# huntr vs Models.dev

huntr records 45,283 monthly visits with -61.9% month-over-month growth and Free pricing. Models.dev records 200,451 monthly visits with -3.9% growth and Free pricing.

| Metric | [huntr](https://openaitools.app/tool/huntr-com) | [Models.dev](https://openaitools.app/tool/models-dev) |
| :--- | :--- | :--- |
| Website URL | https://huntr.com | https://models.dev |
| Categories | AI Developer Tools, Open Source AI Models, AI Models | AI Models, AI API, Open Source AI Models, AI Tools Directory |
| Pricing Model | Free | Free |
| Monthly Visits | 45,283 | 200,451 |
| MoM Growth Rate | -61.9% | -3.9% |
| Primary Market | United States | China |
| Direct Visits | 22,903 | 136,316 |
| Review Score | 4.3 | 4.4 |
| Saved Count | 2 | 0 |
| Supported Platforms | Website | Website |

## huntr

huntr takes the mechanics of a bug bounty marketplace and points them at artificial intelligence and machine learning. Rather than chasing generic web vulnerabilities, hunters pick a challenge, review its rules, and try to break a specific AI assistant, agent or model artifact: convincing a claims assistant to leak a hidden token, getting a stock-trading agent to perform unauthorized trades, or slipping past the guardrails of an assistant designed to keep a secret. Each challenge has clear objectives and a prize pot, and progress is reflected in a public leaderboard ranking.

The workflow is deliberately simple and transparent: choose a battle, move up the leaderboard, win cash. Participants compete over a fixed window, then results and writeups are published, so techniques spread across the community. Featured challenges on the site have carried pots in the range of $15,000. The platform also runs an active blog covering LLM jailbreaks, prompt-injection style bypasses and zero-day intelligence in AI/ML components, including work on Keras model deserialization and TensorFlow Lambda exploits.

huntr is free for researchers to use and is supported by Palo Alto Networks and Prisma AIRS, its AI security platform. That sponsorship structure matters: huntr functions both as a community hunting ground and as a channel for surfacing real vulnerabilities in the AI supply chain, which organizations can then remediate. There is also a Discord community and a FAQ for participants.

**Pricing & Plans:** Free

- **AI/ML-focused challenges:** Time-boxed hacking challenges targeting LLM assistants, agentic systems and model artifacts, each with explicit objectives and rules.
- **Cash prize pots and leaderboards:** Compete over a fixed window to climb public rankings and win real cash prizes for top placements.
- **Published results and writeups:** After a challenge closes, outcomes and techniques are shared so the community can learn from each exploit.
- **Model and library vulnerability hunting:** Research coverage extends to ML supply chain targets such as Keras model deserialization and TensorFlow Lambda exploits.
- **Community and knowledge base:** Discord server, blog and FAQ support researchers from first challenge through advanced rankings.

**Strengths:** Purpose-built for AI/ML security rather than repurposed web bug bounties; Real cash prizes with a transparent, gamified leaderboard; Backed by Palo Alto Networks and Prisma AIRS; Public writeups turn each challenge into a learning resource

**Limitations:** Rewards are concentrated in the top ranks of each challenge; Challenge availability rotates, so there are windows with limited targets; Focus on competition means less emphasis on long-running private disclosure programs

[Profile (Markdown)](https://openaitools.app/tool/huntr-com.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 huntr and Models.dev compare in monthly traffic?

huntr has 45,283 monthly visits, while Models.dev has 200,451. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

huntr has a month-over-month growth rate of -61.9%; Models.dev has -3.9%.

### How does pricing compare?

huntr: Free. Free 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 | [Code Arena](https://openaitools.app/tool/code-arena) | 35,827,928 | +19.4% | Free | Battle-test AI coding models side by side | [.md](https://openaitools.app/tool/code-arena.md) |
| 2 | [Outlier AI](https://openaitools.app/tool/outlier-ai) | 10,372,036 | +7.6% | Paid | Get paid to train the next generation of AI models | [.md](https://openaitools.app/tool/outlier-ai.md) |
| 3 | [clickworker](https://openaitools.app/tool/clickworker-com) | 1,861,149 | +0.5% | Contact for Pricing | Crowdsourcing data services for AI training and market data | [.md](https://openaitools.app/tool/clickworker-com.md) |
| 4 | [LabLab.ai](https://openaitools.app/tool/lablab-ai) | 431,529 | -39.7% | Free | Free AI hackathons and a global community for AI builders | [.md](https://openaitools.app/tool/lablab-ai.md) |
| 5 | [fast.ai](https://openaitools.app/tool/fast-ai) | 250,720 | -10.4% | Free | Free practical deep learning courses, research and open-source libraries | [.md](https://openaitools.app/tool/fast-ai.md) |
| 6 | [Surge AI](https://openaitools.app/tool/surgehq-ai) | 245,142 | +6.6% | Contact for Pricing | Expert human data and evaluation for training frontier AI | [.md](https://openaitools.app/tool/surgehq-ai.md) |
| 7 | [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) |
| 8 | [Open Data Science (ODS.ai)](https://openaitools.app/tool/ods-ai) | 159,630 | +15.9% | Free | Community hub for data science, machine learning and AI practitioners | [.md](https://openaitools.app/tool/ods-ai.md) |
| 9 | [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) |
| 10 | [CalcForge](https://openaitools.app/tool/calcforge) | 31,360 | -4.8% | Free, Paid | Open-source engineering calculators for civil and structural design | [.md](https://openaitools.app/tool/calcforge.md) |

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Source: https://openaitools.app/compare/huntr-com-vs-models-dev

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