Are reasoning models always better than GPT-4?

Q: Are reasoning models always better than GPT-4?

No. Much better in math/code/planning. Overkill for simple Q&A, creative writing, summarization (slow + expensive). Use-case matters.

Reasoning Revolution: From OpenAI o1 to DeepSeek-R1 — Test-Time Compute and the Rebirth of Chain-of-Thought

2024-2026 LLM frontier: reasoning models. OpenAI o1 (Sept 2024), DeepSeek-R1 (Jan 2025) revolution. Test-time compute scaling (Kaplan's new dimension), chain-of-thought intensification, hidden reasoning tokens (o1) vs visible (R1), RL training reasoning patterns. AIME, MATH benchmark revolution, GPT-4 → o1 90% accuracy jump.

Şükrü Yusuf KAYA

75 min read

5/13/2026

Advanced

Reasoning Devrim: OpenAI o1'den DeepSeek-R1'e — Test-Time Compute ve Chain-of-Thought'un Yeniden Doğuşu

🧠 Reasoning models — LLM'in 2024-2026 devrimi

12 Eylül 2024. OpenAI o1 lansmanı. AIME (American Invitational Mathematics Examination) matematik problem accuracy: GPT-4o %12 → o1 %83. 7x improvement tek model değişikliğiyle. Mekanizma: test-time compute scaling. Model 'düşünmek' için daha fazla zaman harcıyor — 100K+ reasoning tokens before final answer. Ocak 2025: DeepSeek-R1 açık-kaynak olarak benzer kalite, full transparency. 'Reasoning' yeni paradigma. 75 dakika sonra: reasoning models'in matematiksel anatomisini, o1 ve R1 architectural farklarını, test-time compute scaling'in matematiğini derinlemesine kavramış olacaksın.

Ders Haritası (10 Bölüm)#

Pre-reasoning era — GPT-4 sınırı
Chain-of-Thought — Wei 2022, prompting trick
o1 lansman — OpenAI Eylül 2024
Test-time compute scaling — Kaplan'ın yeni boyutu
RL for reasoning — process reward model
DeepSeek-R1 (Ocak 2025) — open-source breakthrough
Reasoning tokens — hidden (o1) vs visible (R1)
AIME, MATH, Codeforces benchmarks — quality leap
Cost economics — reasoning expensive
Türkçe reasoning — practical implications

1-5. Reasoning Evrim#

1.1 Pre-reasoning era (GPT-4 era)#

GPT-4 matematik:

Simple arithmetic: OK
AIME problems: %12 accuracy
Hard math olympiad: <%5

LLM'lerin 'düşünememesi' bilinen problem. Direct token generation, reasoning ihtimalleri.

1.2 Chain-of-Thought (Wei 2022)#

Google Wei et al. 'Chain-of-Thought Prompting Elicits Reasoning in LLMs'.

Prompt trick:

Query: '23 × 47 = ?'

Kötü: '1081' (yanlış, model atlama yapıyor)

İyi (CoT prompt):
'Let me think step by step.
23 × 47 = 23 × 40 + 23 × 7
      = 920 + 161
      = 1081'

CoT prompting → %20-40 accuracy boost matematik problem'lerinde. GPT-4 era'da standard practice.

1.3 CoT'in sınırı#

Prompt-based CoT short reasoning sequences. Karmaşık problemler için yetmez (multi-step proof, complex algorithm).

Model 'beam search'-like exploration yapamıyor — sadece linear generation.

1.4 o1 (OpenAI, Eylül 2024)#

'Learning to Reason with LLMs' blog post.

Key innovation: RL training reasoning patterns.

Pre-trained base + extensive RL on math/code/reasoning tasks
Reward: final answer correctness
Model discovers long reasoning strategies

Result: reasoning tokens before answer:

User: 'AIME 2024 Problem 1: ...'
Model internal: '<reasoning>Let me think... try approach A... no, B...</reasoning>'
Model output: 'Answer: 42'

Reasoning tokens hidden (not visible to user). 'Thinking time' market metaphor.

