Head-to-Head Model Comparison

GPT-5.2 Codex vs GPT-5.1 Codex Max

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

GPT-5.2 CodexProfile

GPT-5.1 Codex MaxProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-5.2 Codex

Model Agen Otonom & Alat

GPT-5.2 Codex dari OpenAI adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 400K, kecepatan 118 token/detik, indeks kecerdasan 66/100, s

Intelligence Index66 / 100

Input Token:

$1.75 / 1M

Output Token:

$14.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

78.1%

OpenAI

GPT-5.1 Codex Max

Model Unggulan Garis Depan

GPT-5.1 Codex Max dari OpenAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 400K, kecepatan 95 token/detik, indeks kecerdasan 62/100, serta t

Intelligence Index62 / 100

Input Token:

$1.75 / 1M

Output Token:

$14.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

78.2%

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
GPT-5.2 Codex is faster at 118 tok/s vs 95 tok/s.
GPT-5.1 Codex Max leads on SWE-bench coding (78.2% vs 78.1%).

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

Feature	GPT-5.2 Codex	GPT-5.1 Codex Max
Intelligence Index Composite reasoning & knowledge score (0-100)	66	62
Provider	OpenAI	OpenAI
Input Price Cost per million input tokens	$1.75 / 1M	$1.75 / 1M
Output Price Cost per million output tokens	$14.00 / 1M	$14.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.87 / 1M	$1.87 / 1M
Context Window Maximum input + output tokens	400K tokens~600 A4 pages of text	400K tokens~600 A4 pages of text
Max Output Tokens Per single request	128K	128K
Throughput Speed Tokens generated per second	118 tok/s	95 tok/s
SWE-bench Verified Real-world software engineering	78.1%	78.2%
GPQA Diamond Graduate-level reasoning	78.2%	80%
Cached Input Price Prompt caching discount per 1M tokens	$0.17 / 1M	$0.17 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

GPT-5.1 Codex Max

+0.1% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

GPT-5.2 Codex

+24% faster generation

Context

Tied

400K tokens window

Skor Benchmark Terverifikasi

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

GPT-5.2 CodexGPT-5.1 Codex Max

SWE-bench Verified (Autonomous Coding)

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

78.1%vs78.2%

GPQA Diamond (Doctorate-Level Science & Math)

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

78.2%vs80%

MMLU-Pro (Multi-task Language Understanding)

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

87.4%vs87.2%

LMSYS Chatbot Arena Elo

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

1398ptsvs1378pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

13.8%vs13.2%

Terminal-Bench (Agentic Coding & Terminal Use)

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

53.5%vs54.5%

SciCode (Scientific Computing)

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

48.5%vs48.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

76.5%vs75.5%

AIME (Advanced Math Competition)

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

93.8%vs92.6%

Long-Context Reasoning

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

82%vs80%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

118 tok/s vs95 tok/s

Latensi & Waktu Respons

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

GPT-5.1 Codex Max1.70s

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

GPT-5.2 Codex5.52s

GPT-5.1 Codex Max6.96s

Simulator Biaya Token Interaktif

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

GPT-5.1 Codex Max Monthly API Bill

$15.49 / month

$1.75/1M in · $14/1M out

These models cost the same under this workload — no savings either way.Break-even

GPT-5.2 Codex · Strengths & Key Advantages

Jendela konteks 400K dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 78.1% · GPQA Diamond 78.2%)
Kecepatan generasi tinggi 118 token/detik dengan latensi token pertama (TTFT) hanya 1280 ms
Skema harga API kompetitif (tarif $1.75/$14 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

GPT-5.1 Codex Max · Strengths & Key Advantages

Jendela konteks 400K dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 78.2% · GPQA Diamond 80%)
Kecepatan generasi tinggi 95 token/detik dengan latensi token pertama (TTFT) hanya 1700 ms
Skema harga API kompetitif (tarif $1.75/$14 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

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

Metric	GPT-5.2 Codex	GPT-5.1 Codex Max
Cost per Task Estimated USD to finish one task	$2.20	$2.40
Output Tokens / Task Tokens generated per task	60K	62K
Reasoning Tokens / Task Thinking tokens burned per task	34K	36K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

GPT-5.2 Codex vs GPT-5.1 Codex Max: Jawaban Cepat (FAQ)

Which is more intelligent, GPT-5.2 Codex or GPT-5.1 Codex Max?

GPT-5.2 Codex is more intelligent. It scores 66 compared with 62 on the composite Intelligence Index.

Which model is faster?

GPT-5.2 Codex is faster, generating 118 tokens per second compared with 95 tokens per second.

Which model has lower latency?

GPT-5.2 Codex has lower latency, with a time to first token of 1.28s compared with 1.70s.

Kesimpulan

Baik GPT-5.2 Codex (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-5.1 Codex Max (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT-5.2 Codex unggul dalam penalaran dan pemrograman, GPT-5.2 Codex memberikan kecepatan generasi token lebih tinggi, dan GPT-5.2 Codex lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GPT-5.2 Codex atau GPT-5.1 Codex Max?+

Dalam indeks kecerdasan gabungan dan benchmark coding, GPT-5.2 Codex memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API GPT-5.2 Codex vs GPT-5.1 Codex Max?+

GPT-5.2 Codex mengenakan biaya $1.75 / $14.00 per 1M token input/output, sedangkan GPT-5.1 Codex Max sebesar $1.75 / $14.00. Secara keseluruhan, GPT-5.2 Codex lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

GPT-5.2 Codex mendukung konteks 400K token dibandingkan 400K token pada GPT-5.1 Codex Max, sementara GPT-5.2 Codex unggul dalam kecepatan output token per detik.

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)

Feature	GPT-5.2 Codex	GPT-5.1 Codex Max
Official API Model ID (SDK)	gpt-5-2-codex	gpt-5-1-codex-max
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI Responses API & Chat Completions v1
Release Date	2025-12	2025-11
Knowledge Cutoff	August 2025	October 2024
Architecture	Code-specialised long-horizon reasoning model	Maximum-capability code-specialised reasoning model
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($1.75/1M in) across full 400K window	Flat rate ($1.75/1M in) across full 400K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.175/1M (no extra cache-write fee)	Automatic prefix caching at $0.175/1M (no extra cache-write fee)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	OpenAI Preparedness Framework Audited & System Card Published	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision	text, vision
Output Modalities	text	text

GPT-5.2 Codex vs GPT-5.1 Codex Max

Ganti & Bandingkan Model Apa Saja

GPT-5.2 Codex

GPT-5.1 Codex Max

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GPT-5.2 Codex · Strengths & Key Advantages

Considerations & Trade-offs

GPT-5.1 Codex Max · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GPT-5.2 Codex vs GPT-5.1 Codex Max: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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