Anthropic Removes Claude Code from Pro Plan, Developer Community Erupts, OpenAI Seizes the Opportunity to Strike

marsbitPubblicato 2026-04-22Pubblicato ultima volta 2026-04-22

Introduzione

Anthropic has removed its coding agent, Claude Code, from the $20/month Pro plan, triggering strong backlash from the developer community. The change was reflected in updated pricing pages and documentation, which now state that Claude Code is only available to Max plan subscribers, starting at $100/month. Anthropic’s growth lead, Amol Avasare, described the move as a "small-scale test" affecting only 2% of new users, but the broad updates to public documentation contradicted this claim. He later acknowledged that rising computational costs due to increased usage made the previous pricing structure unsustainable. Developers expressed frustration on platforms like Hacker News, Reddit, and GitHub, with some calling it a reason to switch to local or open-source models like Llama or Mistral. In response, OpenAI quickly emphasized that its competing product, Codex, remains available in both free and $20/month Plus plans, and reaffirmed its commitment to transparent communication with users. Google’s Gemini CLI remains free, adding further competitive pressure. The incident highlights growing tension between AI service affordability and the high compute costs of advanced coding assistants.

Author: Claude, Deep Tide TechFlow

Deep Tide Guide: On April 21, Anthropic quietly removed its programming agent Claude Code from the $20/month Pro plan, with related pricing pages and help documents updated simultaneously. Although Growth Lead Amol Avasare claimed this was only a "small-scale test" targeting about 2% of new users, the site-wide documentation has been completely rewritten to state "Max plan only," sparking a massive backlash in the developer community. OpenAI promptly publicly stated that Codex will remain in the free and Plus plans, while the local model camp called this incident "the best time to switch to self-hosting."

Anthropic is testing developers' price tolerance limits, at the possible cost of user trust.

On April 21, some developers noticed significant changes on Anthropic's official pricing page: Claude Code was marked as unavailable (with an "X") in the feature comparison table for the Pro plan ($20/month), whereas the day before the page still stated that the Pro plan "includes Claude Code." Almost simultaneously, the wording in Anthropic's help documentation quietly changed from "Use Claude Code with your Pro or Max plan" to "Use Claude Code with your Max plan."

AI industry observer Ed Zitron first sounded the alarm on the Bluesky platform, and the news quickly sparked discussions on Hacker News, Reddit r/LocalLLaMA, and X platform.

Official Statement Calls it a "Small-Scale Test," but Documentation is Fully Rewritten

Facing the developer community's intense reaction, Anthropic Growth Lead Amol Avasare responded that day on the X platform, stating this was only a small-scale test targeting about 2% of newly registered users, and existing Pro and Max subscribers would not be affected.

However, multiple media outlets pointed out that Avasare's statement contradicted the actual situation: the feature comparison matrix on the pricing page had been updated site-wide, and the help documentation had also been modified simultaneously. These changes were visible to all visitors and did not resemble a灰度 test for 2% of users. According to The Register, as of publication time, an Anthropic spokesperson had not responded further to this contradiction.

In subsequent tweets, Avasare explained the logic behind the adjustment: when the Max plan was launched a year ago, Claude Code was not bundled with it, Cowork did not exist, and asynchronous agents running for hours were not yet the norm. Since then, Claude Code usage has surged significantly post-Opus 4, and user usage patterns have fundamentally changed. He admitted, "Usage per subscriber has increased substantially, and our current plan structure was not designed for this."

This statement actually revealed a key point: providing Claude Code access for a $20 monthly fee has become unsustainable in terms of computing costs. According to industry analysis, each Claude Code programming session consumes far more tokens than regular chat, and the computational cost of long programming sessions could be several times the subscription fee.

From $20 to $100, Developers Pushed to a Decision Point

If this change is ultimately implemented permanently, Pro users who wish to continue using Claude Code would need to upgrade to the Max 5x plan starting at $100/month, or the Max 20x plan at $200/month. The price has increased fivefold.

Developers reacted quickly and strongly. A GitHub issue titled "Breaking Change: Claude Code CLI Removed from Pro Plan Without Notice" appeared within hours.

A related post on Hacker News received over 100 upvotes and more than 40 comments in less than an hour. One user's experience was representative: he started with the $20 Pro plan in January, upgraded to $200 due to frequently hitting usage limits, downgraded to $100 due to quality drops in Claude Opus 4.6, and recently downgraded back to $20 because he was satisfied with OpenAI Codex's performance on GPT-5.4. "I went from the most enthusiastic recommender to my current state," he wrote.

The reaction on the Reddit r/LocalLLaMA community was more direct. A post titled "Anthropic removes Claude Code from Pro plan, users call it 'the best time to switch to local models'" received extensive discussion, focusing on the progress made by open-source models like Meta's Llama series, Mistral, and DeepSeek in programming capabilities over the past year, as well as the maturity of local deployment tools like Ollama and LM Studio.

OpenAI Quickly Strikes, Codex Remains in Free and Plus Plans

Competitors did not miss this window.

