OpenManus vs Manus AI: Features, Privacy, Cost (2026)

Two products dominate the conversation when teams ask about general-purpose autonomous agents in 2026: Manus AI from the Singapore-incorporated startup Butterfly Effect, and OpenManus, the MIT-licensed framework spun out of the MetaGPT research community. They share a lineage (OpenManus was prototyped within hours of Manus going viral), they share an architectural sketch (an LLM loop that plans, calls tools, and drives a browser), and they share a target audience (engineers who want one agent that can do many things). What they do not share is a deployment model, a pricing model, or a privacy posture.

This guide walks through the real differences, with concrete pricing, citations to primary sources, and a decision framework you can apply to your own stack. We will not invent benchmark numbers; where the published evidence runs out, we will say so and switch to a decision framework instead.

What does each tool actually do?

Both products implement a similar agent loop: take a goal, decompose it into steps, call tools (web browser, code interpreter, file system, search), observe results, iterate. The mechanical contract is roughly the same. The substrate is not.

Manus AI is a hosted product. You sign in at manus.im, type a goal, and watch a remote agent operate a browser, write files, and produce a deliverable inside Manus's cloud sandbox. It bundles its own model routing (multiple frontier LLMs under the hood), its own browser, its own code execution environment, and its own file storage. The user surface is a chat window plus a video-like view of the agent working.

OpenManus is a Python framework you clone, configure, and run locally. The repo at github.com/FoundationAgents/OpenManus sits at 56.3k stars as of mid-2026, with active development under the MetaGPT-affiliated FoundationAgents organisation. It ships an agent loop, a Playwright-based browser tool, an MCP (Model Context Protocol) integration, a data-analysis agent, and a config.toml file where you wire in whichever LLM you want — GPT-4o by default, but anything with an OpenAI-compatible endpoint works, including local models served through Ollama or vLLM.

The conceptual overlap is real, but the deployment story — "call a SaaS" versus "run a Python process" — cascades into every other axis of the comparison.

How do they compare on features?

At the capability level, both products check most of the same boxes. The differences show up in extensibility and in which LLM you can point them at.

The feature set is close enough that on a given afternoon task — "go research these ten companies and produce a comparison table" — either tool will get there. Where OpenManus pulls ahead is for engineering teams: the ability to swap in a cheaper model, run inside your network boundary, or fork the planner to add domain-specific tools is the difference between a product you adopt and a framework you build on.

How do they compare on privacy and data?

This is the axis with the cleanest split, and it is the one most enterprise procurement teams will land on first.

Manus AI is cloud-only. Every file you upload, every URL the agent fetches, every intermediate result lives on Manus's infrastructure. The corporate situation is complicated: Manus was operated by Butterfly Effect, a Singapore-incorporated entity originally founded by a Chinese team. Meta announced a roughly $2 billion acquisition that closed in December 2025, and on April 27, 2026, China's National Development and Reform Commission ordered the deal unwound on national security grounds. As of mid-2026, the corporate parent and the data-flow boundary are actively contested.

For a regulated workload — healthcare, legal, financial services, anything bound by GDPR data-residency requirements, anything covered by export-control concerns about Chinese or US data handling — that uncertainty is itself a problem, separate from the underlying technical privacy story. You cannot DPA your way around "we don't know which jurisdiction owns the parent next quarter."

OpenManus runs on your hardware. The only thing that leaves your machine is whatever your chosen LLM provider sees — and you control that choice. Concretely:

• Run with Claude or GPT-4o via API: prompts and tool outputs leave your machine to Anthropic / OpenAI. Their published retention and DPA terms apply.
• Run with Llama 4, Qwen 3.5, or DeepSeek V4 via Ollama or vLLM on your own GPU: nothing leaves your network. Full air-gap mode is feasible.
• Run against a private inference endpoint (Azure OpenAI, AWS Bedrock, your own VPC): you keep the existing compliance posture of that endpoint.

For SOC 2, ISO 27001, HIPAA, or any GDPR Schrems II analysis, OpenManus is the option that lets you pass without renegotiating your data boundary. That is a structural advantage, not a feature roadmap item.

How do they compare on cost?

