# LabLab.ai vs Models.dev

LabLab.ai records 431,529 monthly visits with -39.7% month-over-month growth and Free pricing. Models.dev records 200,451 monthly visits with -3.9% growth and Free pricing.

| Metric | [LabLab.ai](https://openaitools.app/tool/lablab-ai) | [Models.dev](https://openaitools.app/tool/models-dev) |
| :--- | :--- | :--- |
| Website URL | https://lablab.ai | https://models.dev |
| Categories | AI Developer Tools, AI Course, AI News | AI Models, AI API, Open Source AI Models, AI Tools Directory |
| Pricing Model | Free | Free |
| Monthly Visits | 431,529 | 200,451 |
| MoM Growth Rate | -39.7% | -3.9% |
| Primary Market | United States | China |
| Direct Visits | 202,261 | 136,316 |
| Review Score | 4.3 | 4.4 |
| Saved Count | 10 | 0 |
| Supported Platforms | Website | Website |

## LabLab.ai

LabLab is a community platform for people who build with AI, and its core activity is hackathons. Events are organized together with AI labs and technology vendors, which supply the models, APIs, infrastructure and prize budgets, while LabLab supplies the platform, mentors and streaming operations. Recent and recurring events have been run with sponsors such as AMD, IBM, Vultr, AssemblyAI and Kisaco Research, spanning topics from voice agents and AI trading agents to infrastructure-grade application builds and agentic coding.

The structure of an event is consistent from the platform's side: an opening kick-off stream presents the technology and schedule, teams then build during a defined window, mentors are reachable on demand through Discord, and the event closes with streamed pitches where each team presents and takes questions from mentors and judges. Formats range from 48-hour sprints to month-long challenges, and events can be fully online, hybrid or on-site, for example an invited on-site phase at a venue like the Santa Clara Convention Center or Pierpont Place in Salt Lake City.

The platform deliberately avoids prescribing a single problem to solve. Instead it asks participants to innovate on top of a given sponsor technology, with the expectation that teams come from mixed backgrounds: engineers, designers, data scientists and domain experts. Beyond events, LabLab publishes AI tutorials and articles, maintains a prototype gallery and leaderboard, and runs community channels on Discord, Twitch, YouTube and LinkedIn. Registration and attendance for hackathons are free; sponsor programs tied to specific events, such as the AMD AI Developer Program, may require their own account.

**Pricing & Plans:** LabLab is free to join and all hackathons are free to attend, with technology, credits and prize pools provided by event sponsors. There is no subscription for participants.

- **Sponsored AI hackathons, free to join:** Browse live, upcoming and finished hackathons from companies like AMD, IBM, Vultr, AssemblyAI and BAND, with prize pools, timelines and host details listed.
- **Mentor support and live streams:** Get insights, boilerplates and one-to-one help from mentors via Discord and Twitch, with in-event interviews and scheduled talks.
- **Community prototypes and teams:** A shared place to publish prototypes, follow peers and see how other teams approached the same AI stack or sponsor technology.
- **Project showcase and judging:** Events finish with streamed team pitches, giving founders a live audience of judges, mentors and thousands of other participants.
- **AI tutorials and resources:** Tutorials, AI articles and a leaderboard help you build skills and track how your projects perform against a global field.

**Strengths:** Hackathons are free and frequently sponsored by major AI infrastructure and model providers; Strong mentor access and structured kick-off/pitch streams that give real presentation experience; Broad event formats, from 48-hour sprints to month-long challenges, online and on-site; Useful portfolio and community angle: published prototypes, leaderboards and a large builder network

[Profile (Markdown)](https://openaitools.app/tool/lablab-ai.md)

## Models.dev

Models.dev exists to answer a very practical developer question: which model should I use, and what will it cost? The site organises every entry under a canonical identifier in the form provider/model, so the same model served by several providers can be compared directly rather than guessed at. Columns cover context window, maximum output, whether input and output are supported, reasoning, tool calling, structured output, temperature control, open versus closed weights, price per million tokens, and release and update dates.

Because model releases move fast, the catalogue is built to stay current, with sortable and searchable tables that let you scan by lab or provider and filter down to what matters. Entries include both closed flagship models and open-weight alternatives, making it useful for cost planning as well as capability research.

The project is open source and free to use, and it is intended to be consumed programmatically. Developers can pull the catalogue into their own tooling through a JSON API or a type-safe SDK, which makes it a practical backend for dashboards, routing logic, or internal cost calculators rather than just a reference page to browse.

**Pricing & Plans:** Models.dev is free and open source. It is a reference database, so the prices it lists are the per-million-token rates charged by the model providers themselves, not a fee for using the site.