From OpenAI's side, an employee responsible for Codex-related work posted on the X platform: "I don't know what they're doing over there, but Codex will continue to be available in the free and Plus ($20) plans. We have the compute and efficient models to support it. For important changes, we communicate with the community in advance."

The timing and wording of this statement were highly targeted.

OpenAI's Codex, as the main competitor to Claude Code, is currently offered in the ChatGPT Plus ($20/month) plan with a usage limit of 30 to 150 messages every 5 hours, and also has basic access in the free plan. Additionally, weeks ago, OpenAI released a Codex plugin specifically designed for Claude Code users, allowing developers to directly call Codex for code review or task delegation within the Claude Code workflow.

Google is also active; Gemini CLI is currently completely free. According to The New Stack, Anthropic has recently been under pressure regarding computing supply. Not only did it remove Pro access to Claude Code, but it also previously cut off third-party client OpenClaw's access to Claude via subscription accounts. Platform stability issues and frequent outages have also provided opportunities for competitors.

Domande pertinenti

QWhat change did Anthropic make to its Pro plan regarding Claude Code, and when was it noticed?

AAnthropic removed Claude Code from its $20/month Pro plan on April 21, as noticed by developers who saw the pricing page updated to show Claude Code as unavailable for Pro users, whereas it was previously included.

QHow did Anthropic's growth head, Amol Avasare, explain the removal of Claude Code from the Pro plan?

AAmol Avasare claimed it was a 'small-scale test' affecting only about 2% of new sign-ups, stating that existing Pro and Max subscribers would not be impacted, and attributed the change to significantly increased usage and computational costs.

QWhat was the reaction from the developer community to Anthropic's change?

AThe developer community reacted strongly, with immediate backlash on platforms like Hacker News, Reddit, and X. Many expressed frustration, with some considering switching to local models or alternatives, and a GitHub issue was quickly created to highlight the abrupt change.

QHow did OpenAI respond to Anthropic's removal of Claude Code from the Pro plan?

AOpenAI swiftly responded by affirming that its Codex would remain available in both free and Plus ($20/month) plans, emphasizing their ability to support it with sufficient compute and efficient models, and criticizing Anthropic's lack of prior communication.

QWhat are the pricing implications for Pro users if they want to continue using Claude Code after this change?

AIf the change becomes permanent, Pro users would need to upgrade to the Max plan, which starts at $100/month for the 5x version or $200/month for the 20x version, effectively increasing the cost fivefold or more to access Claude Code.

Letture associate

It Took Me a Year to See the Hard Truth About Agent Payments

**Title: It Took Me a Year to See the Hard Truth About Agent Payments** Over the past year, I've worked on infrastructure for the Agent economy, engaging with major players like Stripe, Visa, Coinbase, and numerous startups. The findings reveal a stark reality: genuine, widespread demand for Agent-based payments does not yet exist. **Key Observations:** * **Agent-to-Merchant (Shopping):** The user experience for AI shopping often falls short, especially for visual product discovery. While AI excels at understanding needs, conversational interfaces can't yet replace browsing and comparing multiple products visually. Current merchant interest is largely defensive ("Agent Engine Optimization") for a future that hasn't arrived. High-frequency, low-friction purchases (like food delivery) are potential fits, but lack open APIs and face high AI inference costs. Simpler, more affordable, or cross-language interactions for complex UIs are a niche opportunity but require massive consumer distribution to scale. * **Agent-to-API (Developer Tools):** Developer payment needs for APIs (computing, data, models) are already met through subscriptions and prepaid credits. The core challenge is not payment friction but supplier economics: most large SaaS providers prefer enterprise contracts over micropayments for API calls. Protocols like MPP and x402 suit the long-tail of smaller services but cater to a developer market historically reluctant to pay for these tools. Major infrastructure needs at the top of the stack are already being addressed. * **Agent-to-Agent (Machine Commerce):** This is a long-term vision with almost no current transaction volume. While a future with high-speed, high-frequency, multi-party machine-to-machine transactions would require novel infrastructure, it remains theoretical. The market is not here yet. * **Agent-to-Finance:** This is the only category with clear, present demand. Financial professionals and DeFi users already pay for tools, and AI augmentation is a natural evolution. Autonomous AI agents can enable entirely new financial strategies. However, competition is fierce from established, regulated incumbents who can more easily layer AI onto their existing products. **The Core Insight:** Companies, especially giants with long time horizons, are building defensively for a potential future of mass machine commerce. For them, early investment is a low-cost hedge. For startups, the current market reality is different. The primary challenge isn't just moving money between agents (payments). The larger, unsolved problem is **orchestration** – coordinating work between agents and humans, verifying outcomes, and then settling. Payment is just a part of settlement, which is just a part of orchestration. Companies that solve the orchestration problem will subsume payments, not the other way around. After a year of building, we see the real, growing, and underserved market opportunity lies in this broader domain of orchestration.