Manus's pricing as published on manus.im/pricing in 2026 is a credit-based model:

• Free — $0/month, 300 daily refresh credits + 1,000 starter credits, one concurrent task
• Pro Standard — $20/month, 4,000 monthly credits
• Pro Customisable — $40/month, 8,000 monthly credits
• Pro Extended — $200/month, 40,000 monthly credits
• Team — $20/seat/month, two-seat minimum

Credits do not roll over month to month. Each agent action consumes credits based on complexity, which means the per-task cost is non-deterministic from the user's side — the same prompt can cost more or less depending on how the agent decides to decompose it.

OpenManus is free. The real cost is the LLM behind it. A worked example to make the comparison concrete: assume a moderately heavy month of agent use — say, 50 GAIA-class research tasks, each averaging about 200K input tokens and 30K output tokens after the back-and-forth of planning, browsing, and synthesis.

The pattern is predictable: Manus wins on low-volume convenience (a few tasks a week, no infra to maintain), OpenManus on a frontier API wins on transparency (you know exactly what each task cost), and OpenManus on a self-hosted open model wins on high-volume runs (the fixed GPU cost amortises across hundreds of tasks). The cross-over point depends on how heavy your typical task is, but for most agentic workloads, somewhere between 50 and 200 tasks per month is where each option becomes optimal in turn.

How do they compare on benchmarks?

Manus's headline benchmark numbers are from its March 2025 launch on the GAIA benchmark (a Meta / Hugging Face / AutoGPT-team test of multi-step, tool-using, real-world reasoning). The published scores were 86.5% on Level 1, 70.1% on Level 2, and 57.7% on Level 3, which beat OpenAI's then-published agentic results of 74.3% / 69.1% / 47.6% on the same levels (Bind AI, March 2025; arXiv 2505.02024).

OpenManus does not publish equivalent first-party GAIA numbers — and this is the honest part of the comparison. OpenManus's score on GAIA is not a property of OpenManus; it is a property of whichever LLM you plug in. The same OpenManus harness with GPT-4o behind it scores very differently from the same harness with a local 8B Qwen behind it. Any "OpenManus GAIA score" you see quoted is really a score for a specific OpenManus + model + tool configuration on a specific eval run.

If raw GAIA performance is the only thing you optimise for, Manus is the safer pick today, full stop. For most production workloads, however, GAIA is the wrong target — you care about how the agent does on your tasks with your tools. That eval has to be built in-house regardless of which substrate you choose, and on those internal evals OpenManus's swap-the-model property is usually the more useful axis to optimise.

Companion guide

For the full landscape of agentic coding tools, see our AI coding agents complete guide for 2026.

When should you pick Manus AI?

Manus AI is the right call when:

• You want a working agent today, not a framework. Sign in, prompt, done. No infra, no config.toml, no GPU rental.
• Your data is non-sensitive. Public research, marketing analysis, exploratory work that does not touch customer PII, regulated data, or proprietary source code.
• Your usage is low-to-moderate. Under ~50 tasks per month, the $20–$40 tier is hard to beat on total cost of ownership when you price in the engineering time you would otherwise spend wiring up OpenManus.
• Vision-on-browser is core to your workflow. Manus's cloud browser + vision stack is well-tuned out of the box; replicating it on OpenManus means choosing a vision-capable LLM and configuring Playwright to feed it screenshots, which works but is more setup.
• The corporate-uncertainty story is acceptable to your buyer. If your security review will not pass on "the parent company's jurisdiction is contested," stop here — pick OpenManus.

When should you pick OpenManus?

OpenManus is the right call when:

• You need data residency or air-gap. Regulated industries, government, or internal-IP-heavy engineering teams. Run against a local Ollama or your own VPC inference endpoint.
• You want predictable, transparent cost. Per-task USD is exactly what your LLM provider charges; no credit-system non-determinism.
• You want to customise the agent. Add domain-specific tools via MCP, swap the planner, fine-tune the prompts, integrate with your existing toolchain. MIT licence; no API to negotiate with.
• You are running at scale. Above a few hundred tasks per month, the OpenManus + self-hosted open model combination dominates on unit economics.
• You want to move between LLMs. The frontier moves fast. OpenManus lets you A/B Claude vs GPT vs Qwen vs Llama on your real workload without changing the rest of your stack.

The pragmatic answer for many teams is both: Manus for individual contributors doing ad-hoc work where speed matters, OpenManus as the backbone for any production-grade agent deployment.

How do you actually get OpenManus running?

