Model Selection
Pick the best model for research synthesis
The model parameter on POST /research lets you choose which LLM synthesizes the final answer. If you omit it, Caesar automatically selects a model based on your query.
Start by omitting model to let Caesar auto-select. Set model when you need consistent output, a specific latency profile, or a fixed provider.
Supported models
The model parameter controls research synthesis. Retrieval and source gathering remain the same.
When to use each model
gpt-5.2
Best for
- Complex, multi-step reasoning
- Cross-domain technical synthesis
- High-stakes decisions that need the strongest accuracy
Trade-offs
- Typically higher latency than Flash-tier models
gemini-3-pro
Best for
- Deep analysis in code, math, or STEM topics
- Long-context synthesis over large documents or datasets
- Balanced quality for strong reasoning in technical research
Trade-offs
- Slower than Flash for simple or high-throughput workloads
gemini-3-flash
Best for
- Large scale processing and batch research
- Low latency, high volume workloads
- Agentic or iterative tasks that need fast turns
Trade-offs
- Less depth than Pro or Opus on very complex analysis
claude-opus-4.5
Best for
- Long form synthesis and narrative quality
- Nuanced analysis and careful reasoning
- Research outputs that need strong readability
Trade-offs
- Higher latency than smaller or Flash-tier models