Manus AI Faces Heat Over Unpredictable Credit Costs After USD 75M Benchmark Fund
AI-generated Image (Credit: Jacky Lee)
Manus, the autonomous agent platform developed by Butterfly Effect Technology, is facing sustained criticism over its credit-based pricing, with many developers arguing that the system feels unpredictable and too restrictive for serious coding and data work.
The service runs complex multi-step workflows in a cloud “virtual computer” and charges not per chat or minute, but in credits. As Manus has moved from a hyped invite-only beta to a commercial product, this model has become a central point of tension between the company and its most intensive users.
Company Origins and Launch
Butterfly Effect Technology was founded by entrepreneur Xiao Hong (肖弘), a Huazhong University of Science and Technology graduate and serial founder.
In 2015, Xiao launched Nightingale Technology in Wuhan and built WeChat-ecosystem tools Yiban Assistant and Weiban Assistant, which attracted early backing from investors including ZhenFund.
As large language models took off, Xiao created Butterfly Effect in 2022 and released Monica, a browser-based “all-in-one” AI assistant that integrates major models such as ChatGPT and Claude. By early 2024, Chinese business press reported Monica’s user base had exceeded several million and was growing in overseas markets.
Manus was announced on 5 March 2025 and launched publicly on 6 March. The team positions it as a general-purpose autonomous AI agent: rather than responding in a simple chat window, Manus plans tasks, runs tools and code inside a sandboxed cloud environment, and then reports back when a workflow is complete.
Media coverage and social posts describe intense early interest, with long waitlists and a rapid influx of users to Manus’s community channels following launch.
Butterfly Effect subsequently raised US$75 million in a Series B round led by Benchmark at a reported US$500 million valuation, according to Chinese financial media. Reports also note that the company has shifted its operational headquarters to Singapore, even as its origins and much of its engineering talent remain in China.
Architecture and GAIA Benchmark Performance
Under the hood, Manus uses a multi-agent architecture. Different internal agents handle planning, execution, knowledge retrieval and verification while manipulating a full virtual machine in the cloud, including a browser and terminal.
Butterfly Effect has stated that Manus orchestrates existing foundation models rather than training its own; public write-ups consistently describe it as building on models from providers such as Anthropic and Alibaba (Claude and Qwen) with additional tooling and control logic on top.
The company highlights Manus’s performance on GAIA – a benchmark for “general AI assistants” designed by Meta AI, Hugging Face and collaborators. Company-reported GAIA scores, cited in several independent analyses, are:
Level 1 (basic tasks): Manus 86.5%, OpenAI Deep Research 74.3%
Level 2 (intermediate): Manus 70.1%, Deep Research 69.1%
Level 3 (complex): Manus 57.7%, Deep Research 47.6%
These figures place Manus at or near the top of the public GAIA leaderboard and well above GPT-4 with plugins, which hovers around 15% in the same benchmark, though still below the human average of about 92%.
Reviewers who have tested Manus, however, note that real-world performance is uneven: while it can complete multi-step programming and data tasks, it still fails or stalls on some seemingly straightforward jobs such as simple games or travel bookings.
How the Credit System Works Today
Manus has always used credits as its core unit of usage. According to the company’s Help Center, credits are consumed based on three main drivers:
Large language model (LLM) tokens for planning, decision-making and output,
Virtual machine time for file operations, browser automation and code execution,
Third-party API calls to services such as financial data or professional databases.
Credits are only consumed while a task is actively running; storing results or deployed websites does not use additional credits. If tasks fail because of technical issues on Manus’s side, the company says it provides full credit refunds.
As of late November 2025, the public pricing page shows three top-level plans: Free, Pro and Team.
Free – US$0/month
300 daily credits, labeled “up to 1,500/month”;
“Simple research for quick answers”;
1 concurrent task;
2 scheduled tasks.
Pro – from US$20/month
Base tier includes 4,000 credits per month;
300 refresh credits everyday (separate from the monthly pool);
“Advanced research for any topic”, “Professional website deployment” and slide-generation tools;
Early access to beta features;
Up to 20 concurrent tasks and 20 scheduled tasks.
