# LLMBoard.ai vs Models.dev

LLMBoard.ai records 378 monthly visits with 0% month-over-month growth and Free pricing. Models.dev records 200,451 monthly visits with -3.9% growth and Free pricing.

| Metric | [LLMBoard.ai](https://openaitools.app/tool/llmboard-ai) | [Models.dev](https://openaitools.app/tool/models-dev) |
| :--- | :--- | :--- |
| Website URL | https://www.llmboard.ai | https://models.dev |
| Categories | Large Language Models (LLMs), AI Models | AI Models, AI API, Open Source AI Models, AI Tools Directory |
| Pricing Model | Free | Free |
| Monthly Visits | 378 | 200,451 |
| MoM Growth Rate | 0% | -3.9% |
| Primary Market | United States | China |
| Direct Visits | 124 | 136,316 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## LLMBoard.ai

LLMBoard.ai answers a question that changes every few weeks: which model should we actually use? It maintains a continuously updated catalogue of model records and benchmark results, then presents them as leaderboards organised by task — overall chat and LLM, agentic work, coding, reasoning, math, knowledge, instruction following, plus separate tracks for vision, image generation and editing, video, text-to-speech, speech-to-text and embeddings. Each ranking is backed by named benchmarks such as GPQA, MMLU-Pro, AIME, SWE-Bench Verified, LiveCodeBench, MATH, HumanEval and MMMU-Pro.

What distinguishes it from a single-number ranking is that it deliberately separates signals. Capability scores sit apart from price, runtime latency and provider reliability, so a model that tops a benchmark is not automatically presented as the best value or the most dependable at scale. There are dedicated pricing pages for chat tokens, images, video and audio, plus speed and reliability views. A scoring and data section documents how models are mapped and scored, and each benchmark page shows raw model order and coverage so you can judge the evidence behind a position.

The site is aimed at engineering teams, product managers and researchers evaluating models for a specific workload, as well as anyone tracking how open models compare with frontier proprietary ones. It is an information and comparison resource — it does not host the models themselves, and results should be treated as a starting point for your own testing.

**Pricing & Plans:** Free to browse. The leaderboard, benchmark library, model directory, pricing comparisons and scoring documentation are openly accessible without a subscription. Token prices shown on the site are the vendors' own per-model API prices compiled for comparison, not a charge from LLMBoard.ai itself.

- **Task-Specific Leaderboards:** Separate rankings for overall chat, coding, reasoning, math, knowledge, instruction following, agentic tasks, vision, image, video, audio and embeddings, so you compare models on the work you actually do.
- **Benchmark Library:** Individual evaluations such as GPQA, MMLU-Pro, AIME, SWE-Bench Verified, LiveCodeBench, MATH, HumanEval and MMMU-Pro, each with raw model order and coverage details.
- **Price and Value Comparison:** Dedicated pricing views for chat tokens, image, video and audio, plus cost-versus-capability framing to identify where extra spend actually buys better output.
- **Runtime and Reliability Data:** Output speed and latency rankings alongside provider reliability signals, covering the operational side of model choice rather than only benchmark scores.
- **Model Directory and Scoring Docs:** A filterable catalogue by vendor, type, modality and price, with published scoring, coverage and model-mapping rules so rankings can be audited.

**Strengths:** Free to use across the entire leaderboard, benchmark library and pricing data; Deliberately separates capability, price, runtime and reliability instead of collapsing them into one score; Wide modality coverage extends well beyond chat models to image, video, audio and embeddings; Publishes its scoring and mapping methodology so rankings can be inspected rather than taken on faith

**Limitations:** Benchmark positions are synthetic test results and may not predict performance on your own data; Rapid model releases mean pages can lag behind the newest announcements at any given moment; No direct model hosting or API access; it is a research and comparison tool only

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

LLMBoard.ai has 378 monthly visits, while Models.dev has 200,451. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

LLMBoard.ai has a month-over-month growth rate of 0%; Models.dev has -3.9%.

### How does pricing compare?

LLMBoard.ai: Free. Free to browse. The leaderboard, benchmark library, model directory, pricing comparisons and scoring documentation are openly accessible without a subscription. Token prices shown on the site are the vendors' own per-model API prices compiled for comparison, not a charge from LLMBoard.ai itself. 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 | [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) |
| 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 | [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) |
| 6 | [Frontier Model Forum](https://openaitools.app/tool/frontiermodelforum-org) | 10,545 | +33.5% | Contact for Pricing | An industry non-profit advancing the safety and security of frontier AI models | [.md](https://openaitools.app/tool/frontiermodelforum-org.md) |
| 7 | [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) |
| 8 | [LLM Price Check](https://openaitools.app/tool/llm-price-check) | 8,255 | -34.7% | Free | Tracks per-token API prices across major AI providers in a simple comparison table, but omits broader model specs. | [.md](https://openaitools.app/tool/llm-price-check.md) |
| 9 | [OpenGPT](https://openaitools.app/tool/opengpt-com) | 3,559 | -24.7% | Freemium | Helps compare foundation model performance and AI stacks, but functions more as a decision workspace than raw database. | [.md](https://openaitools.app/tool/opengpt-com.md) |
| 10 | [thisorthis.ai](https://openaitools.app/tool/thisorthis-ai) | 2,250 | -43.7% | Free | Vote for the best AI each month and follow the live leaderboard | [.md](https://openaitools.app/tool/thisorthis-ai.md) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)