# Gauth vs Labelbox

Gauth records 7,164,627 monthly visits with +52.4% month-over-month growth and Freemium pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Gauth](https://openaitools.app/tool/gauthmath-com) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://gauthmath.com | https://labelbox.com |
| Categories | AI Homework Helper, AI Math, AI Copilot, AI Writing Assistants | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 7,164,627 | 762,803 |
| MoM Growth Rate | +52.4% | -22.8% |
| Primary Market | United States | United States |
| Direct Visits | 1,267,204 | 370,024 |
| Review Score | 4.2 | 4.4 |
| Saved Count | 24 | 0 |
| Supported Platforms | Website | Website |

## Gauth

Gauth is a study app that turns homework problems into teaching moments. A student photographs a question or pastes it into the web calculator, and the AI returns a structured solution that walks through the reasoning instead of just giving the final number. Coverage is deliberately broad: maths and calculus, statistics, physics, chemistry, biology, economics, literature, business, writing, social science and general-purpose subjects, which makes it useful across very different curricula rather than only for algebra drills.

The platform pairs two layers of help. The AI layer answers quickly and free of charge for a limited number of questions, with detailed step-by-step explanations and tips on the concepts behind each step. On top of that sits a tutor service that connects students to real verified tutors around the clock; the company advertises tens of thousands of experts, high satisfaction rates, fast turnaround on hard problems and access to a large question knowledge base. Gauth also offers an Academy section with courses and a newer Gauth Atlas experience that turns subjects into explorable visual maps grounded in cited sources.

Gauth is available on the web and as a mobile app on both iOS and Android, where it has a large review base. It follows a freemium model: a free tier covers everyday questions, while Gauth AI Pro unlocks unlimited answers and premium features via a free trial followed by a paid subscription. Exact plan prices and trial conditions should be confirmed on the official site.

**Pricing & Plans:** Freemium. A free tier answers a limited number of questions, and Gauth AI Pro offers unlimited answers and premium features, typically starting with a free trial before a paid subscription. Check the official site for current prices and trial terms.

- **Photo-based question solving:** Upload or paste a homework question and receive an answer in seconds, covering maths, sciences, business, writing and social sciences.
- **Step-by-step explanations:** Solutions break the problem into clear steps with reasoning and conceptual tips, so students learn the method rather than only the result.
- **24/7 human tutor service:** When AI output is not enough, students can escalate to verified human tutors for personalised guidance, including help with advanced problems.
- **Gauth Atlas visual learning:** An explorable visual map turns a subject into zoomable layers of related ideas, with each image traced back to a real source.
- **Cross-platform access:** Use Gauth in the browser or through the iOS and Android apps, with an Academy area for structured courses and a large study-resource library.

**Strengths:** Works across a very wide range of subjects, not just mathematics; Step-by-step explanations focus on understanding rather than answer copying; Human tutors are available around the clock for problems the AI cannot resolve; Free tier plus mobile apps on iOS and Android make it easy to try

**Limitations:** Unlimited answers and premium features require the paid Gauth AI Pro subscription; AI-generated solutions can still contain mistakes on unusual or ambiguous problems, so they should be checked; Reliance on instant answers can undermine learning if used without attempting the problem first

[Profile (Markdown)](https://openaitools.app/tool/gauthmath-com.md)

## Labelbox

Labelbox occupies an unusual position in the AI stack: it does not primarily sell a chatbot or a generator, it builds the environments and data that models learn from plus the agents that take over recurring engineering and research work. The homepage claim is direct — frontier AI labs train on Labelbox environments and expert data, while engineering and research teams hand their recurring backlog to agents running on Recursion. A recent Series E of more than $100 million signals a heavily funded bet on that thesis.,For enterprises, the newer half of the product is Recursion. Agents are triggered by real signals — a failing build, a PagerDuty alert, a new dependency release, a fresh model checkpoint, or a Monday morning schedule — and then work in the background until the job is done. The examples the company promotes are concrete deliverables rather than summaries: a tested pull request with a preview link, a postmortem draft in Confluence with every event sourced, an ablation table naming the changes that mattered, or a patch for each exposed service after a critical CVE. A grader agent scores each run, and the graded session leaves behind memories, notes and skills that the next session reads before it acts.,The original foundation remains the data business: labeling, annotation, curation and model evaluation supported by expert human reviewers, used for computer vision, NLP, supervised fine-tuning, RLHF and red teaming. In practice the two halves reinforce each other, since the environments that produce high-quality training data are the same environments where agents learn to do real work. Pricing is enterprise-style, routed through sales conversations and shaped by data volume and agent usage, with no public self-serve tier.

