LOGISTAR — Scheda Oggetto

Dati oggetto

URL

Apri

Titolo / Descrizione

Sito / Azienda

Estratto / Body

What is a Large Reasoning Model (LRM)?

Let's decompose the name:

LRM = Large + Reasoning + Model

Large - it has billions of parameters, often in the range of 7B to 700B+, just like a normal Large Language Model.
    Reasoning - it does not jump straight to the answer. It produces a long chain of intermediate steps first.
    Model - it is still a neural network that predicts the next token.

So, a Large Reasoning Model is a Large Language Model that has been specially trained to spend extra tokens thinking before it gives the final answer.

These models are also called reasoning models or thinking models. They all mean the same thing.

Examples we may have heard of: OpenAI's GPT-5.5 Thinking, DeepSeek R1, Qwen3, Google's Gemini 3 with Deep Think, Anthropic's extended thinking mode in Claude, and xAI's Grok 4.1 with Think mode.
LLM vs LRM

The best way to understand LRMs is to compare them with a regular LLM.

Let's say we ask both a small word problem. We will reuse this same problem throughout the blog to keep the comparisons clean.

A shop sells pens at 2 for 5 rupees, and notebooks at 3 for 12 rupees. We buy 6 pens and 9 notebooks. How much do we pay in total?

A regular LLM may answer in 1 second, generate just a few tokens like "51 rupees", and sometimes get the answer wrong because it tries to guess in one shot.

A Large Reasoning Model takes a different path. It first thinks - working out the per-unit cost, multiplying, adding, and verifying the total - and only then writes the final answer. For a small problem like this, the trace may use a few hundred reasoning tokens. For harder problems, it can stretch into thousands or tens of thousands of tokens.

The reasoning tokens are usually hidden from the end user. We only see the final answer. But the model paid the cost of thinking, and that is why the answer is more accurate.

A quick visual of what each one is doing:

LLM:   [question] ---> [short answer]            (fast, sometimes wrong)

LRM:   [question] ---> [long thinking trace]
                          ---> [short answer]    (slow, more often right)

Here, we can see that the LRM adds a thinking step in the middle, which the LLM skips entirely.

Let me tabulate the differences between LLM and LRM for your better understanding so that you can decide which one to use based on your use case.
Aspect	Standard LLM	Large Reasoning Model (LRM)
Answering style	Direct, one shot	Thinks first, answers later
Tokens used	Few (100 to 500)	Many (5,000 to 50,000)
Latency	1 to 3 seconds	10 to 120 seconds
Cost per query	Low	10x to 30x higher
Strength	Simple chat, summaries, drafting	Math, code, logic, planning
Weakness	Hard multi-step problems	Slow and expensive for easy tasks

Both are useful, just for different jobs.
How does an LRM actually think?

This was all about the difference between LLMs and LRMs. Now, let's understand what really happens inside an LRM when it thinks.

The thinking inside an LRM is just more text. There is no magic new component. The model writes out its thought process as tokens, the same way it writes any other tokens.

This long block of internal text is often called the reasoning trace or thinking trace. For our pen and notebook question above, it looks somewhat like below:

<think>
Pens: 2 pens cost 5 rupees, so 1 pen costs 2.5 rupees.
6 pens cost 6 * 2.5 = 15 rupees.

Notebooks: 3 notebooks cost 12 rupees, so 1 notebook costs 4 rupees.
9 notebooks cost 9 * 4 = 36 rupees.

Total = 15 + 36 = 51 rupees.

Let me verify. 6 pens means 3 packs of 2 pens, each pack 5 rupees, so 3 * 5 = 15. Correct.
9 notebooks means 3 packs of 3 notebooks, each pack 12 rupees, so 3 * 12 = 36. Correct.
Total 15 + 36 = 51. Correct.
</think>

We pay 51 rupees in total.

Here, we can see three things happening:

The model breaks the problem into smaller steps.
    It tries an approach, computes intermediate values, and writes them down.
    It checks its own answer at the end before giving the final reply.

This is the same pattern a careful student follows on a rough sheet during an exam. The rough sheet is the reasoning trace. The final answer is what the student writes on the answer sheet.

A simple flow diagram:

The reasoning trace is hidden in many products (like GPT-5.5 Thinking, where we only see a short summary), and visible in some others (like DeepSeek R1).

We have a complete program on Reasoning Models, LLM Internals, and Chain of Thought (CoT) Prompting - check out our AI and Machine Learning Program at Outcome School.

Note

Voto oggetto (1-10)

Precisione (1-10)

Immagine

Tag (7)

✎ Modifica tag 🤖 Suggerisci AI

          ai:5, llm:5, tool:5, fine-tuning:5, machine learning:5, lrm:5        

ai 5 fine-tuning 5 llm 5 lrm 5 machine learning 5 tool 5 wpg 5

ID: 68080 • Tipo: WPG • Reg: 2026-07-14 • Mod: 29/07/2026 20:16