链捕手20 min fa

It Took Me a Year to See the Hard Truth About Agent Payments

链捕手20 min fa

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

A researcher discovered a critical "infinite mint" vulnerability in the Zcash cryptocurrency's Orchard protocol using Claude Opus 4.8, leading to a swift fix but also a 50% market drop, erasing billions in value. This incident highlights a new era where powerful, accessible AI models are dramatically lowering the barrier to finding software vulnerabilities. Previously, the security community feared specialized models like Claude Mythos Preview, capable of finding decades-old zero-day exploits. The Zcash case, however, involved a publicly available, general-purpose model. This shift makes advanced security auditing—and attack capabilities—accessible to far more people, not just experts. The mass democratization of vulnerability discovery brings a dual challenge: a flood of low-quality, AI-generated false reports that overwhelm maintainers, and the real, rapid uncovering of deep, dangerous bugs. Open-source projects, often understaffed and unfunded, are particularly vulnerable to this "attention DDoS." The article cites examples like curl shutting down its bug bounty program due to the unsustainable workload. Our perceived digital safety has often been luck, relying on the high cost and effort required to find deeply hidden flaws in complex systems, as seen with historical vulnerabilities like Heartbleed or Baron Samedit. AI changes this cost structure, effectively "mass-producing flashlights" to illuminate every corner of our codebase. While large companies operate extensive security chains involving external white-hat hackers and massive defensive operations, the global cybersecurity workforce faces a severe shortage, especially of experienced personnel capable of analyzing complex threats and coordinating fixes. The core dilemma emerges: AI makes *finding* bugs cheap and scalable, but *fixing* them remains a slow, expensive, and human-intensive process. The article concludes that AI won't destroy the internet but acts as a bright light, revealing that our digital existence is not inherently secure but is precariously maintained by ongoing human effort. The true cost in the AI era may not be discovery, but whether there will be enough people left willing and able to do the hard work of repair.

marsbit53 min fa

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

marsbit53 min fa

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

"Codex Goal Mode: How to Make AI Work Continuously Toward a Specific Goal" OpenAI's Codex "goal mode" (/goal) transforms the AI from a reactive code assistant into a proactive execution agent capable of working autonomously for hours or even days to achieve a defined objective. To maximize its effectiveness, follow these key principles: 1. **Define Clear, Verifiable Exit Criteria:** The goal prompt should be a concise, measurable success condition, not a lengthy specification. Use quantifiable metrics like "reduce build time by 30%" or "achieve 100% test parity." 2. **Provide Initial Guidance and Tools:** Direct Codex toward likely problem areas and specify available tools (e.g., browsers, testing environments) to prevent it from exploring unproductive paths. 3. **Enable Progress Measurement:** Equip Codex with ways to track advancement, such as creating comparison tools for visual tasks or evaluation sets, ensuring it can gauge its own progress. 4. **Use a Realistic Execution Environment:** For tasks like performance optimization, provide access to environments that closely mimic production (e.g., similar configs, databases) to yield valid results. 5. **Be Cautious with Visual Goals:** Avoid vague "pixel-perfect" instructions. Instead, supplement visual references with functional checklists or design system specifications to prevent Codex from obsessing over minor details. 6. **Implement Progress Tracking:** For long-running tasks, have Codex commit code to draft PRs, update progress documents, or send Slack updates to maintain visibility into its work. 7. **Review and Consolidate Results:** Once the goal is met, instruct Codex to review its work, clean up ineffective experimental code, and reflect on what strategies succeeded or failed. Ultimately, using goal mode shifts the developer's role from writing prompts to managing a persistent engineering agent—defining objectives, establishing metrics, configuring environments, and conducting final reviews.

marsbit2 h fa

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

marsbit2 h fa

From Ethereum to AI's 'CROPS': What Exactly Is This 'Slow Variable' That Vitalik Has Repeatedly Emphasized?

Recently, Vitalik Buterin has frequently emphasized the concept of "CROPS," first outlined in the Ethereum Foundation's March mandate as core principles guiding its focus: Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. CROPS represents Ethereum's commitment to providing foundational capabilities for user sovereignty—enabling asset ownership, identity expression, and coordination without reliance on centralized platforms or surrendering ultimate control. This framework is gaining new urgency with the rise of AI, particularly AI agents managing digital assets and automating transactions. While AI offers convenience, it risks centralizing user data, intent, and control if dependent on opaque, centralized services. Vitalik argues for "CROPS AI"—AI that is open, privacy-preserving, secure, and capable of local execution to maintain user agency. He highlights convergence between "CROPS Ethereum access layers" and "CROPS AI," such as using zero-knowledge proofs for private remote LLM calls and Ethereum RPC reads, ensuring users can access services without exposing sensitive information. Ultimately, CROPS is not just an abstract ideal but a practical guide for Ethereum's development and AI integration. It addresses the critical long-term question: as digital systems grow more powerful, how can users retain control over their privacy, assets, and autonomy? In an AI-driven era, these principles may define Ethereum's enduring value—prioritizing verifiable, secure, and user-centric design over short-term optimizations like speed and cost alone.

marsbit2 h fa

From Ethereum to AI's 'CROPS': What Exactly Is This 'Slow Variable' That Vitalik Has Repeatedly Emphasized?

marsbit2 h fa

Trading

Spot
Futures
活动图片