Head-to-Head Model Comparison

GPT-5.2 Codex vs Devstral 2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

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GPT-5.2 CodexProfile

Devstral 2Profile

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OpenAI

GPT-5.2 Codex

Otonom Ajan ve Araç Modeli

OpenAI tarafından geliştirilen GPT-5.2 Codex; otonom ajan iş akışları, araç çağırma (tool calling) ve çok adımlı otomasyon için tasarlanmış olup 400K bağlam penceresi, 118 token/sn hız, 66/100 zeka indeksi ve 1M token başına $1.75/$14 fiyat

Intelligence Index66 / 100

Input Token:

$1.75 / 1M

Output Token:

$14.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

78.1%

Mistral AI

Devstral 2

Otonom Ajan ve Araç Modeli

Mistral AI tarafından geliştirilen Devstral 2; otonom ajan iş akışları, araç çağırma (tool calling) ve çok adımlı otomasyon için tasarlanmış olup 262K bağlam penceresi, 70 token/sn hız, 56/100 zeka indeksi ve 1M token başına $1.5/$4.5 fiyat

Intelligence Index56 / 100

Input Token:

$1.50 / 1M

Output Token:

$4.50 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

58.2%

Bir Bakışta Temel Farklar

Devstral 2 is 2.3x cheaper for API usage (input + output tokens combined).
GPT-5.2 Codex has a larger context window (400K vs 262K tokens).
GPT-5.2 Codex is faster at 118 tok/s vs 70 tok/s.
GPT-5.2 Codex leads on SWE-bench coding (78.1% vs 58.2%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	GPT-5.2 Codex	Devstral 2
Intelligence Index Composite reasoning & knowledge score (0-100)	66	56
Provider	OpenAI	Mistral AI
Input Price Cost per million input tokens	$1.75 / 1M	$1.50 / 1M
Output Price Cost per million output tokens	$14.00 / 1M	$4.50 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.87 / 1M	$0.99 / 1M
Context Window Maximum input + output tokens	400K tokens~600 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	128K	66K
Throughput Speed Tokens generated per second	118 tok/s	70 tok/s
SWE-bench Verified Real-world software engineering	78.1%	58.2%
GPQA Diamond Graduate-level reasoning	78.2%	66.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.17 / 1M	$0.35 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

GPT-5.2 Codex

+19.9% SWE-bench lead

Cost

Devstral 2

2.3x cheaper monthly runtime

Speed

GPT-5.2 Codex

+69% faster generation

Context

GPT-5.2 Codex

400K tokens window

Doğrulanmış Benchmark Puanları

Official evaluation metrics verified by independent third-party research labs.

GPT-5.2 CodexDevstral 2

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

78.1%vs58.2%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

78.2%vs66.5%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

87.4%vs83.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1398ptsvs1365pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

13.8%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

53.5%vs52%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

48.5%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

76.5%vsN/A

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

93.8%vs78%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

82%vs82%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

118 tok/s vs70 tok/s

Gecikme Süresi ve Yanıt Hızı

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

GPT-5.2 Codex1.28s

Devstral 20.52s

Est. End-to-End Response (500 tokens)TTFT + generation time

GPT-5.2 Codex5.52s

Devstral 27.66s

Gerçek Zamanlı Token Maliyet Simülatörü

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

GPT-5.2 Codex Monthly API Bill

$15.49 / month

$1.75/1M in · $14/1M out

Devstral 2 Monthly API Bill

$6.82 / month

$1.5/1M in · $4.5/1M out

Devstral 2 saves approximately $8.66 / month (56%) under this workload.2.3x Savings

GPT-5.2 Codex · Strengths & Key Advantages

400K bağlam penceresi ve yanıt başına maksimum 128K çıktı tokenı kapasitesi (SWE-bench Verified 78.1% · GPQA Diamond 78.2%)
Sadece 1280 ms ilk token gecikmesi (TTFT) ile saniyede 118 token yüksek üretim hızı
İstem önbellekleme (Prompt Caching), araç çağırma ve yapılandırılmış JSON çıktısını destekleyen rekabetçi API ekonomisi (1M token başına $1.75/$14 fiyatlandırma)

Considerations & Trade-offs

Kapalı ağırlıklı bulut modelidir; çevrimdışı yerel sunuculara indirilerek çalıştırılamaz
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Devstral 2 · Strengths & Key Advantages

262K bağlam penceresi ve yanıt başına maksimum 66K çıktı tokenı kapasitesi (SWE-bench Verified 58.2% · GPQA Diamond 66.5%)
Sadece 520 ms ilk token gecikmesi (TTFT) ile saniyede 70 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (123B)

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	GPT-5.2 Codex	Devstral 2
Cost per Task Estimated USD to finish one task	$2.20	$0.65
Output Tokens / Task Tokens generated per task	60K	42K
Reasoning Tokens / Task Thinking tokens burned per task	34K	20K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-5.2 Codex	Devstral 2
Official API Model ID (SDK)	gpt-5-2-codex	devstral-2-latest
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-12	2025-12
Knowledge Cutoff	August 2025	October 2025
Architecture	Code-specialised long-horizon reasoning model	Advanced Agentic Code Transformer
Parameters	Confidential	123B
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Flat rate ($1.75/1M in) across full 400K window	Flat rate ($1.5/1M in) across full 262K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.175/1M (no extra cache-write fee)	Cached input at $0.35/1M tokens
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Native Web Search tool + Hosted Code Interpreter & File Search RAG	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16)
Safety & Frontier Classification	OpenAI Preparedness Framework Audited & System Card Published	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text
Output Modalities	text	text

GPT-5.2 Codex vs Devstral 2: Hızlı Yanıtlar (SSS)

Which is more intelligent, GPT-5.2 Codex or Devstral 2?

GPT-5.2 Codex is more intelligent. It scores 66 compared with 56 on the composite Intelligence Index.

Which model is faster?

GPT-5.2 Codex is faster, generating 118 tokens per second compared with 70 tokens per second.

Which model is cheaper to run?

Devstral 2 is cheaper at $0.99 per 1M tokens blended, compared with $1.87 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Devstral 2 has lower latency, with a time to first token of 0.52s compared with 1.28s.

Which model has a larger context window?

GPT-5.2 Codex has a larger context window, supporting 400K tokens compared with 262.1K tokens.

Sonuç

Hem GPT-5.2 Codex (otonom ajan iş akışları, araç çağırma (tool calling) ve çok adımlı otomasyon, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de Devstral 2 (otonom ajan iş akışları, araç çağırma (tool calling) ve çok adımlı otomasyon, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: GPT-5.2 Codex karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, GPT-5.2 Codex daha yüksek üretim hızı sağlar; Devstral 2 ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için GPT-5.2 Codex mi yoksa Devstral 2 mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde GPT-5.2 Codex önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

GPT-5.2 Codex ve Devstral 2 API fiyatları nasıl karşılaştırılır?+

GPT-5.2 Codex 1M giriş/çıkış tokeni başına $1.75 / $14.00 ücretlendirilirken, Devstral 2 $1.50 / $4.50 seviyesindedir. Karma maliyette Devstral 2 daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

GPT-5.2 Codex 400K token bağlam penceresi sunarken Devstral 2 262.1K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise GPT-5.2 Codex öndedir.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

GPT-5.2 Codex vs Devstral 2

İstediğiniz Modelleri Seçin ve Karşılaştırın

GPT-5.2 Codex

Devstral 2

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

GPT-5.2 Codex · Strengths & Key Advantages

Considerations & Trade-offs

Devstral 2 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

GPT-5.2 Codex vs Devstral 2: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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