# Manus vs Promptr

Manus records 19,195,779 monthly visits with -1.7% month-over-month growth and Free Trial pricing. Promptr records 0 monthly visits with 0% growth and Pay-as-you-go pricing.

| Metric | [Manus](https://openaitools.app/tool/manus) | [Promptr](https://openaitools.app/tool/promptr) |
| :--- | :--- | :--- |
| Website URL | https://manus.im | https://promptr.dev |
| Categories | AI Assistant, AI Agent, AI Copilot | Other |
| Pricing Model | Free Trial | Pay-as-you-go |
| Monthly Visits | 19,195,779 | 0 |
| MoM Growth Rate | -1.7% | 0% |
| Primary Market | Brazil | Global |
| Direct Visits | 10,563,180 | 0 |
| Review Score | 4.6 | 3.6 |
| Saved Count | 10 | 1 |
| Supported Platforms | Website | Website |

## Manus

Manus positions itself as an "action engine" rather than a chatbot. You describe an outcome, and the agent breaks it into steps, decides which tools to use, browses the web when it needs current information, and assembles the final deliverable in a cloud sandbox. Typical outputs include presentation decks, working websites and small games, generated images and video, and long-form research reports assembled through its Wide Research workflow.

The product ships as a mobile app (iOS and Android), a desktop app, and a browser extension, plus a web console with an API for programmatic use. A Slack integration lets teams dispatch tasks from chat, and workspace features such as team plans and SSO make it viable inside organizations rather than only for individuals. Because the agent runs asynchronously, a common pattern is to queue a task, walk away, and review the result later.

Pricing combines a free trial-style entry point with paid subscription tiers and a business plan; exact credit allowances and seats change over time, so confirm current terms on the official pricing page. The obvious trade-off is control: autonomous agents can misread intent or burn credits on the wrong approach, so clear briefs and intermediate review matter.

**Pricing & Plans:** Free trial available, with paid subscription tiers and a business plan for teams and higher usage. Credit allowances and seat details change, so check the official pricing page.

- **Autonomous task execution:** Give the agent a goal and it plans the steps, chooses tools, and works through the task in a cloud environment without constant supervision.
- **Deliverable generation:** Produces finished artifacts such as slide decks, functional websites, small games, and formatted documents rather than raw text.
- **Wide Research:** Runs broad, multi-source investigations and compiles the findings into a structured report.
- **Media creation:** Generates images, video, and music as part of a larger task or as standalone outputs.
- **Integrations and API:** Connects to Slack for task dispatch, offers mobile and desktop apps, and exposes an API for embedding agent work in your own stack.

**Strengths:** Delivers finished artifacts instead of only conversational answers; Works across web, mobile, desktop, Slack, and API surfaces; Wide Research handles broad multi-source investigations well; Team plans and SSO make it usable in organizational settings

**Limitations:** Autonomous runs consume credits and can pursue the wrong approach if the brief is vague; Complex tasks benefit from human review at checkpoints; Output quality varies by task type, especially outside common formats

[Profile (Markdown)](https://openaitools.app/tool/manus.md)

## Promptr

Promptr targets a very specific cost problem: long prompts sent to large language models are billed by the token, and repeating the same lengthy instructions hundreds of times adds up quickly. Its API takes the text of a prompt and returns a denser, compressed form that keeps the meaning while dropping redundant wording. The site's own example compresses a 136-token instruction about an AI agent designing a toy company website down to roughly 75 tokens, which it reports as a 45% reduction.

Integration is deliberately minimal. You POST your prompt as a JSON string to the compress endpoint with an API token in the header, and you receive the compressed text back as plain text. Documentation provides ready-made snippets for curl, Python, JavaScript and C#, so the service can be inserted as one extra step before your existing model call. You can also control behaviour inline with tags: wrap text in a promptr compress="false" block to leave it untouched and unbilled, or set an explicit ratio such as 0.5 or 0.8 to decide how aggressively a section is shortened.

The pricing page frames the value in terms of savings. At $1 per 1M input tokens processed by Promptr, it illustrates a one-off gpt-4-turbo prompt costing $10 without compression versus $7 with it, roughly 30% saved, and larger savings when a prompt is reused many times. Access currently runs through a waitlist. Because compression is lossy by nature and output quality is task-dependent, the sensible approach is to benchmark Promptr on your own prompts before rolling it out broadly.

**Pricing & Plans:** Pay-as-you-go at $1 per 1M input tokens processed, with no hidden costs according to the site. Sections marked as uncompressed are not billed. Access is currently gated behind a waitlist.

