Not All Models Are Equal

Different AI models have different strengths. Using GPT-4 for everything is like using a sledgehammer for every nail—sometimes effective, always expensive, often overkill.

Matching models to tasks improves results and reduces costs.

Model Characteristics

Large models (GPT-4, Claude 3 Opus, Gemini Ultra):

• Best for: Complex reasoning, nuanced analysis, creative work
• Trade-offs: Slower, more expensive
• Use when: Quality matters most, task is complex

Medium models (GPT-3.5, Claude 3 Sonnet, Gemini Pro):

• Best for: Balanced tasks, everyday use
• Trade-offs: Good enough for most things
• Use when: Speed and cost matter, task isn’t extremely complex

Small/Fast models (Claude Haiku, GPT-4o mini):

• Best for: Simple tasks, high volume, real-time applications
• Trade-offs: Less capable on complex reasoning
• Use when: Speed is critical, task is straightforward

Local models (Llama, Mistral via Ollama):

• Best for: Privacy-sensitive data, offline use
• Trade-offs: Depends on model and hardware
• Use when: Data can’t leave your network

Task-Model Matching

Code completion and simple edits: Fast model Quick suggestions, syntax completion, simple refactoring—speed matters more than deep reasoning.

Code review and architecture: Large model Finding subtle bugs, understanding complex patterns, suggesting architectural improvements—this needs reasoning capability.

Summarization: Medium or fast model Extracting key points from documents doesn’t require the most powerful model.

Analysis and synthesis: Large model Combining information from multiple sources, identifying patterns, drawing conclusions—complex reasoning tasks.

Translation and formatting: Fast model Straightforward transformation tasks that don’t require creative thinking.

Creative writing: Large model (usually) Nuance, voice, originality—benefits from more capable models.

Data extraction: Medium or fast model Pulling structured information from unstructured text is usually straightforward.

Cost Considerations

Model costs vary dramatically:

Using large models for everything can cost 10-20x more than appropriate model selection.

Quality vs. Speed vs. Cost

Every model choice involves trade-offs:

          Quality
             ^
             |
    Large *  |
             |
  Medium  *  |
             |
    Small    *> Speed/Cost

Choose based on what matters for your task.

Multi-Model Workflows

Sophisticated workflows use different models for different steps:

1. Triage (fast model): Classify incoming request
2. Research (medium model): Gather relevant information
3. Analysis (large model): Deep reasoning on complex parts
4. Response (medium model): Draft the output
5. Review (fast model): Check for issues

This approach gets large-model quality where it matters, fast-model speed elsewhere.

Calliope’s Multi-Model Support

In Calliope, switch models based on task:

Chat Studio:

%calliope chat -m claude [complex question]
%calliope chat -m gpt4o [quick question]

AI Lab:

%calliope ask-sql -m gpt4o [simple query]
%calliope ask-sql -m claude [complex analysis]

Deep Agent: Configure which models agents use for different subtasks.

When to Use Local Models

Local models via Ollama make sense when:

• Data is sensitive and can’t leave your network
• You’re working offline
• You want zero API costs
• You’re learning/experimenting

Trade-off: Local models are generally less capable than cloud APIs, but the gap is narrowing.

Testing Model Performance

Before committing to a model for a workflow:

1. Test the same prompt on multiple models
2. Compare output quality
3. Measure response time
4. Calculate cost per query
5. Choose the minimum viable model

Don’t assume the largest model is best. Test and verify.

The Model Selection Checklist

When choosing a model:

• How complex is the task?
• How important is output quality?
• What’s the acceptable latency?
• What’s the cost budget?
• Does data need to stay local?
• Is this high-volume or occasional?

Right model, right task, right results.

Try multi-model workflows in Calliope →