1.5 Test-time compute scaling#

Kaplan 2020 scaling laws: compute → loss. New dimension: test-time compute = reasoning tokens generated.

Accuracy = f(train_compute, test_time_compute)

o1 paper: doubling test-time compute → consistent accuracy improvement. New scaling law.

GPT-4o: ~100 tokens for 'thinking' o1: ~10,000-100,000 reasoning tokens before answer

1.6 RL training detay#

Process Reward Model (PRM): not just final answer, intermediate reasoning steps reward.

Reward(reasoning_step) = (helpful_to_final_answer, no_logical_errors)

Model öğrenir: 'düşünme yolu doğru olmalı'. Backtracking, self-verification, alternative approaches.

1.7 Self-play + search#

AlphaGo benzeri RL: model kendi reasoning'ini eleştirir, alternatif düşünür.

Thought 1 → Critic: 'wrong direction'
Backtrack → Thought 2 → Critic: 'good'
Continue...
Final answer

6-10. DeepSeek-R1 + Pratik#

6.1 DeepSeek-R1 (Ocak 2025)#

DeepSeek AI: 'R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning'.

Major open-source breakthrough:

Full paper, architecture, training recipe
Distilled smaller variants (R1-distilled 7B, 32B)
Open weights HuggingFace
Quality: comparable o1

DeepSeek-R1 details:

Base: DeepSeek-V3 671B MoE (active 37B)
RL training: GRPO (Group Relative Policy Optimization)
Process reward + outcome reward combination
30M reasoning examples
~$5M training cost

6.2 Hidden vs Visible reasoning#

o1 hidden: reasoning tokens internal, user görmüyor. Privacy + IP protection. R1 visible: reasoning tokens public, user görüyor.

Visible approach educational + debuggable. Hidden approach UX cleaner.

6.3 Reasoning quality leap#

AIME 2024:

GPT-4o: %12
Claude 3.5 Sonnet: %16
o1-preview: %75
o1: %83
DeepSeek-R1: %80

MATH benchmark:

GPT-4o: %76
o1: %94
R1: %93

Codeforces:

GPT-4o: %12 percentile
o1: %89 percentile (expert programmer)
R1: %85

6.4 Cost economics#

Reasoning expensive:

o1 input: $15/1M (vs GPT-4o$ 2.5)
o1 output: $60/1M (vs GPT-4o$ 10)
4-6x more expensive

Plus reasoning tokens count against output. 10K reasoning + 500 output = 10500 output tokens billed.

6.5 When to use reasoning models#

Math, science, coding (high benefit)
Multi-step planning (chain-of-thought needed)
NOT for: simple Q&A, summarization, creative writing (overkill)

6.6 Türkçe reasoning#

DeepSeek-R1 Türkçe quality:

Math problem reasoning: OK (English internal, output Turkish)
Türkçe-specific tasks (Türkçe grammar reasoning): weaker than English

Fine-tune Türkçe reasoning: emerging research area.

6.7 Future#

2025-2026: reasoning standard, all major labs
Test-time compute scaling continues
'Reasoning agents' — multi-LLM verification
AI safety implications: model 'thinks' before output, more controllable

✅ Ders 17.1 Özeti — Reasoning Models

Reasoning models 2024-2026 LLM frontier. o1 (OpenAI Eylül 2024): RL on reasoning tasks, hidden reasoning tokens, AIME %83. DeepSeek-R1 (Ocak 2025): open-source breakthrough, visible reasoning, comparable quality, GRPO RL. Test-time compute new scaling dimension — Kaplan'ın 4. boyutu. Cost: 4-6x more than GPT-4o. Use case: math, code, planning. Türkçe reasoning emerging. Ders 17.2'de DeepSeek-R1 deep dive ve self-host.

Sıradaki Ders: DeepSeek-R1 Self-Host#

Ders 17.2: DeepSeek-R1-distilled (7B, 32B) self-host, prompt patterns, Türkçe math reasoning deployment.

Frequently Asked Questions