Compare AI models.
An AI model is the system that generates an answer. Choose a task to compare how different models answered it, how long they took and what they cost.
Choose “Helping customers” for a support example. No AI requests are sent when you browse these results.
Each prepared scenario is tested up to five times per model. An AI judge rates the answers and can be wrong. Use the results to choose what to test in your own project.
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Loading model results and quality evaluations from the public benchmark database.
Estimate your prompt costThe same task.
Each benchmark states the task, settings and number of samples, so you can see what was compared.
The evidence.
Individual runs show measured tokens, time, cost and MiniMax’s quality score with a short comment. Partial results and failed attempts are included.
Your workload.
Results apply to the prepared task and date shown. Estimate your own workload before choosing a model. Estimate prompt cost →
Benchmarks refresh every 15 seconds. Quality is an automated rubric rating, not a probability of correctness. Costs are USD. Your tests always stay private. They never appear here.
Test method, prompts and limits
Five repeats use the same fixed input for each task, not five independently sampled tasks. Scores measure these prepared cases and are not a universal ranking. MiniMax M3 is an automated judge and can make mistakes; hiding model names reduces one source of bias.
Read sample counts and completion status beside each score. Failed, ungraded and truncated attempts are distinct. A completion is not itself a verified correct answer, and an AI rubric score is not a deterministic valid-answer rate. Request times are client-observed; provider usage and confirmed charges are separate from estimates.
Inspect the actual prompts, synthetic fixtures, reference answers and rubrics below, or download the frozen task references. Reference answers are examples of acceptable answers, not measured candidate outputs. Candidate outputs are not published in this release; individual-run evidence includes metrics and evaluator comments.
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