# Adept vs Aquin

Adept records 12,261 monthly visits with -46.3% month-over-month growth and Contact for Pricing pricing. Aquin records 0 monthly visits with 0% growth and Contact for Pricing pricing.

| Metric | [Adept](https://openaitools.app/tool/adept) | [Aquin](https://openaitools.app/tool/aquin) |
| :--- | :--- | :--- |
| Website URL | https://www.adept.ai | https://www.aquin.app |
| Categories | AI Agent, AI Workflow, AI Productivity Tools, AI Document Extraction | AI Browsers, AI Search Engine, AI Assistant, AI Speech-to-Text |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 12,261 | 0 |
| MoM Growth Rate | -46.3% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 3,793 | 0 |
| Review Score | 4.2 | 4.1 |
| Saved Count | 3 | 0 |
| Supported Platforms | Website | Website |

## Adept

Adept positions itself as an ML research and product lab: instead of stopping at chat-style answers, it builds models that act. The core idea is an "action transformer" that observes a user's screen and carries out multi-step workflows in existing software through natural-language instructions, so teams do not have to rebuild their tooling around a new interface.

The company's public work includes the ACT-1 model for taking actions in browsers, the openly released Persimmon family of permissively licensed language models, and engineering contributions such as FlashAttention research on efficient long-sequence transformer training. This mix of applied agent research and foundational ML work is what distinguishes Adept from single-purpose automation scripts.

Adept has been backed by substantial venture funding, including a $350M Series B round, and targets enterprise teams that want to delegate repetitive, high-volume processes — data entry, document handling, form filling and cross-application workflows — to an agent. Because the technology is delivered through enterprise engagements, exact commercial terms are not published on the website; teams generally reach out directly to discuss a pilot.

**Pricing & Plans:** Adept does not publish self-serve pricing. Commercial access is arranged through direct enterprise conversations and pilots, so cost depends on scope, deployment and support requirements.

- **Action transformers (ACT):** Models trained to observe a screen and carry out multi-step actions inside existing software, not just describe what to do.
- **Natural-language task delegation:** Users state a goal in plain language and the agent maps it to concrete clicks, keystrokes and navigation steps.
- **Persimmon open-source LLMs:** A permissively licensed language model family released publicly for research and developer use.
- **Cross-application workflows:** Automation spans browsers and desktop tools, so agents can bridge systems that were never designed to talk to each other.
- **Foundational ML research:** Published work on efficient transformer training and long-sequence attention underpins the product line.

**Strengths:** Research depth is unusual: applied agent work plus open foundational models; Agents act inside existing software, avoiding large migration projects; Publicly released Persimmon models give developers something concrete to test; Well-funded and backed by recognised investors

**Limitations:** No transparent self-serve pricing or instant sign-up; Public product and blog updates are sparse, so current capabilities are hard to verify; Enterprise-only motion makes it impractical for individuals or small teams

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

## Aquin

Aquin is best understood as a research lab rather than a single consumer app. The homepage describes the team as reverse engineering intelligence through interpretability, and frames its goal as applied prediction that compounds over time - systems that understand what a model has learned and that teams can point at their own data. The flagship applied product is Aleph, a universal predictor spanning tables, media, annotation and decisions.\n\nWhat separates Aleph from a general-purpose assistant is its output contract. It produces schema-bound results for programs: probabilities, labels, bounding boxes and scores, each carrying confidence so that uncertain items can be escalated rather than silently guessed. The lab positions this as 'one base, any modality, any task', fine-tuned into a specific workload and improving as more labelled work arrives.\n\nAround that core, Aquin publishes applied research on interpretability. Published work includes an interpreter for attention-as-routing-graph circuits from a single forward pass, tiled SVD methods for locating usable weight mechanisms, agentic weight-editing experiments on small models, and simulations of LLM behaviour in varied environments. Separate projects like the axi self-learning agent and the aq developer environment target people building and refining foundation models.

**Pricing & Plans:** Aquin does not publish a public price list. The site mixes freely readable research with applied engagements, so commercial terms are discussed directly with the team.

