The Tool Desk
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What task-aware thinking routing means
Task-aware routing is an application policy: your code estimates the reasoning effort a request needs, then selects a thinking level supported by the model you will call. For example, a short, direct request might be routed to a lower level, while a multi-step analysis might use a higher one. These are policy examples, not universal recommendations.
Google documents per-request thinking-level configuration, but its guide does not describe a built-in task classifier or automatic task-routing feature. Your application must make and maintain that decision.
Set a thinking level in a TypeScript interaction
Google’s JavaScript and TypeScript example uses GoogleGenAI from @google/genai, then passes thinking_level inside generation_config to client.interactions.create. The field name is snake case.
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import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
type Task = "simple" | "standard" | "complex";
function chooseThinkingLevel(task: Task) {
if (task === "simple") return "low";
if (task === "complex") return "high";
return "medium";
}
const task: Task = "standard";
const interaction = await client.interactions.create({
model: "gemini-3.8-flash",
input: "Summarize the supplied material.",
generation_config: {
thinking_level: chooseThinkingLevel(task),
},
});
console.log(interaction.output_text);This demonstrates the wiring, not a universal mapping of task categories to levels. The model ID and example values should be checked against the model you actually deploy and Google’s current [thinking documentation](https://ai.google.dev/gemini-api/docs/thinking).
Design the routing policy
Keep classification separate from the API call so that the policy is explicit, reviewable, and testable. Useful inputs include the reasoning depth a task requires, the acceptable latency, and how costly incomplete output would be. A router can use task labels, application context, or other signals, but the choice of signals and thresholds belongs to your application.
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- Define task categories in terms of work required, not just prompt length. A brief prompt can still request complex reasoning.
- Map each category to a level only after checking the target model’s supported values and default.
- Test representative requests and measure latency, cost, and output quality for your workload before treating a mapping as optimal.
- Handle rejected or unavailable model/configuration combinations rather than assuming every level works with every model.
Google’s guide lists model-specific allowed values and defaults, so a level is not necessarily portable between model IDs. Verify the current configuration for the selected model in the [thinking guide](https://ai.google.dev/gemini-api/docs/thinking).
Account for token limits and incomplete output
max_output_tokens includes thinking tokens. If the interaction reaches that ceiling during reasoning, it may finish with status incomplete and truncated or empty output. Google advises lowering thinking_level to reduce cost or latency instead of setting an artificially small output cap when avoiding truncation matters. See Google’s [Interactions API documentation](https://ai.google.dev/gemini-api/docs/interactions).
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Choose an output-token ceiling with the expected final response in mind, then inspect the returned interaction status and output rather than assuming a successful request always produced a complete answer. A lower thinking level may help control effort, but it should not be treated as a substitute for validating results.
Choose stateful or stateless requests deliberately
The Interactions API stores requests by default to support server-side conversation state. To continue a conversation, provide the earlier interaction’s ID as previous_interaction_id. Set store: false for a stateless request; in that case, your application must manage any needed context. Google’s [Interactions API documentation](https://ai.google.dev/gemini-api/docs/interactions) describes these state options.
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For a multi-turn workflow, decide whether each turn should be reevaluated by the router or whether the conversation should keep a consistent model and thinking-level policy. Reevaluate when the task changes substantially; preserve continuity when later turns depend on a stable setup. This is an application design decision, not an automatic API behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Inspect interaction steps without relying on thought summaries
The TypeScript example in Google’s [Interactions API documentation](https://ai.google.dev/gemini-api/docs/interactions) shows iterating through interaction.steps and checking whether a thought step has a summary. Such summaries can be absent or empty. Code that observes steps should therefore handle missing summaries, and should not treat a thought summary as the final answer; use the interaction’s output for the response.
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