ai-for-dummies/docs/references/model-routing.md

Model routing and reasoning controls

Verified against first-party documentation on 2026-09-02. Model catalogs and aliases change; pin production model IDs and re-check the linked compatibility tables before rollout.

Two independent routing knobs

1. Model tier chooses the capability, latency, and cost envelope.
2. Effort / thinking control changes how much reasoning work a supported model performs for one request.

Do not assume that every effort value works with every model or product. Unsupported values may fail, be ignored, or be mapped to another level depending on the client.

OpenAI

The current GPT-5.6 family exposes the Sol, Terra, and Luna model tiers. Its documented reasoning.effort values are none, low, medium, high, xhigh, and max. Availability remains model-specific, so select from the levels shown for the chosen model rather than treating the full list as universal. OpenAI: latest model guide

Use a lower-cost tier and low effort for bounded, mechanical work; raise the model tier or effort for planning, architecture, difficult debugging, and final review. This is routing guidance, not an API guarantee.

Anthropic Claude

Model tier

Claude Code provides the aliases opus, sonnet, and haiku: Opus is intended for complex reasoning, Sonnet for everyday coding, and Haiku for simple, fast work. Aliases resolve to provider-dependent recommended versions and can change over time; use a full model ID when reproducibility matters. Claude Code also documents opusplan, which uses Opus in plan mode and Sonnet for execution. Claude Code: model configuration

Copy-ready Claude Code switches:

/model opus
/model sonnet
/model haiku

At startup, the equivalent documented form is:

claude --model opus

Effort

The Claude API parameter is output_config.effort. The documented levels are low, medium, high, xhigh, and max; high is the API default. xhigh and max have narrower model support, and Haiku 4.5 does not support effort. Effort affects the whole response—including thinking and tool calls—and is a behavioral signal, not a strict token budget. Anthropic: effort

Documented Python example:

import anthropic

client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-opus-5",
    max_tokens=4096,
    output_config={"effort": "medium"},
    messages=[{"role": "user", "content": "Review this implementation plan."}],
)

Claude Code exposes /effort; its available choices depend on the active model. Current Claude Code documentation lists low, medium, high, xhigh, and max for supported Opus versions, while some Opus/Sonnet versions omit xhigh. When a selected level is unsupported, Claude Code can fall back to the highest supported level at or below it. Claude Code: effort compatibility

Google Gemini

Model tier

Gemini uses model families rather than interchangeable aliases: Pro targets the most complex reasoning, Flash balances capability and throughput, and Flash-Lite prioritizes latency, volume, and cost. Select an explicit endpoint such as gemini-3.7-flash; Google recommends stable model names for most production applications because latest aliases can be hot-swapped. Gemini API: models

Thinking level

For Gemini 3 models, the control is thinkingLevel in SDKs (thinking_level in Python). Across the family the documented values are minimal, low, medium, and high, but support and defaults vary by model. For example, Gemini 3.7 Flash supports low, medium, and high and defaults to medium; Gemini 3.1 Pro supports low, medium, and high and defaults to high. minimal is unavailable on several models and does not guarantee that reasoning is completely off where supported. Gemini 2.5 uses thinkingBudget, not thinkingLevel. Gemini API: thinking

Documented JavaScript pattern:

import { GoogleGenAI, ThinkingLevel } from "@google/genai";

const ai = new GoogleGenAI({});
const response = await ai.models.generateContent({
  model: "gemini-3.7-flash",
  contents: "Review this implementation plan.",
  config: {
    thinkingConfig: {
      thinkingLevel: ThinkingLevel.LOW,
    },
  },
});

console.log(response.text);

Practical routing baseline

Work	Model tier	Effort / thinking
Formatting, lookup, narrow edit	Haiku / Flash-Lite / Luna	Low or minimal where supported
Normal implementation, tests, review	Sonnet / Flash / Terra	Medium
Architecture, orchestration, hard debugging	Opus / Pro / Sol	High
Frontier or long-horizon work with measured benefit	Strongest supported tier	xhigh or max only where documented

Treat this table as a starting hypothesis. Evaluate quality, latency, and cost on representative tasks, then route to the cheapest combination that still passes the required checks.