- **Universal predictor (Aleph):** A single base that handles tables, media, annotation and decision tasks, with the option to fine-tune toward your specific data.
- **Schema-bound outputs:** Outputs are typed and scored rather than free text, so downstream programs can act on results and escalate low-confidence cases.
- **Interpretability research:** Treats attention as a routing map from a single forward pass, isolating a small subgraph aimed at the answer and testing it under ablation.
- **Weight editing experiments:** Agentic weight-editing experiments that localise causal layers, apply rank-one MLP updates and validate then roll back steps when checks fail.
- **Self-learning developer agent:** An agent for controlling developer environments and running coding or research loops with recorded lessons and rejected paths.

**Strengths:** Research-grade interpretability work published openly alongside the applied product; Outputs designed for programs rather than chat, which suits automation pipelines; Explicit confidence scores make it practical to escalate uncertain predictions; One base is claimed to cover tables, media, annotation and decisions

**Limitations:** Aquin Labs is a research organisation, so most output is papers and interfaces rather than a polished consumer app; Aleph is described as a predictor for programs and teams, not a general chat assistant; No transparent public price list was found for commercial engagements; Material is heavily technical and assumes familiarity with ML evaluation work

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

## Frequently Asked Questions

### How do Adept and Aquin compare in monthly traffic?

Adept has 12,261 monthly visits, while Aquin has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Adept has a month-over-month growth rate of -46.3%; Aquin has 0%.

### How does pricing compare?

Adept: Contact for Pricing. Adept does not publish self-serve pricing. Commercial access is arranged through direct enterprise conversations and pilots, so cost depends on scope, deployment and support requirements. Aquin: Contact for Pricing. Aquin does not publish a public price list. The site mixes freely readable research with applied engagements, so commercial terms are discussed directly with the team.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | High-efficiency open-weight and hosted reasoning AI powered by DeepSeek-V4 and R1 at ultra-low token costs. | [.md](https://openaitools.app/tool/deepseek.md) |
| 2 | [Grok](https://openaitools.app/tool/grok) | 203,846,050 | -0.8% | Freemium | xAI's real-time AI assistant and coding workspace powered by Grok 4.7 with live X integration. | [.md](https://openaitools.app/tool/grok.md) |
| 3 | [Muse](https://openaitools.app/tool/muse-ai-1) | 45,751,822 | +228.3% | Contact for Pricing | Your personal AI agent from Meta that actually gets things done | [.md](https://openaitools.app/tool/muse-ai-1.md) |
| 4 | [n8n](https://openaitools.app/tool/n8n) | 4,522,449 | -7.4% | Freemium | Open-source workflow automation built for AI agents and technical teams | [.md](https://openaitools.app/tool/n8n.md) |
| 5 | [TraeWork](https://openaitools.app/tool/traework) | 2,785,249 | -7.8% | Contact for Pricing | ByteDance's AI-native workspace for natural-language task execution | [.md](https://openaitools.app/tool/traework.md) |
| 6 | [WorkBuddy](https://openaitools.app/tool/workbuddy) | 2,341,262 | +43.9% | Freemium | An AI work partner that turns a sentence into finished office deliverables | [.md](https://openaitools.app/tool/workbuddy.md) |
| 7 | [MindStudio](https://openaitools.app/tool/mindstudio) | 1,793,337 | +14.2% | Contact for Pricing | No-code builder for custom AI agents and workflows | [.md](https://openaitools.app/tool/mindstudio.md) |
| 8 | [Dia Browser](https://openaitools.app/tool/dia-browser) | 1,254,172 | +108.4% | Free | The AI browser that reads between your tabs | [.md](https://openaitools.app/tool/dia-browser.md) |
| 9 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | The environments frontier AI trains on, and the agents that do the recurring work | [.md](https://openaitools.app/tool/labelbox.md) |
| 10 | [Eigent AI](https://openaitools.app/tool/eigent-ai) | 575,703 | +1.4% | Freemium, Free Trial, Contact for Pricing | Open-source desktop AI workforce that runs browser, file, and app tasks for you | [.md](https://openaitools.app/tool/eigent-ai.md) |

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Source: https://openaitools.app/compare/adept-vs-aquin

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