4 agents vs 16 agents: choosing the right level of effort
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Two configurations, two uses
Grok’s multi-agent system offers two main configurations that determine how many agents are deployed for your request. This choice affects research depth, response time, and token usage all at once. It is the only setting that truly shapes the model, and it is worth thinking about before every call instead of hard-coding a default in your code.
| Configuration | Agents | Best use | xAI SDK | OpenAI SDK |
|---|---|---|---|---|
| Fast | 4 | Targeted research, direct questions | agent_count=4 | effort="low" or "medium" |
| Deep | 16 | Exhaustive research, complex topics | agent_count=16 | effort="high" or "xhigh" |
The 4-agent configuration
With 4 agents, you deploy a small, focused team. It is the right size for factual questions like “What are the latest open-source AI models of 2026?”, for simple comparisons like “Compare Claude API vs GPT-5.6 pricing”, or for a bounded search such as “Find xAI’s latest announcements this week”. It is also the configuration to choose whenever response time is part of the experience—a chat interface, an assistant built into an app.
The four agents split the work quickly, latency stays contained, and token usage remains reasonable. For a request you run hundreds of times a day, that last point actually becomes the deciding factor, even before answer quality.
The 16-agent configuration
With 16 agents, you deploy a full team for exhaustive research. This configuration makes the most sense for a complex comparative analysis—“Compare the top 5 cloud GPU providers by performance, price, availability, and GDPR compliance”—for a state-of-the-art review such as “Survey the state of the art in RAG architectures in 2026”, or for a multi-source investigation like “Analyze the open-source community’s reaction to Meta’s latest licenses”. What these three requests have in common: completeness matters more than speed, and each has enough facets to keep sixteen researchers busy.
The trade-off is financial, and you need to plan for it. The 16 agents cover much more ground, but token usage is significantly higher: a single 16-agent request can easily use 10 to 50 times more tokens than a standard request. Running a series of tests in deep mode out of curiosity is the best way to discover an unexpected bill at the end of the month.
How the mapping works
You don’t set the number of agents the same way across SDKs. The xAI SDK exposes a direct parameter, agent_count, that you set to 4 or 16. The OpenAI SDK and the REST API go through reasoning.effort: "low" and "medium" trigger 4 agents, while "high" and "xhigh" trigger 16.
Be careful not to generalize this mapping. It is specific to the multi-agent system. On other Grok models, reasoning.effort controls the model’s reasoning budget, not the number of agents deployed—the same parameter, two different meanings depending on the model you call.
Choosing the right configuration
Four questions are enough to decide in most cases. Is the question simple or complex? Do you need completeness or speed? Is the token budget constrained? Is the user waiting for the answer on screen, or is the processing asynchronous? In each pair, the first option leads to 4 agents and the second to 16.
When in doubt, start with 4 agents. This rule beats a long up-front reasoning process: run the request, read the answer, and if it clearly lacks depth or misses a dimension of the topic, run it again with 16. You’ll have spent a small request to avoid, if needed, a big one.
Key points to remember
- 4 agents = fast, targeted research, moderate usage
- 16 agents = exhaustive research, high token usage
- The mapping goes through
agent_count(xAI SDK) orreasoning.effort(OpenAI SDK) - low/medium → 4 agents, high/xhigh → 16 agents
- Start with 4 agents and scale up if needed