**Pricing & Plans:** Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases.

- **Data labeling and expert annotation:** Structured annotation, curation, model evaluation and human-in-the-loop review across vision, language and generative tasks, used to build the training and eval data behind production models.
- **Autonomous agent sessions:** Recursion agents start on their own from builds, alerts, commits, checkpoints and schedules, then work in the background until the task is finished and reviewable.
- **Domain specialists by function:** Specialist agents cover software engineering, reliability, security, ML research, evals, R&D, product and finance workflows, from flaky test fixes to postmortem drafts and vulnerability patches.
- **Graded outcomes that compound:** Every agent run is scored by a grader, and graded sessions leave memories, notes and skills that the next session reads before acting, so quality compounds over time.
- **Native tool and data integrations:** Agents operate against the tools teams already use, including GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery, Databricks, Vercel and cloud providers.

**Strengths:** Covers both sides of the problem: training and eval data production plus agents that act on recurring work; Backed by a recent $100M+ Series E and adoption by frontier AI labs, indicating real funding and demand; Agent runs produce concrete deliverables with proof — tests, reruns, ablations — rather than unverified suggestions; Integrates with the toolchain teams already run: GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery and Databricks

**Limitations:** No self-serve pricing or open signup: everything routes through a sales conversation, which slows down evaluation; The platform assumes real engineering and research infrastructure (GitHub, cloud providers, data warehouses) to deliver value; Positioning has shifted quickly toward autonomous agent sessions, so teams wanting only labeling workflows may find it heavier than needed

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

## Frequently Asked Questions

### How do Gauth and Labelbox compare in monthly traffic?

Gauth has 7,164,627 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Gauth has a month-over-month growth rate of +52.4%; Labelbox has -22.8%.

### How does pricing compare?

Gauth: Freemium. Freemium. A free tier answers a limited number of questions, and Gauth AI Pro offers unlimited answers and premium features, typically starting with a free trial before a paid subscription. Check the official site for current prices and trial terms. Labelbox: Contact for Pricing. Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases.

## 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 | OpenAI's flagship multimodal assistant powered by GPT-6 and GPT-5.6 for reasoning, coding, and deep research. | [.md](https://openaitools.app/tool/chatgpt-4.md) |
| 2 | [Perplexity](https://openaitools.app/tool/perplexity-ai) | 88,310,223 | -3.7% | Freemium | Real-time AI answer engine and research workspace combining live web citations with frontier reasoning models. | [.md](https://openaitools.app/tool/perplexity-ai.md) |
| 3 | [Studocu](https://openaitools.app/tool/studocu-global) | 39,297,122 | +1.1% | Freemium | Provides extensive shared study notes and AI learning tools, offering broader crowdsourced resources than Gauth's prompt solver. | [.md](https://openaitools.app/tool/studocu-global.md) |
| 4 | [Course Hero](https://openaitools.app/tool/coursehero-com) | 5,163,395 | -1.4% | Freemium | Combines AI homework help with a massive repository of student-uploaded study documents, unlike Gauth's pure solver focus. | [.md](https://openaitools.app/tool/coursehero-com.md) |
| 5 | [Solvely AI](https://openaitools.app/tool/solvely-ai) | 4,526,289 | +134% | Freemium | Offers broad multi-subject homework assistance and exam prep similar to Gauth, but with more comprehensive flashcard tools. | [.md](https://openaitools.app/tool/solvely-ai.md) |
| 6 | [MathGPT](https://openaitools.app/tool/mathgpt-ai-math-solver-animator) | 2,254,286 | +10.3% | Freemium | Provides detailed step-by-step math problem-solving like Gauth, adding animated visual explanations for tricky equations. | [.md](https://openaitools.app/tool/mathgpt-ai-math-solver-animator.md) |
| 7 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Streamlines end-to-end computer vision workflows from annotation to model deployment, but lacks Labelbox's general enterprise agent orchestration. | [.md](https://openaitools.app/tool/roboflow.md) |
| 8 | [Appen](https://openaitools.app/tool/appen-com) | 665,840 | +0.2% | Contact for Pricing | Relies heavily on managed human-in-the-loop workforce services for data validation, offering less self-serve technical tooling than Labelbox. | [.md](https://openaitools.app/tool/appen-com.md) |
| 9 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Delivers enterprise-grade labeling and model evaluation at massive scale, but often requires higher enterprise budgets than Labelbox. | [.md](https://openaitools.app/tool/scale.md) |
| 10 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Offers robust multimodal annotation and workflow automation, featuring slightly more intuitive data curation pipelines for computer vision teams. | [.md](https://openaitools.app/tool/super-annotate.md) |

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Source: https://openaitools.app/compare/gauthmath-com-vs-labelbox

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