# Models.dev vs Multi-Agent Arena

Models.dev records 200,451 monthly visits with -3.9% month-over-month growth and Free pricing. Multi-Agent Arena records 5,242 monthly visits with -78% growth and Free pricing.

| Metric | [Models.dev](https://openaitools.app/tool/models-dev) | [Multi-Agent Arena](https://openaitools.app/tool/multi-agent-arena) |
| :--- | :--- | :--- |
| Website URL | https://models.dev | https://olamlabs.ai |
| Categories | AI Models, AI API, Open Source AI Models, AI Tools Directory | AI Testing, AI Agent, AI Models, AI Research Tool |
| Pricing Model | Free | Free |
| Monthly Visits | 200,451 | 5,242 |
| MoM Growth Rate | -3.9% | -78% |
| Primary Market | China | United States |
| Direct Visits | 136,316 | 1,953 |
| Review Score | 4.4 | 4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## 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)

## Multi-Agent Arena

Multi-Agent Arena sits inside Olam Labs' broader effort to scale simulated worlds for frontier AI labs. The premise is that richer simulations drive more capable and safer models, so the team builds environments where several models interact at once and their behaviour can be measured rather than merely observed in a single chat. Games provide the shared rules and incentives that make social behaviour comparable across models.

The suite covers titles with genuinely different payoff structures: Diplomacy, where alliances and betrayals shape outcomes; Poker, which tests bluffing and reading opponents; Risk, which mixes long-horizon planning with temporary cooperation; and other negotiation scenarios. Because each game creates its own pressures, the same model can look cooperative in one and self-interested in another, and the resulting data is what the evaluations are built on. Public releases have reported metrics such as broken promise rates and relative share of spoils across model families.

Access is free, and the site publishes essays, release notes, and evaluation write-ups alongside the arena itself. Olam Labs frames the work around research throughput, arguing that infrastructure and grading capacity are the real bottlenecks in studying multi-agent behaviour, and it collaborates with labs and researchers on reinforcement learning, evaluation, and post-training use cases.

**Pricing & Plans:** Free. There is no paid tier advertised; the arena and published research material are openly available.

- **Multiple games with distinct incentives:** Diplomacy, Poker, Risk, and other negotiation scenarios each create different pressures, so a model's social behaviour can be compared across contexts.
- **Multi-agent evaluation data:** Matches generate measurable outcomes such as broken promise rates and share of spoils, turning vague impressions of model personality into comparable numbers.
- **Designed for model comparison:** Several models can be placed in the same scenario, making head-to-head differences in cooperation, deception, and negotiation visible.
- **Published research and release notes:** The team documents findings, updates, and collaborations so results can be read alongside the arena itself.
- **Free to use:** The arena is open at no cost, aimed at researchers, builders, and anyone curious about multi-agent dynamics.

**Strengths:** Free access with no account barrier described; Games give structured, comparable incentives that plain chat evaluation cannot provide; Covers both cooperative and adversarial dynamics in one environment; Backed by published research and release notes from the Olam Labs team

**Limitations:** It is a research and evaluation environment rather than a polished consumer game; Interpretation of results requires some familiarity with agent evaluation; Behaviour in a specific game does not directly predict real-world deployment behaviour

[Profile (Markdown)](https://openaitools.app/tool/multi-agent-arena.md)

## Frequently Asked Questions

### How do Models.dev and Multi-Agent Arena compare in monthly traffic?

Models.dev has 200,451 monthly visits, while Multi-Agent Arena has 5,242. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Models.dev has a month-over-month growth rate of -3.9%; Multi-Agent Arena has -78%.

### How does pricing compare?

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. Multi-Agent Arena: Free. Free. There is no paid tier advertised; the arena and published research material are openly available.

## 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 | [Design Arena](https://openaitools.app/tool/design-arena) | 949,534 | -13.2% | Free | Crowdsourced arena where AI models compete and humans crown the best design output | [.md](https://openaitools.app/tool/design-arena.md) |
| 3 | [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) |
| 4 | [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) |
| 5 | [AI Agent Store](https://openaitools.app/tool/ai-agent-store) | 169,388 | -5.4% | Freemium, Free Trial, Paid | Marketplace to find, hire, or build AI agents for real work | [.md](https://openaitools.app/tool/ai-agent-store.md) |
| 6 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) |
| 7 | [Coarena by Coasty](https://openaitools.app/tool/coarena-by-coasty) | 59,004 | -33.5% | Free | Benchmark computer- and phone-use AI agents with blind head-to-head battles | [.md](https://openaitools.app/tool/coarena-by-coasty.md) |
| 8 | [huntr](https://openaitools.app/tool/huntr-com) | 45,283 | -61.9% | Free | The bug bounty platform built for AI and ML security | [.md](https://openaitools.app/tool/huntr-com.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 | [Museum of Models (Rival)](https://openaitools.app/tool/rival) | 9,187 | -35% | Free | Archives how various AI models answer identical prompts over time, offering qualitative comparison rather than pricing. | [.md](https://openaitools.app/tool/rival.md) |

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Source: https://openaitools.app/compare/models-dev-vs-multi-agent-arena

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