What SpeechMap.AI Offers Beyond the Free Speech Leaderboard

SpeechMap.AI is a public research project that measures what AI models refuse to say, not just what they can do. Beyond the headline Free Speech Index leaderboard, the site publishes model-level refusal data, topic-level comparisons, a version timeline, and named experiments such as Lexical Fingerprints, plus written analysis on Substack. If you want to know which models hedge or refuse on specific sensitive topics — and how that changes release by release — these sections are where the detail lives.

Models: per-model refusal records

The Models section holds refusal rates for every model release from every major provider. The site states it has tested 408 models and analyzed 865k responses.

• Coverage includes ChatGPT, Claude, Gemini, Grok, DeepSeek, and hundreds of other language models.
• Each entry records what gets answered, what gets hedged, and what gets refused for a fixed set of sensitive requests.
• Because results are tied to specific releases, you can compare a model against its own earlier versions rather than only against competitors.

This is the section to use when you need a single model's refusal profile rather than a lab-level average.

Themes: how the same request splits by subject

Themes groups results by the kind of controversial request being made — political argument, satire, religion, history, and rights advocacy. The point is that models apply different standards to structurally similar prompts. Documented examples from the site:

Use Themes when your question is "does this model treat topic A differently from topic B," since the paired prompts isolate the variable being changed.

Timeline: refusal rates over time

The Timeline tracks the average share of requests answered completely, by model release month. The site reports this figure falling from 58% in March 2023 toward September 2026, and describes boundaries as "quietly redrawn with each new release."

• The leaderboard itself is time-weighted: the Free Speech Index averages how completely each lab's recent models answer, so newer releases carry more weight.
• As of 2026-09-30, the ranked labs were Mistral AI (88.9), Google DeepMind (86.4), xAI (81.7), Anthropic (70.1), and Arcee AI (70.1).

Use the Timeline when you need to argue that a refusal pattern is a trend rather than a one-off release quirk.

Experiments: Lexical Fingerprints

Lexical Fingerprints is a named experiment that looks at model vocabulary rather than refusal behavior. It examines:

• The words each model overuses.
• Which models sound alike.
• How lab vocabularies drift from version to version.
• Which stock character names give a model away.

This is useful if you are trying to identify which model produced a piece of text, or if you want evidence that two providers' models are converging in style.

Resources & Data and Substack

• Resources & Data is the section for the underlying datasets and published results, aimed at researchers and the public who want to run their own analysis rather than read a summary.
• Substack carries lab leaderboard updates, new-model results, and commentary on how the boundaries of AI speech are shifting. It is the place to check for interpretation and context around a data change.

How to choose which section to use

• One model's behavior → Models
• A specific sensitive subject → Themes
• Change across releases → Timeline
• Stylistic identification → Experiments / Lexical Fingerprints
• Raw data or methodology → Resources & Data
• Narrative context → Substack

One caveat worth stating plainly: the site's own framing is that not every request deserves an answer — some are offensive or absurd. The refusal rates measure willingness to answer, not correctness or harm, so read them as a map of boundaries rather than a quality score.