If you've seen Manus AI in your feed lately, here's what the marketing doesn't emphasize: Manus does not have its own model. It routes your requests through Anthropic's Claude 3.5 Sonnet (with Alibaba's Qwen handling specific sub-tasks), breaks them into steps, and executes them inside a cloud-based Ubuntu sandbox.
This was confirmed in March 2025 when a user prompted Manus to output its own internal runtime files, exposing system prompts, a 29-tool integration suite, and the full model configuration. Manus's chief scientist publicly confirmed the Claude + Qwen stack after the leak.
The execution layer is real engineering. Manus uses a "CodeAct" approach — instead of brittle pre-defined API tool calls, the agent writes and runs disposable Python scripts dynamically. The 29-tool integration handles browser automation, file operations, shell commands, and code execution. Maintaining that across thousands of edge cases is non-trivial work.
But if you're a developer, none of that justifies the price.
The Cost Gap
Manus runs on credits. The $20/month Standard plan gives you 4,000 credits. Here's what tasks actually cost in practice:
- Simple web search: 10–20 credits
- Data visualization: ~200 credits
- Complex web app build: 900+ credits
- Large research task (user-reported failure): 8,555 credits wasted
Four thousand credits. One complex build eats a quarter of that. Four tasks and your month is over.
Manus's own help documentation states that any credit-cost estimate the AI generates should be treated as "hallucinations rather than factual commitments." If credits run out mid-task, the work is permanently lost with no way to save or recover it.
Now compare that to Claude Code.
Claude Code is a standalone CLI tool running the same Claude reasoning engine that powers Manus. You get a 400,000 token context window, multi-file agentic editing, and Zero Data Retention. You pay API rates with full visibility into token consumption and hard spending caps you control.
A web-debugging session that burns $200 in Manus credits costs roughly $5 through Claude Code. Same model. Same reasoning. Forty times cheaper.
What You're Actually Paying For
The gap between $5 and $200 is the Orchestration Tax — the premium Manus charges for wrapping foundation models in a managed execution environment.
That tax buys you: sandboxed cloud VMs, memory persistence across long-running tasks, multi-model routing, and a polished UI that requires zero infrastructure setup.
If you can't set up a Docker container, wire up Playwright, and manage a LangChain orchestration pipeline yourself, that tax has real value. Marketing agencies and non-technical operators save hours of manual work per task.
If you can do those things — and you're reading this on Dev.to, so you probably can — you're paying a massive convenience fee for an interface you don't need.
The Stack for Developers
- Codebase management: Claude Code (CLI, API pricing, ZDR)
- Research-heavy tasks: Perplexity Pro ($20/month, flat rate, cited multi-model responses)
- Full local control: OpenClaw (open-source, zero cost, but watch the security — one audit found 500+ vulnerabilities including 8 critical)
- General AI assistance: Claude Pro or ChatGPT Plus ($20/month, flat rate)
All of these charge predictable rates. None of them will silently drain your budget on a hallucination loop.
Full Review
We published a complete structural analysis covering the credit system, GAIA benchmark issues (self-submitted scores, conflict of interest with Meta), the geopolitical situation (both founders barred from leaving China), and the "My Computer" desktop app's privacy implications.
Full breakdown: https://future-stack-reviews.com/manus-ai-review-2026/
Top comments (1)
Solid analysis. The orchestration tax framing really nails it -- we hit this exact tradeoff building fazm.ai (consumer macOS agent) and Terminator (open source desktop automation framework, like Playwright but for your whole OS). One architectural choice that matters as much as the model layer: we use native accessibility APIs instead of screenshots for interacting with the desktop. It's faster, more reliable, and way cheaper on tokens since you're not shipping pixel data back and forth. The 40x cost gap you describe compounds when the orchestration layer also makes bad choices about how it observes the screen.