model calls: 1,824 (including 8 review subagents) - Total tokens: 781M - Cache reads: 772M - Cache writes: 7M - Output: 1.5M (0.6M of it thinking) - Cost at Opus 5.5 API prices: ~$233 External APIs - 3D food models (Hunyuan 3D + bake-off, fal): ~$15 - Food photos (OpenAI gpt-image-2.5, ~115 images): free via Codex CLI but ~$14 at API prices - Music, voice, sound effects (Lyria 3, ElevenLabs, fal): ~$3.50 - Image model bake-off (FLUX.2 Pro, Gemini): ~$1 - External subtotal: ~$33 --- Prompt 1 (one-shot app, first draft of video) --- I want you to build an iOS calorie tracker app. It should have a super clean, minimalist design: very design-forward and aesthetic. Sweat all the tiny details and make everything feel super premium. Make every interaction extremely delightful. You can go so far as to write custom Metal shaders, custom UI components, etc., to make everything fluid, unique and delightful. The idea I have here is to make it one unified conversational experience. I should be able to page through different days, each of which is a conversation thread with an agent. Theview source ↗
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X X / Twitter 5d ▲ 4 positive claude / pricing90 seconds on low. 67 minutes on max. Same workout-app request. Different results. What did the extra time buy—and did you need it? My guide to Claude Code’s effort settings: when to iterate fast, when to verify deeply, and what to measure. https://t.co/zg9mlTZlf0 90 seconds on low. 67 minutes on max. Same workout-app request. Different results. What did the extra time buy—and did you need it? My guide to Claude Code’s effort settings: when to iterate fast, when to verify deeply, and what to measure. https://t.co/zg9mlTZlf0view source ↗
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X X / Twitter 1w ▲ 1 positive think / humani think very soon llm outputs cannot be simply graded by human readings - right now we judge model outputs by checking things like 1) oh this is more concise 2) oh this made the whole point more relevant 3) oh this is better for documentation, rather than, "what is actually correct, in-depth and future proof". of cour… i think very soon llm outputs cannot be simply graded by human readings - right now we judge model outputs by checking things like 1) oh this is more concise 2) oh this made the whole point more relevant 3) oh this is better for documentation, rather than, "what is actually correct, in-depth and future proof". of course, each task's purpose is also different, so being a good judge at all subjects is also very difficult. right now the biggest difference i see of opus 5 vs 5.5 is decision making, 5 is indecisive leading to many self-contraditions, where 5.5 makes sure to check corners and decide on a most-probably-correct outcomeview source ↗
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X X / Twitter 1w ▲ 1 positive claude / pricingI don’t know why Sonnet 5 is currently the default model in claude - if in their own docs they advise keeping the default on Opus 5.5. And just look at the price/intelligence. Even in the documentation they recommend: > For most agent workloads, start with Claude Opus 5.5 at its default effort (medium). https://t.co/2… I don’t know why Sonnet 5 is currently the default model in claude - if in their own docs they advise keeping the default on Opus 5.5. And just look at the price/intelligence. Even in the documentation they recommend: > For most agent workloads, start with Claude Opus 5.5 at its default effort (medium). https://t.co/2QHzcfBKMT So I see the point of switching so that you don’t throw money away. #claude #opus5.5 #anthropic #claudecode #opus #sonet5 #claudeaiview source ↗
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X X / Twitter 1w ▲ 2 positive pricing / anthropicNo major model releases over the last day. Apparently the big tech vendors used all available compute preparing talking points for the Xi state dinner. And what a cluster it was. Elon Musk, Jensen Huang, Lisa Su, Sam Altman, Mark Zuckerberg, Sundar Pichai, Satya Nadella, Tim Cook and Jeff Bezos all checked present for… No major model releases over the last day. Apparently the big tech vendors used all available compute preparing talking points for the Xi state dinner. And what a cluster it was. Elon Musk, Jensen Huang, Lisa Su, Sam Altman, Mark Zuckerberg, Sundar Pichai, Satya Nadella, Tim Cook and Jeff Bezos all checked present for the White House orbit last night. Musk, Huang, Su and Cook were seated at the head table with Trump and Xi. However, no corporate CEOs from China. Anthropic was also absent, and didn't even make the kiddies table. You can guess why. Did they say anything substantive afterward? Mostly, no. Dinner coverage was overwhelmingly handshakes, seating charts and sea bass. Bilateral talks did include AI, and Xi publicly calling for continuing U.S.