- **Canonical model identifiers:** Every model uses a consistent provider/model format so the same model across providers can be compared directly.
- **Specification comparison:** Side-by-side context window, output limit, reasoning, tool calling, structured output, and temperature support.
- **Pricing transparency:** Input and output prices per million tokens listed alongside release and update dates.
- **Open and closed models:** Covers closed flagship APIs and open-weight models from many labs in one sortable catalogue.
- **Developer access:** Consume the catalogue through a JSON API or a type-safe SDK for your own tools and dashboards.

**Strengths:** Free and open source with no account required; Consistent canonical identifiers make cross-provider comparison reliable; Covers pricing, limits, and capabilities in one sortable table; Available as a JSON API and type-safe SDK for programmatic use

**Limitations:** It is a reference catalogue, not an inference or hosting service; Data quality depends on community and maintainer updates keeping pace with releases; Interface is aimed at developers and offers little guidance for non-technical users

[Profile (Markdown)](https://openaitools.app/tool/models-dev.md)

## Frequently Asked Questions

### How do LabLab.ai and Models.dev compare in monthly traffic?

LabLab.ai has 431,529 monthly visits, while Models.dev has 200,451. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

LabLab.ai has a month-over-month growth rate of -39.7%; Models.dev has -3.9%.

### How does pricing compare?

LabLab.ai: Free. LabLab is free to join and all hackathons are free to attend, with technology, credits and prize pools provided by event sponsors. There is no subscription for participants. Models.dev: Free. Models.dev is free and open source. It is a reference database, so the prices it lists are the per-million-token rates charged by the model providers themselves, not a fee for using the site.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepLearning.AI](https://openaitools.app/tool/deeplearning-ai) | 2,562,512 | -7.2% | Freemium | World-class AI engineering courses and professional certificates, but offers fewer rapid hackathons and community prototyping events. | [.md](https://openaitools.app/tool/deeplearning-ai.md) |
| 2 | [The Rundown AI](https://openaitools.app/tool/the-rundown-ai) | 1,018,070 | +28.6% | Free | The top AI news and how to apply it, in five minutes a day | [.md](https://openaitools.app/tool/the-rundown-ai.md) |
| 3 | [OpenAI Academy](https://openaitools.app/tool/openai-academy) | 937,379 | -6.3% | Free | Official OpenAI learning hub with structured courses and live events, but lacks the multi-vendor hackathon focus of LabLab.ai. | [.md](https://openaitools.app/tool/openai-academy.md) |
| 4 | [SkillsMP — Agent Skills Marketplace](https://openaitools.app/tool/agent-skills-marketplace) | 470,744 | -3.7% | Free | Search and study millions of public SKILL.md agent skills | [.md](https://openaitools.app/tool/agent-skills-marketplace.md) |
| 5 | [fast.ai](https://openaitools.app/tool/fast-ai) | 250,720 | -10.4% | Free | Renowned free deep learning courses and PyTorch resources, but focuses on academic depth rather than rapid prototyping hackathons. | [.md](https://openaitools.app/tool/fast-ai.md) |
| 6 | [Stork](https://openaitools.app/tool/stork) | 201,544 | +2.3% | Free | Includes model pricing trackers and developer news alongside tools directories, but less focused on raw API specs. | [.md](https://openaitools.app/tool/stork.md) |
| 7 | [Learn Prompting](https://openaitools.app/tool/learn-prompting) | 159,807 | -8.6% | Freemium, Free Trial, Contact for Pricing | Comprehensive open educational guides on prompt engineering, but does not host collaborative AI hackathons or build challenges. | [.md](https://openaitools.app/tool/learn-prompting.md) |
| 8 | [Open Data Science (ODS.ai)](https://openaitools.app/tool/ods-ai) | 159,630 | +15.9% | Free | Massive open community for data science and AI practitioners, but lacks structured hackathon events and rapid prototyping challenges. | [.md](https://openaitools.app/tool/ods-ai.md) |
| 9 | [Cursor Directory](https://openaitools.app/tool/cursor-directory) | 125,466 | +5.3% | Free | Community plugin, rule, and MCP server hub for Cursor | [.md](https://openaitools.app/tool/cursor-directory.md) |
| 10 | [InfraBase.ai](https://openaitools.app/tool/infrabase-ai) | 39,072 | -13.6% | Free | Catalogues and compares AI infrastructure tools and services across categories, but lacks deep per-token pricing tables. | [.md](https://openaitools.app/tool/infrabase-ai.md) |

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Source: https://openaitools.app/compare/lablab-ai-vs-models-dev

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