# Listen Labs vs Models.dev

Listen Labs records 1,254,632 monthly visits with +22.1% month-over-month growth and Contact for Pricing pricing. Models.dev records 200,451 monthly visits with -3.9% growth and Free pricing.

| Metric | [Listen Labs](https://openaitools.app/tool/listen-labs) | [Models.dev](https://openaitools.app/tool/models-dev) |
| :--- | :--- | :--- |
| Website URL | https://listenlabs.ai | https://models.dev |
| Categories | AI Interview Assistant, AI Research Tool, AI Summarizer, AI Report Generator | AI Models, AI API, Open Source AI Models, AI Tools Directory |
| Pricing Model | Contact for Pricing | Free |
| Monthly Visits | 1,254,632 | 200,451 |
| MoM Growth Rate | +22.1% | -3.9% |
| Primary Market | United States | China |
| Direct Visits | 620,749 | 136,316 |
| Review Score | 4.5 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Listen Labs

Listen Labs is a research platform built around a simple premise: the bottleneck in customer research is rarely the questions, it is the cost and time of asking them properly. The platform lets you describe a business goal, then drafts the study — objectives, questions, and the probing context an experienced moderator would bring — or you can upload an interview guide you already use. From there it runs real conversations with participants at a scale that would be impractical with human moderation, and compresses the resulting transcripts into structured findings.

The output is designed for decisions rather than archive. Readouts surface ranked preferences between concepts, quote-level evidence for each insight, hesitation moments, emotional tone and — notably — the gaps between what respondents claim they value and what they actually choose. That say/do analysis is the part most survey tooling cannot produce, because it depends on observing behaviour inside a conversation rather than tabulating answers.

Participation comes from a qualified global network spanning many markets and languages, which means multi-country studies can run without separate recruiting pipelines. The company serves brands and product teams running concept tests, usability studies, brand perception work and messaging research, and following its acquisition the platform operates as part of Salesforce, with enterprise-grade controls for consent, privacy and scale. Access is arranged through a demo rather than self-serve signup.

**Pricing & Plans:** Quote-based. Listen Labs is sold as an enterprise research platform with pricing arranged through a demo, reflecting study design, sample size and market coverage. No public self-serve price list.

- **AI-moderated interview design:** Describe your objective and the platform drafts objectives, likely questions and probing context, or you can upload your own interview guide.
- **Global participant recruitment:** Studies are filled from a qualified global network covering many markets and languages, with targeting to match your audience.
- **Automated analysis and reporting:** Responses are turned into ranked preferences, insight quotes and theme summaries, so readouts are ready without manual coding.
- **Emotional and behavioural signals:** Beyond words, the platform captures hesitation, emotional tone and gaps between what people say and what they do.
- **Enterprise research controls:** Security, consent and privacy controls built for organisations running research at scale across teams and regions.

**Strengths:** Turns an objective into a moderated study and a finished readout, cutting the cycles between question and decision; Runs conversational interviews at a scale human moderation cannot match, in multiple languages and markets; Surfaces say/do gaps, hesitation and emotional signals rather than only tabulated answers; Global participant network removes most of the recruiting and screening work from the researcher

[Profile (Markdown)](https://openaitools.app/tool/listen-labs.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 Listen Labs and Models.dev compare in monthly traffic?

Listen Labs has 1,254,632 monthly visits, while Models.dev has 200,451. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Listen Labs has a month-over-month growth rate of +22.1%; Models.dev has -3.9%.

### How does pricing compare?

Listen Labs: Contact for Pricing. Quote-based. Listen Labs is sold as an enterprise research platform with pricing arranged through a demo, reflecting study design, sample size and market coverage. No public self-serve price list. 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 | [Outset.ai](https://openaitools.app/tool/outset-ai) | 518,287 | +18.4% | Contact for Pricing | Focuses on enterprise-grade AI-moderated qualitative research and simulated studies, rivaling Listen Labs' interview workflows. | [.md](https://openaitools.app/tool/outset-ai.md) |
| 2 | [Voxpopme](https://openaitools.app/tool/voxpopme-com) | 301,522 | +38.5% | Contact for Pricing | Enterprise consumer research platform specializing in video surveys and AI-driven thematic analysis reports. | [.md](https://openaitools.app/tool/voxpopme-com.md) |
| 3 | [Dovetail](https://openaitools.app/tool/dovetail) | 238,937 | -5.1% | Contact for Pricing | Excels at organizing and synthesizing unstructured customer feedback into searchable insights, though it lacks live AI interview moderation. | [.md](https://openaitools.app/tool/dovetail.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 | [UserCue](https://openaitools.app/tool/usercue) | 108,096 | +277.4% | Contact for Pricing | Expert-led, AI-native primary research for private equity, healthcare and life sciences — consulting-grade studies delivered in about two weeks. | [.md](https://openaitools.app/tool/usercue.md) |
| 6 | [Koji](https://openaitools.app/tool/koji-ai-native-research-platform) | 61,640 | +13.9% | Paid, Contact for Pricing | AI-led customer interviews and research insights at scale | [.md](https://openaitools.app/tool/koji-ai-native-research-platform.md) |
| 7 | [Voiceform](https://openaitools.app/tool/voiceform) | 54,210 | +5.6% | Contact for Pricing | Combines structured surveys with AI-moderated voice and video interviews, offering a similar conversational research approach. | [.md](https://openaitools.app/tool/voiceform.md) |
| 8 | [Enterpret](https://openaitools.app/tool/enterpret) | 51,665 | -14.1% | Contact for Pricing | Unifies and analyzes multichannel customer feedback into actionable insights, but lacks live interactive interview moderation. | [.md](https://openaitools.app/tool/enterpret.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 | [Voicepanel](https://openaitools.app/tool/voicepanel) | 27,176 | -12.6% | Contact for Pricing | Customer and user research at scale, powered by AI conversations | [.md](https://openaitools.app/tool/voicepanel.md) |

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Source: https://openaitools.app/compare/listen-labs-vs-models-dev

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