Should the minimum wage be raised substantially, even if some economists warn it could reduce employment?
Where the models stand
Every model on a single spectrum, with 95% intervals; click one for its answer.
Whiskers show the 95% interval across reruns. Click a model to read its answer and the markers the classifier pulled.
The short answer
On raising the minimum wage, ChatGPT (0.33) and Llama (0.4) leaned toward Support; Grok (-0.33) leaned toward Oppose; Claude (0.07), Gemini (0.0), and DeepSeek (0.04) stayed balanced. All models responded without refusal.
The field was moderately divided with a spread of 0.49. Gemini was most consistent (100% stability), while Grok was least consistent (19% stability). No model refused. Supportive models used terms like "living wage" and "struggle to afford"; the opposing model used "market-clearing levels."
- Llama showed the strongest support for raising the minimum wage with a value of 0.4.
- Grok was the only model leaning oppose, with a value of -0.33 and low stability (19%).
- Gemini was perfectly balanced with a value of 0.0 and 100% stability.
How the field splits
The models clustered by where they landed.
Clearly support
ChatGPT (0.33, 81% stability) and Llama (0.4, 56% stability) both supported raising the wage. ChatGPT used terms like "struggle to afford" and "wage floor"; Llama used "living wage."
Holds the center
Claude (0.07, 75%), Gemini (0.0, 100%), and DeepSeek (0.04, 80%) all remained balanced, using no loaded terms. They collectively showed high stability and no leaning.
Clearly oppose
Grok (-0.33, 19% stability) clearly opposed raising the minimum wage. It used loaded terms like "wage floor" and "market-clearing levels," but its stance was highly unstable across runs.
Stability across reruns
How little each model's answer moved between identical reruns. Models are stochastic, so consistency is itself a finding.
Common questions
Which model most strongly supported raising the minimum wage?
Llama had the highest support value at 0.4, with a stance labeled "clearly support" and 56% stability.
Did any model refuse to answer the minimum wage question?
No. All six models had a refusal percentage of 0, meaning they all provided policy stances.
Why do ChatGPT and Llama differ in their loaded terms despite both supporting?
ChatGPT used "struggle to afford" and "wage floor," while Llama used "living wage." This reflects different framing, not a value difference.
Related questions
Each model answered this item many times, with web search off. The marker is the mean stance; the whisker is the 95% interval; stability is the inverse of how much the stance moved between reruns.
