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GPT-5.2 Codex vs GPT-5.1 Codex Max

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-5.2 Codex vs GPT-5.1 Codex Max: Benchmarks, Pricing & Speed Comparison

GPT-5.2 Codex von OpenAI und GPT-5.1 Codex Max von OpenAI gehören zu den führenden KI-Modellen. GPT-5.2 Codex bietet ein Kontextfenster von 400K Tokens bei API-Preisen von $1.75/$14.00 pro 1M Tokens, während GPT-5.1 Codex Max 400K Tokens Kontext zu $1.75/$14.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

GPT-5.2 Codex

Autonomes Agenten-Modell

GPT-5.2 Codex von OpenAI ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 400K Kontextfenster, 118 Token/s Durchsatz, einem Intelligenz-Index von 66/100 und $1.75/$14 pro 1M To

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

Frontier-Flaggschiff-Modell

GPT-5.1 Codex Max von OpenAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 400K Kontextfenster, 95 Token/s Durchsatz, einem Intelligenz-Index von 62/100 und $1.75/$14 pro 1M

Intelligence Index62 / 100
Input Token:

$1.75 / 1M

Output Token:

$14.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

78.2%

Wichtigste Unterschiede auf einen Blick

  • 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 →
FeatureGPT-5.2 CodexGPT-5.1 Codex Max
Intelligence Index
Composite reasoning & knowledge score (0-100)
6662
Provider
OpenAIOpenAI
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 text400K tokens~600 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
118 tok/s95 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

100K / day
10K500K1M
30K / day
5K150K300K
50% 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

  • 400K Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 78.1% · GPQA Diamond 78.2%)
  • Hohe Generierungsgeschwindigkeit von 118 Token/Sekunde bei nur 1280 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($1.75/$14 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

GPT-5.1 Codex Max · Strengths & Key Advantages

  • 400K Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 78.2% · GPQA Diamond 80%)
  • Hohe Generierungsgeschwindigkeit von 95 Token/Sekunde bei nur 1700 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($1.75/$14 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

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

MetricGPT-5.2 CodexGPT-5.1 Codex Max
Cost per Task
Estimated USD to finish one task
$2.20$2.40
Output Tokens / Task
Tokens generated per task
60K62K
Reasoning Tokens / Task
Thinking tokens burned per task
34K36K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-5.2 CodexGPT-5.1 Codex Max
Official API Model ID (SDK)gpt-5-2-codexgpt-5-1-codex-max
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2025-122025-11
Knowledge CutoffAugust 2025October 2024
ArchitectureCode-specialised long-horizon reasoning modelMaximum-capability code-specialised reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 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 windowFlat rate ($1.75/1M in) across full 400K window
Prompt Cache Policy & TTLAutomatic 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 AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

GPT-5.2 Codex vs GPT-5.1 Codex Max: Schnelle Antworten (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.

Fazit

Sowohl GPT-5.2 Codex (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-5.1 Codex Max (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: GPT-5.2 Codex führt bei komplexem Reasoning und Coding, GPT-5.2 Codex liefert den höheren Token-Durchsatz und GPT-5.2 Codex ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-5.2 Codex oder GPT-5.1 Codex Max?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt GPT-5.2 Codex vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-5.2 Codex und GPT-5.1 Codex Max?+

GPT-5.2 Codex kostet $1.75 / $14.00 pro 1M Input-/Output-Tokens, GPT-5.1 Codex Max liegt bei $1.75 / $14.00. Beim gewichteten Gesamtpreis ist GPT-5.2 Codex günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

GPT-5.2 Codex bietet 400K Tokens Kontext gegenüber 400K Tokens bei GPT-5.1 Codex Max. Bei der Generierungsgeschwindigkeit liegt GPT-5.2 Codex vorn.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Offizielle Quellen: Offizielle API-Dokumentation von OpenAI und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).