The AMA AI Model Picker Toolkit is a simple spreadsheet that helps you choose the right AI model for what your specific tasks and priorities. You tell it what AI capabilities matters most to you—such as speed, accuracy, creativity, or handling long documents—and it does the comparison for you.
Based on real-world performance data and practical judgment, the tool scores and ranks popular AI models and clearly shows which one fits your needs best. You don’t need technical knowledge or hours of research—just adjust your priorities and get a clear recommendation.
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Why Use the AMA AI Model Picker Toolkit
- Personalized Model Selection
- Ranks AI models based on how closely their capabilities match your chosen priorities, rather than generic or one-size-fits-all ratings.
- Transparent, Customizable Criteria
- Lets you clearly set the importance of up to 13 capability categories (e.g., speed, context handling, cost), allowing objective, scenario-based evaluation.
- Instant, Weighted Comparisons
- Calculates a weighted score for each model using both your input and publicly available model capability data—no manual calculations required.
- Flexible Scenario Testing
- Quickly adjust importance levels and immediately see how model recommendations change, supporting rapid experimentation for different use cases.
- Quickly adjust importance levels and immediately see how model recommendations change, supporting rapid experimentation for different use cases.
How to Use the AMA AI Model Picker Toolkit
- Navigate to the “Picker” Tab
- Begin by finding the “Picker” tab in the spreadsheet.
- Set Capability Priorities
- In the “Importance (1-5)” column, enter a value (0–5) for each capability based on how essential it is to your use case (e.g., 5 = essential, 0 = not important).
- Review Model Scoring
- The toolkit instantly calculates weighted scores for each model in the “Models” tab by multiplying your priority values with each model’s rated capability.
- Read the Model Recommendation
- At the bottom of the “Picker” tab, the sheet displays the name of the AI model with the highest weighted score as the suggested best fit.
- Explore Alternative Scenarios
- Adjust the importance ratings at any time to test recommendations for different workflows or requirements.
AMA AI Model Picker Toolkit Features & Capabilities
| Feature | Description |
|---|---|
| Model Capability Ratings | Contains up-to-date ranking (1–5) of each supported model’s strengths across 13 key categories. |
| Priority Input Column | Allows users to assign 0–5 values to each capability based on their current context or needs. |
| Instant Weighted Scoring | Uses formulas to calculate a weighted total for each model, reflecting user priorities. |
| Model Recommendation | Automatically displays the model with the highest weighted score as the best fit. |
| Scenario Flexibility | Importance values can be changed on-the-fly for immediate recalculation and comparison. |
Common Challenges Solved by This Toolkit
| Problem | How the Toolkit Helps |
|---|---|
| Overwhelmed by too many AI model options | Uses a structured, side-by-side comparison based on real capabilities, not hype |
| Unclear which features actually matter | Lets users weight only the capabilities relevant to their workflow |
| Time-consuming, manual model research | Provides instant, evidence-based recommendations without custom benchmarking |
| Inconsistent or subjective selection logic | Applies transparent, repeatable scoring for objective decisions |
| Unsure which model to start with | Converts priorities into a single clear “Suggested Model” |
| Inconsistent comparisons across providers | Normalizes models across companies using a shared 1–5 capability scale |
| Over-focusing on one factor (cost, speed, etc.) | Uses weighted scoring across multiple dimensions to balance tradeoffs |
| Hard to justify choices to stakeholders | Produces a clear, auditable rationale tied to weighted priorities |
| Different workflows need different models | Allows fast re-scoring for new use cases to surface better-fit models |
Tip:
For highly specialized tasks, try toggling “Importance” ratings for less-critical capabilities to zero. This sharpens the recommendation and reduces noise from model features that don’t impact your outcome.