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Head-to-Head Model Comparison

o3 vs Claude Opus 4.5

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
o3 vs Claude Opus 4.5: Benchmarks, Pricing & Speed Comparison

o3 von OpenAI und Claude Opus 4.5 von Anthropic gehören zu den führenden KI-Modellen. o3 bietet ein Kontextfenster von 200K Tokens bei API-Preisen von $2.00/$8.00 pro 1M Tokens, während Claude Opus 4.5 200K Tokens Kontext zu $5.00/$25.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

o3

Deep-Reasoning-Engine

o3 von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 75 Token/s Durchsatz, einem Intelligenz-Index von 56/100 und $2/$8 pro 1M Token.

Intelligence Index56 / 100
Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

69.1%

Anthropic

Claude Opus 4.5

Frontier-Flaggschiff-Modell

Claude Opus 4.5 von Anthropic ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 200K Kontextfenster, 62 Token/s Durchsatz, einem Intelligenz-Index von 63/100 und $5/$25 pro 1M To

Intelligence Index63 / 100
Input Token:

$5.00 / 1M

Output Token:

$25.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

77.2%

Wichtigste Unterschiede auf einen Blick

  • o3 is 2.8x cheaper for API usage (input + output tokens combined).
  • o3 is faster at 75 tok/s vs 62 tok/s.
  • Claude Opus 4.5 leads on SWE-bench coding (77.2% vs 69.1%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Featureo3Claude Opus 4.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
5663
Provider
OpenAIAnthropic
Input Price
Cost per million input tokens
$2.00 / 1M$5.00 / 1M
Output Price
Cost per million output tokens
$8.00 / 1M$25.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.55 / 1M$3.85 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text200K tokens~300 A4 pages of text
Max Output Tokens
Per single request
100K64K
Throughput Speed
Tokens generated per second
75 tok/s62 tok/s
SWE-bench Verified
Real-world software engineering
69.1%77.2%
GPQA Diamond
Graduate-level reasoning
76.5%74.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.50 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Claude Opus 4.5
+8.1% SWE-bench lead
Cost
o3
2.8x cheaper monthly runtime
Speed
o3
+21% faster generation
Context
Tied
200K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

o3Claude Opus 4.5
SWE-bench Verified (Autonomous Coding)

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

69.1%vs77.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

76.5%vs74.8%
MMLU-Pro (Multi-task Language Understanding)

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

87%vs87.3%
LMSYS Chatbot Arena Elo

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

1345ptsvs1376pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

12.9%vs13.7%
Terminal-Bench (Agentic Coding & Terminal Use)

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

42%vs49.2%
SciCode (Scientific Computing)

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

42.5%vs45.6%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

70.5%vs74.1%
AIME (Advanced Math Competition)

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

91.6%vs88.5%
Long-Context Reasoning

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

78.5%vs79.4%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs62 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
o32.80s
Claude Opus 4.52.10s
Est. End-to-End Response (500 tokens)TTFT + generation time
o39.47s
Claude Opus 4.510.16s

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)
o3 Monthly API Bill
$10.95 / month
$2/1M in · $8/1M out
Claude Opus 4.5 Monthly API Bill
$30.75 / month
$5/1M in · $25/1M out
o3 saves approximately $19.80 / month (64%) under this workload.2.8x Savings

o3 · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 69.1% · GPQA Diamond 76.5%)
  • Hohe Generierungsgeschwindigkeit von 75 Token/Sekunde bei nur 2800 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$8 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

Claude Opus 4.5 · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 64K maximale Output-Token (SWE-bench Verified 77.2% · GPQA Diamond 74.8%)
  • Hohe Generierungsgeschwindigkeit von 62 Token/Sekunde bei nur 2100 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($5/$25 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.

Metrico3Claude Opus 4.5
Cost per Task
Estimated USD to finish one task
$1.35$5.86
Output Tokens / Task
Tokens generated per task
48K73K
Reasoning Tokens / Task
Thinking tokens burned per task
26K43K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Featureo3Claude Opus 4.5
Official API Model ID (SDK)o3claude-opus-4-5-20251101
API ProtocolOpenAI Responses API & Chat Completions v1Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2025-042025-11
Knowledge CutoffJune 2024March 2025
ArchitectureReasoning transformer with adjustable thinking effortHybrid Reasoning
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) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 200K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $0.5/1M (no extra cache-write fee)$0.5/1M 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

o3 vs Claude Opus 4.5: Schnelle Antworten (FAQ)

Which is more intelligent, o3 or Claude Opus 4.5?

Claude Opus 4.5 is more intelligent. It scores 63 compared with 56 on the composite Intelligence Index.

Which model is faster?

o3 is faster, generating 75 tokens per second compared with 62 tokens per second.

Which model is cheaper to run?

o3 is cheaper at $1.55 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Opus 4.5 has lower latency, with a time to first token of 2.10s compared with 2.80s.

Fazit

Sowohl o3 (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Claude Opus 4.5 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Claude Opus 4.5 führt bei komplexem Reasoning und Coding, o3 liefert den höheren Token-Durchsatz und o3 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – o3 oder Claude Opus 4.5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Claude Opus 4.5 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von o3 und Claude Opus 4.5?+

o3 kostet $2.00 / $8.00 pro 1M Input-/Output-Tokens, Claude Opus 4.5 liegt bei $5.00 / $25.00. Beim gewichteten Gesamtpreis ist o3 günstiger.

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

o3 bietet 200K Tokens Kontext gegenüber 200K Tokens bei Claude Opus 4.5. Bei der Generierungsgeschwindigkeit liegt o3 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).