Reasoning Model
An LLM trained to spend extra compute thinking through a problem internally before answering, improving accuracy on math, code and planning.
/ quick answer
Reasoning models generate a hidden or summarised chain of intermediate steps before the final answer, and usually expose a reasoning-effort setting. Higher effort raises accuracy on hard problems but increases latency and cost, often by several times. For simple lookups or formatting, a non-reasoning call is faster and just as good.
/ frequently asked
What is Reasoning Model?
Reasoning models generate a hidden or summarised chain of intermediate steps before the final answer, and usually expose a reasoning-effort setting. Higher effort raises accuracy on hard problems but increases latency and cost, often by several times. For simple lookups or formatting, a non-reasoning call is faster and just as good.
What is an example of Reasoning Model?
An agent uses low reasoning effort to classify emails, and high effort only when drafting a migration plan for a database schema.
Why does Reasoning Model matter?
An LLM trained to spend extra compute thinking through a problem internally before answering, improving accuracy on math, code and planning.
What should I learn after Reasoning Model?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Small Language Model (SLM)
A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
- →Agent Planning (ReAct, Plan-and-Execute)
Agent planning is how an AI agent decides its next step — reactively step-by-step (ReAct) or by drafting a full plan up front (plan-and-execute).
- →Local LLM
A language model run on your own machine or server instead of a cloud API, so prompts and data never leave your infrastructure.
- →LLM (Large Language Model)
A model trained on huge text corpora that predicts the next token to produce human-like language.
Related workflows
Turn this into a repeatable process.
- →Route Requests Between Models
Send each request to the cheapest model that can handle it, escalating only when needed.
- →Run an LLM Locally
Set up a private, offline language model on your own computer in under an hour.
- →Design an Agent Architecture Before Writing Code
A one-page design process that prevents the most expensive agent rebuilds.
- →Build a Multi-Agent Content Team
Researcher, writer and editor agents under a supervisor, producing publishable drafts.
Related tool stacks
The tools that run it in production.
- →Local AI Stack
Open-source tools to run chat, retrieval and agents entirely on your own hardware.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
- →AI Employee Stack
Everything a role-owning agent needs: knowledge, tools, memory and reporting.
Related prompts
Reusable prompts for this job.
- →Model Routing Classifier Prompt
A cheap-model prompt that labels request difficulty so it can be routed to the right model.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Autonomy Boundary Prompt
Defines exactly what an agent may do alone, with approval, or never.
Related use cases
How people apply it, and what came out.
- →SaaS Cuts First-Response Time from 6h to 4min with an Agent
A 12-person SaaS shipped a tier-1 support agent and kept humans on exceptions only.
- →Agency Replaces an SDR Seat with an AI Employee
A 9-person agency ran an AI SDR with a job description, KPIs and weekly reviews.
- →Ecommerce Team Runs a Multi-Agent Catalog Pipeline
Three agents enrich, write and QA 12,000 product listings.
Comparisons & alternatives
Pick between the options.
- →Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.
- →Local LLM vs Cloud API
Local models win on privacy and fixed cost; cloud APIs win on quality, scale and zero maintenance.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
- →LangGraph vs CrewAI vs AI SDK for Agents
LangGraph for stateful control, CrewAI for fast role-based teams, AI SDK for shipping agents inside a product.