- **One-call prompt compression:** Send your prompt to a single REST endpoint and receive a shorter version back as plain text, ready to pass to your model.
- **Up to ~40% cost savings:** The vendor reports up to 40% lower LLM input cost, with a worked example showing a 45% reduction on a sample prompt.
- **Multi-language snippets:** Copy-paste examples are provided for curl, Python, JavaScript and C#, so integration takes minutes rather than days.
- **Inline ratio control:** Wrap sections in tags to exclude them from compression or to set a specific compression ratio for that block.
- **Uncompressed sections are free:** Text marked to be left alone is not billed, letting you protect critical instructions from being altered.

**Strengths:** Simple single-endpoint API that is easy to slot into existing code; Clear, explicit pay-as-you-go price of $1 per 1M input tokens; Code snippets for four common languages reduce integration effort; Inline tags let you protect sensitive or critical text from compression

**Limitations:** Access requires joining a waitlist rather than signing up directly; Compression is inherently lossy, so it can change model output quality; Real savings depend on your prompt length and reuse, so benefits must be tested case by case

[Profile (Markdown)](https://openaitools.app/tool/promptr.md)

## Frequently Asked Questions

### How do Manus and Promptr compare in monthly traffic?

Manus has 19,195,779 monthly visits, while Promptr has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Manus has a month-over-month growth rate of -1.7%; Promptr has 0%.

### How does pricing compare?

Manus: Free Trial. Free trial available, with paid subscription tiers and a business plan for teams and higher usage. Credit allowances and seat details change, so check the official pricing page. Promptr: Pay-as-you-go. Pay-as-you-go at $1 per 1M input tokens processed, with no hidden costs according to the site. Sections marked as uncompressed are not billed. Access is currently gated behind a waitlist.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [ChatGPT](https://openaitools.app/tool/chatgpt-4) | 6,048,022,723 | +2.2% | Freemium | Provides powerful reasoning agents and deep research, but lacks Manus's specialized continuous cloud workspace execution. | [.md](https://openaitools.app/tool/chatgpt-4.md) |
| 2 | [Claude](https://openaitools.app/tool/claude-2) | 1,253,439,413 | +23.8% | Freemium | Features advanced computer use and interactive artifacts, but operates primarily as a chat assistant rather than a dedicated autonomous agent platform. | [.md](https://openaitools.app/tool/claude-2.md) |
| 3 | [Muse](https://openaitools.app/tool/muse-ai-1) | 45,751,822 | +228.3% | Contact for Pricing | Executes tasks using cloud virtual Linux machines, but focuses more on personal desktop automation than complex multi-app research. | [.md](https://openaitools.app/tool/muse-ai-1.md) |
| 4 | [Hermes-Agent](https://openaitools.app/tool/hermes-agent-1) | 3,638,741 | -5.4% | Freemium | Provides open-source personal task automation, but lacks the polished cloud sandbox environment of Manus. | [.md](https://openaitools.app/tool/hermes-agent-1.md) |
| 5 | [Runable](https://openaitools.app/tool/runable) | 1,048,370 | -1.9% | Freemium | Automates multi-step workflows and asset creation, but with fewer built-in general browser navigation tools. | [.md](https://openaitools.app/tool/runable.md) |
| 6 | [Skywork.ai](https://openaitools.app/tool/skywork-ai) | 1,021,069 | -11.8% | Freemium | Converts prompts into structured office deliverables, but acts primarily as a document generation workspace. | [.md](https://openaitools.app/tool/skywork-ai.md) |
| 7 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Open-source, memory-efficient fine-tuning and local model running | [.md](https://openaitools.app/tool/unsloth-ai.md) |
| 8 | [LibreChat](https://openaitools.app/tool/librechat) | 346,879 | -3.5% | Free | Unites multiple models and custom agents in a self-hosted workspace, but lacks pre-built autonomous execution sandboxes. | [.md](https://openaitools.app/tool/librechat.md) |
| 9 | [Lindy](https://openaitools.app/tool/lindy) | 323,329 | -0.2% | Freemium, Contact for Pricing | Deploys autonomous business agents for operational work, but targets enterprise workflows rather than general open-ended research. | [.md](https://openaitools.app/tool/lindy.md) |
| 10 | [Hyperagent](https://openaitools.app/tool/hyperagent) | 182,523 | -51.8% | Contact for Pricing | Deploys agent fleets to build business deliverables, but requires more manual setup for complex web research. | [.md](https://openaitools.app/tool/hyperagent.md) |

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Source: https://openaitools.app/compare/manus-vs-promptr

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