A dropdown on the Pro card lets users choose significantly larger monthly credit bundles — up to 1,200,000 credits per month (US$5000 Pro Plan).
Team – US$39 per seat per month
Includes everything in Pro;
Adds SSO, the ability to opt out of data training, team-usage analytics, internal access control and shared slide templates.
The Help Center further explains that:
Monthly credits refresh on the subscription billing date;
Free credits and add-on credits never expire;
Event credits, earned from promotions, expire with the event.
Earlier in 2025, official documentation and independent pricing guides described a broader line-up — Free, Basic (US$19), Plus (US$39) and Pro (US$199) — with monthly credit pools of 1,900; 3,900; and 19,900 respectively, plus a Team plan at US$39 per seat. Those historical figures still appear in some reports and blog posts, but they no longer match the simplified Free/Pro/Team structure displayed on Manus’s current pricing page.
Developer Pushback on Credit Limits
Despite adjustments to plan names and allowances, the core criticism from many developers has remained the same: that Manus’s credits are too easy to burn and too hard to predict.
On the r/ManusOfficial subreddit, users describe spending hundreds or thousands of credits on single complex tasks such as long-running refactors or multi-file research agents. One thread detailed a subscription user exhausting close to 1,000 credits on “a couple of PDFs” and questioning whether 1,900 or 4,000 credits per month is viable for real-world work.
Other posts recount tasks that ran for dozens of hours, consumed several thousand credits and still did not produce working code, leading to refund requests and concerns about “double dipping” when both a subscription fee and credits are involved.
Manus employees active in those threads emphasise that credit consumption depends heavily on task complexity and suggest breaking projects into smaller pieces, writing more detailed prompts and avoiding very long conversational chains. They also note that refunds are available for failures attributable to the platform.
For casual users experimenting with simple automations or short research tasks, the Free plan’s 300 daily credits may be sufficient. But for Manus’s core early adopters — independent developers and small teams — the friction centres on two points:
Unpredictability: Without pre-task estimates, it is hard to know whether a given workflow will consume a few hundred credits or several thousand.
Perceived value: When credits are consumed quickly on a handful of heavy jobs, users compare Manus unfavourably with flat-fee assistants that offer “unlimited” or high-cap usage for similar monthly prices.
Why Butterfly Effect Sticks With Credits
Butterfly Effect frames the credit system as a pragmatic response to the economics of agentic AI. Because Manus coordinates multiple large models, runs a full remote desktop and often executes long, multi-step workflows, its underlying costs vary widely between tasks.
Internal and external analyses of agent platforms suggest that such systems can consume several times as many tokens as a simple chatbot, once planning, verification and tool-calling overheads are counted. Credit-based billing allows providers to tie revenue more closely to this variable infrastructure load.
Pricing specialists and SaaS analysts note that usage-linked models like Manus’s have become common among AI infrastructure vendors, especially in early product stages. At the same time, they observe a clear customer preference, particularly among developers, for predictable per-seat pricing, even with fair-use limits, over granular credits that can be difficult to forecast.
Positioning in the Wider AI Tool Landscape
Manus operates in a crowded market of AI tools for coders and knowledge workers. Many competitors, such as GitHub Copilot, Cursor or cloud-IDE assistants like Replit Ghostwriter, focus on inline code completion and repo-wide edits inside existing editors and charge flat monthly subscriptions without exposing token or task-level metering.
By contrast, Manus positions itself as a more autonomous operator capable of doing full-stack tasks: market research, financial analysis, travel planning, slide creation and full website or app prototyping. That autonomy is what attracts many early users, but it also magnifies both costs and complexity.
Analyst firms expect adoption of agentic AI to keep growing across enterprises while also predicting consolidation as customers gravitate towards platforms that balance capability, governance and pricing transparency.
For Butterfly Effect, the challenge is to show that Manus’s flexibility and autonomy justify its credit-based economics, or to continue evolving the model, for example through clearer pre-task estimates, more generous base tiers or hybrid approaches that combine flat access with usage-based billing. How it responds to ongoing user pushback will help determine whether Manus becomes a mainstream tool for developers or remains a specialised option associated with powerful autonomy but demanding economics.
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