-China dialogue on AI risks and benefits, preventing misuse and keeping AI under human control. Musk was the exception, but made his interesting comments before the hors d'oeuvres rather than over dessert. In an interview aired by Chinese state media, Musk called Chinese AI models “generally outstanding”. He went on, saying Chinese may be the best work in the world on performance per unit of compute. He estimated China could overcome its lithography and chip-manufacturing constraint in roughly two to three years, mark those as Musk years. cough, cough. Moving on, the latest OpenAI rumor is ChatGPT Pro Max. Tibor Blaho found PROMAX / chatgptpromax references in ChatGPT’s front-end code. The unreleased plan is showing around $500/month, with the key description “Fastest Work and Codex.” A $600 figure in one screenshot appears to include VAT. Which then led to speculation that Cerebras inference could be involved. Reddit immediately reached the obvious conclusion: if OpenAI wants five hundred bucks a month, somebody had better put something considerably more interesting than slightly faster Sol behind the velvet rope. They were more distracted by picking up a useful piece of evidence. which is no longer one Azurview source ↗
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X X / Twitter 1w ▲ 1 positive claude / pricingI went through Anthropic’s official Opus 5.5 + Claude Code guidance and turned it into a practical guide covering prompting, CLAUDE.md, context, subagents, effort levels, verification, and better workflows. Full article below ↓ https://t.co/LzA4svKzA8 I went through Anthropic’s official Opus 5.5 + Claude Code guidance and turned it into a practical guide covering prompting, CLAUDE.md, context, subagents, effort levels, verification, and better workflows. Full article below ↓ https://t.co/LzA4svKzA8view source ↗
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X X / Twitter 1w ▲ 2 positive claude / pricingHidden Claude Capability 3 (that ChatGPT users miss): The communication quality that reads as human instead of AI. He told them about the quality dimension that no benchmark measures and that the ChatGPT user noticed immediately but attributed to the wrong cause. The ChatGPT user said Claude's writing was "impressive… Hidden Claude Capability 3 (that ChatGPT users miss): The communication quality that reads as human instead of AI. He told them about the quality dimension that no benchmark measures and that the ChatGPT user noticed immediately but attributed to the wrong cause. The ChatGPT user said Claude's writing was "impressive." He noticed the quality. He attributed it to Claude being "a writing tool" implying that Claude trades capability for polish, that the writing quality comes at the cost of the analytical depth ChatGPT provides. He told him the attribution is backwards. Claude Opus 5.5's writing quality doesn't come at the expense of analytical depth it comes alongside it. The same model that writes with natural cadence, varied sentence structure, and direct communication also scores Fable-class performance on coding benchmarks, completes complex refactors in fewer iterations than competing models, and achieves the highest alignment score of any model Anthropic has ever tested. He told him early testers of Opus 5.5 consistently describe the communication as "clearer and more direct" than any previous Claude model and markedly more human-sounding than Astra's output. Anthropic trains for communication quality as a distinct capability not just saying the right thing (accuracy) but saying it in a way that reads as a knowledgeable human rather than a capable machine (expression). He told him the practical impact compounds across every output the user doesn't edit before sending. The email drafted by Claude that requires zero editing before forwarding to a client saves 5 minutes. The report that reads as professional analysis rather than AI-generated summary saves 15 minutes of rewriting. The code comments that read as if a senior engineer wrote them rather than a documentation bot save the entire team's reading time. The communication quality isn't a "writing feature." It's a time-saving feature that applies to every output the AI produces.view 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 ▲ 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 ▲ 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 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 ▲ 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 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 ▲ 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 ↗