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Head-to-Head Model Comparison

GPT-5.1 Codex Max vs Claude Opus 5.5

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-5.1 Codex Max vs Claude Opus 5.5: Benchmarks, Pricing & Speed Comparison

GPT-5.1 Codex Max von OpenAI und Claude Opus 5.5 von Anthropic gehören zu den führenden KI-Modellen. GPT-5.1 Codex Max bietet ein Kontextfenster von 400K Tokens bei API-Preisen von $1.75/$14.00 pro 1M Tokens, während Claude Opus 5.5 1M Tokens Kontext zu $4.00/$20.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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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%

Anthropic

Claude Opus 5.5

Frontier-Flaggschiff-Modell

Claude Opus 5.5 von Anthropic ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 78 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und $4/$20 pro 1M Toke

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Wichtigste Unterschiede auf einen Blick

  • GPT-5.1 Codex Max is 1.6x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 400K tokens).
  • GPT-5.1 Codex Max is faster at 95 tok/s vs 78 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 78.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-5.1 Codex MaxClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
6294
Provider
OpenAIAnthropic
Input Price
Cost per million input tokens
$1.75 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$14.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.87 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
400K tokens~600 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
95 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
78.2%84.6%
GPQA Diamond
Graduate-level reasoning
80%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.17 / 1M$0.40 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Claude Opus 5.5
+6.4% SWE-bench lead
Cost
GPT-5.1 Codex Max
1.6x cheaper monthly runtime
Speed
GPT-5.1 Codex Max
+22% faster generation
Context
Claude Opus 5.5
1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-5.1 Codex MaxClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

78.2%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

80%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

87.2%vs91.5%
LMSYS Chatbot Arena Elo

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

1378ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

13.2%vs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

54.5%vs66.4%
SciCode (Scientific Computing)

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

48.5%vs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

75.5%vs83.1%
AIME (Advanced Math Competition)

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

92.6%vs95.4%
Long-Context Reasoning

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

80%vs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs78 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-5.1 Codex Max1.70s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-5.1 Codex Max6.96s
Claude Opus 5.57.81s

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-5.1 Codex Max Monthly API Bill
$15.49 / month
$1.75/1M in · $14/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
GPT-5.1 Codex Max saves approximately $9.11 / month (37%) under this workload.1.6x Savings

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

Claude Opus 5.5 · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
  • Hohe Generierungsgeschwindigkeit von 78 Token/Sekunde bei nur 1400 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($4/$20 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 hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Token Use & Task Economics

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

MetricGPT-5.1 Codex MaxClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$2.40$3.85
Output Tokens / Task
Tokens generated per task
62K68K
Reasoning Tokens / Task
Thinking tokens burned per task
36K42K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-5.1 Codex MaxClaude Opus 5.5
Official API Model ID (SDK)gpt-5-1-codex-maxclaude-opus-5-5-20260901
API ProtocolOpenAI Responses API & Chat Completions v1Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2025-112026-09
Knowledge CutoffOctober 2024June 2026
ArchitectureMaximum-capability code-specialised reasoning modelExtended Autonomous Hybrid Reasoning Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($1.75/1M in) across full 400K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $0.175/1M (no extra cache-write fee)$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
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 Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Custom Model Tuning via Amazon Bedrock & Prompt Distillation
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 PublishedAnthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
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-5.1 Codex Max vs Claude Opus 5.5: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-5.1 Codex Max or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 62 on the composite Intelligence Index.

Which model is faster?

GPT-5.1 Codex Max is faster, generating 95 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

GPT-5.1 Codex Max is cheaper at $1.87 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Opus 5.5 has lower latency, with a time to first token of 1.40s compared with 1.70s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 400K tokens.

Fazit

Sowohl GPT-5.1 Codex Max (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Claude Opus 5.5 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Claude Opus 5.5 führt bei komplexem Reasoning und Coding, GPT-5.1 Codex Max liefert den höheren Token-Durchsatz und GPT-5.1 Codex Max ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-5.1 Codex Max oder Claude Opus 5.5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Claude Opus 5.5 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-5.1 Codex Max und Claude Opus 5.5?+

GPT-5.1 Codex Max kostet $1.75 / $14.00 pro 1M Input-/Output-Tokens, Claude Opus 5.5 liegt bei $4.00 / $20.00. Beim gewichteten Gesamtpreis ist GPT-5.1 Codex Max günstiger.

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

GPT-5.1 Codex Max bietet 400K Tokens Kontext gegenüber 1M Tokens bei Claude Opus 5.5. Bei der Generierungsgeschwindigkeit liegt GPT-5.1 Codex Max 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 Anthropic, Systemkarten und unabhängige Benchmark-Auswertungen (2026).