The setup is intentionally minimal. From the FoundationAgents/OpenManus repo:

git clone https://github.com/FoundationAgents/OpenManus.git
cd OpenManus
pip install -r requirements.txt
cp config/config.example.toml config/config.toml
# edit config.toml: set model, base_url, api_key
python main.py

The config.toml is where the leverage lives. A minimal Claude-backed configuration looks like:

[llm]
model = "claude-opus-4-7"
base_url = "https://api.anthropic.com/v1"
api_key = "sk-ant-..."
max_tokens = 8192
temperature = 0.0

Point base_url at http://localhost:11434/v1 for Ollama, at your Azure OpenAI deployment URL for Azure, or at an internal proxy if you front your LLM access for compliance reasons. The rest of the framework does not need to change.

What's the verdict?

The honest one-line verdict: Manus AI is the better product; OpenManus is the better foundation. If you want a polished agent to use, Manus has a head start on the cloud-browser experience and the published benchmark numbers. If you want an agent stack you control — cost, model, data path, tools — OpenManus is the obvious choice, and the cost story usually closes the conversation once volume goes up.

Most production deployments we see in 2026 lean OpenManus, not because of any single feature, but because the combination of MIT licence, swap-the-LLM, run-anywhere, and zero credit-system overhead is hard to architect around once a team has tasted it.

Who builds this kind of agent infrastructure?

If you're scaling agent infrastructure — wiring OpenManus into your toolchain, building custom MCP tools, designing evals for your domain, or running a self-hosted inference stack to keep agent data on-premise — the bottleneck is usually engineers who have shipped this kind of system before, not the framework itself. Codersera matches you with vetted remote developers who have built production agentic-AI systems: MCP tool authors, agent harness engineers, and inference-stack specialists. We run a risk-free trial so you can validate technical fit before committing.

FAQ

Is OpenManus an official fork of Manus AI?

No. OpenManus is an independent open-source project built by contributors from the MetaGPT research community, including Xinbin Liang and Jinyu Xiang, and is not affiliated with Butterfly Effect or Manus. The two projects share design inspiration and a similar agent-loop architecture, but OpenManus was reverse-engineered from public information about Manus, not derived from its source code.

Can OpenManus match Manus on GAIA benchmarks?

It depends entirely on the LLM you plug in. Manus's published GAIA scores (86.5% / 70.1% / 57.7%) are for its integrated, tuned stack. OpenManus paired with a frontier model and a comparable tool set can land in similar territory, but no first-party benchmark from the OpenManus team currently establishes that publicly. For most production teams, internal task-specific evals matter more than GAIA leaderboards either way.

Does OpenManus need a GPU to run?

Only if you want to host the LLM locally. If you point OpenManus at the Anthropic, OpenAI, or any other hosted API, the OpenManus process itself is a lightweight Python application that runs comfortably on a laptop — the heavy lifting happens on the LLM provider's hardware. GPUs only enter the picture when you run a local Ollama, vLLM, or similar inference server for fully air-gapped deployments.

Can I use Manus AI for customer data?

Read Manus's DPA and current corporate ownership status carefully before answering yes. As of mid-2026, Manus's parent-company situation is in flux following China's reversal of the Meta acquisition. Any workload subject to GDPR data-residency rules, US export controls on data transfer to Chinese-controlled entities, or HIPAA-grade privacy requirements should treat Manus as a closer-to-public-cloud risk and plan accordingly. For regulated workloads, OpenManus on a private inference endpoint is the lower-risk path.

Is OpenManus actually production-ready?

Define production. As a framework it is stable, MIT-licensed, and at 56k+ GitHub stars with an active maintainer organisation. As a turnkey product it is not — you are expected to add observability, error handling, retries, security review of tool access, and your own eval harness. For most teams, OpenManus is the engine, not the car. Plan engineering time accordingly.

Which LLM should I pair OpenManus with?

For research-and-analysis style agent tasks where reasoning quality dominates, Claude Opus 4.7 or GPT-4o-class models are the safe defaults. For high-volume, narrower-scope tasks where cost dominates, Qwen 3.5 or Llama 4 served locally via Ollama is usually the right pick. The framework's portability is the point — start with a frontier API, build your evals, then move workload off to cheaper or self-hosted models as the evals tell you it's safe.

Can I run Manus AI and OpenManus side by side?

Yes, and many teams do. A common pattern is Manus as the on-demand tool for ad-hoc, low-sensitivity work ("summarise these ten URLs, build me a slide deck") and OpenManus as the platform for repeatable, production-grade agent workflows that need to live in your network boundary. They are not mutually exclusive — they target slightly different points on the convenience-versus-control curve.