Model Selection

Pick the best model for research synthesis

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

Model	Provider	Summary
`gpt-5.2`	OpenAI	Highest quality synthesis for complex, multi-step research tasks
`gemini-3.1-pro`	Google	Latest Gemini Pro-tier reasoning model for deep, long-context synthesis
`gemini-3-pro`	Google	Advanced reasoning with strong long context performance
`gemini-3-flash`	Google	Best performance for high-volume, low-latency research
`claude-opus-4.6`	Anthropic	Strongest Claude-tier synthesis quality for long-form, nuanced analysis

gemini-3-pro remains supported for backward compatibility. Prefer gemini-3.1-pro for new integrations.

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-3.1-pro

Best for

Deep analysis in code, math, or STEM topics
Long-context synthesis over large documents or datasets
Highest Gemini-tier quality for technical and research-heavy prompts

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-4.6

Best for

Maximum Claude-tier synthesis quality
Highly nuanced argumentation and editorial polish
Long-form outputs where tone and structure matter

Trade-offs

Higher latency than Flash-tier and many non-Opus models

Example

1 {
2   "query": "Compare major approaches to carbon capture and their performance",
3   "model": "gemini-3-flash",
4   "reasoning_loops": 2
5 }

Quick picker

Goal	Recommended model
Fast, high volume research	`gemini-3-flash`
Strong reasoning with long context	`gemini-3.1-pro`
Maximum accuracy on complex research	`gpt-5.2`
Maximum Claude-tier writing quality	`claude-opus-4.6`

Learn more

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

Model	Provider	Summary
`gpt-5.2`	OpenAI	Highest quality synthesis for complex, multi-step research tasks
`gemini-3.1-pro`	Google	Latest Gemini Pro-tier reasoning model for deep, long-context synthesis
`gemini-3-pro`	Google	Advanced reasoning with strong long context performance
`gemini-3-flash`	Google	Best performance for high-volume, low-latency research
`claude-opus-4.6`	Anthropic	Strongest Claude-tier synthesis quality for long-form, nuanced analysis

gemini-3-pro remains supported for backward compatibility. Prefer gemini-3.1-pro for new integrations.

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-3.1-pro

Best for

Deep analysis in code, math, or STEM topics
Long-context synthesis over large documents or datasets
Highest Gemini-tier quality for technical and research-heavy prompts

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-4.6

Best for

Maximum Claude-tier synthesis quality
Highly nuanced argumentation and editorial polish
Long-form outputs where tone and structure matter

Trade-offs

Higher latency than Flash-tier and many non-Opus models

Example

1 {
2   "query": "Compare major approaches to carbon capture and their performance",
3   "model": "gemini-3-flash",
4   "reasoning_loops": 2
5 }

Quick picker

Goal	Recommended model
Fast, high volume research	`gemini-3-flash`
Strong reasoning with long context	`gemini-3.1-pro`
Maximum accuracy on complex research	`gpt-5.2`
Maximum Claude-tier writing quality	`claude-opus-4.6`

1	{
2	"query": "Compare major approaches to carbon capture and their performance",
3	"model": "gemini-3-flash",
4	"reasoning_loops": 2
5	}