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Head-to-Head Model Comparison

Claude 3.7 Sonnet vs GPT-4o

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
Claude 3.7 Sonnet vs GPT-4o: Benchmarks, Pricing & Speed Comparison

Claude 3.7 Sonnet von Anthropic und GPT-4o von OpenAI gehören zu den führenden KI-Modellen. Claude 3.7 Sonnet bietet ein Kontextfenster von 200K Tokens bei API-Preisen von $3.00/$15.00 pro 1M Tokens, während GPT-4o 128K Tokens Kontext zu $2.50/$10.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Popular Matchups:Claude 3.7 vs GPT-4o
Anthropic

Claude 3.7 Sonnet

Spitzenmodell für Programmierung

Claude 3.7 Sonnet von Anthropic ist ein KI-Modell für autonomes Software-Engineering, Code-Refactoring und komplexe Fehleranalyse mit 200K Kontextfenster, 72 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und $3/$15 pro 1M Token.

Intelligence Index48 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

70.3%

OpenAI

GPT-4o

Natives Sprach- & Audiomodell

GPT-4o von OpenAI ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 128K Kontextfenster, 105 Token/s Durchsatz, einem Intelligenz-Index von 41/100 und $2.5/$10 pro 1M Token.

Intelligence Index41 / 100
Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38.8%

Wichtigste Unterschiede auf einen Blick

  • GPT-4o is 1.3x cheaper for API usage (input + output tokens combined).
  • Claude 3.7 Sonnet has a larger context window (200K vs 128K tokens).
  • GPT-4o is faster at 105 tok/s vs 72 tok/s.
  • Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 38.8%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude 3.7 SonnetGPT-4o
Intelligence Index
Composite reasoning & knowledge score (0-100)
4841
Provider
AnthropicOpenAI
Input Price
Cost per million input tokens
$3.00 / 1M$2.50 / 1M
Output Price
Cost per million output tokens
$15.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.31 / 1M$2.38 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
64K16K
Throughput Speed
Tokens generated per second
72 tok/s105 tok/s
SWE-bench Verified
Real-world software engineering
70.3%38.8%
GPQA Diamond
Graduate-level reasoning
65%53.6%
Cached Input Price
Prompt caching discount per 1M tokens
$0.30 / 1M$1.25 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Claude 3.7 Sonnet
+31.5% SWE-bench lead
Cost
GPT-4o
1.3x cheaper monthly runtime
Speed
GPT-4o
+46% faster generation
Context
Claude 3.7 Sonnet
200K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Claude 3.7 SonnetGPT-4o
SWE-bench Verified (Autonomous Coding)

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

70.3%vs38.8%
GPQA Diamond (Doctorate-Level Science & Math)

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

65%vs53.6%
MMLU-Pro (Multi-task Language Understanding)

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

78.2%vs72.6%
LMSYS Chatbot Arena Elo

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

1335ptsvs1286pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

8.9%vs4.1%
Terminal-Bench (Agentic Coding & Terminal Use)

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

35.2%vs18.5%
SciCode (Scientific Computing)

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

40.1%vs27.3%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

64.8%vs43.9%
AIME (Advanced Math Competition)

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

61.3%vs29.7%
Long-Context Reasoning

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

71.5%vs58.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

72 tok/s vs105 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
Claude 3.7 Sonnet0.52s
GPT-4o0.41s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude 3.7 Sonnet7.46s
GPT-4o5.17s

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)
Claude 3.7 Sonnet Monthly API Bill
$18.45 / month
$3/1M in · $15/1M out
GPT-4o Monthly API Bill
$14.63 / month
$2.5/1M in · $10/1M out
GPT-4o saves approximately $3.82 / month (21%) under this workload.1.3x Savings

Claude 3.7 Sonnet · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 64K maximale Output-Token (SWE-bench Verified 70.3% · GPQA Diamond 65%)
  • Hohe Generierungsgeschwindigkeit von 72 Token/Sekunde bei nur 520 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($3/$15 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

GPT-4o · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 38.8% · GPQA Diamond 53.6%)
  • Hohe Generierungsgeschwindigkeit von 105 Token/Sekunde bei nur 410 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2.5/$10 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.

MetricClaude 3.7 SonnetGPT-4o
Cost per Task
Estimated USD to finish one task
$1.42$0.68
Output Tokens / Task
Tokens generated per task
41K24K
Reasoning Tokens / Task
Thinking tokens burned per task
22K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureClaude 3.7 SonnetGPT-4o
Official API Model ID (SDK)claude-3-7-sonnet-20250201gpt-4o
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIOpenAI Responses API & Chat Completions v1
Release Date2025-022024-05
Knowledge CutoffNovember 2024October 2023
ArchitectureHybrid Reasoning MoEOmni-Multimodal Dense
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($2.5/1M in) across full 128K window
Prompt Cache Policy & TTL$0.3/1M cached read (90% off); 5-min default & 1-hour extended TTLAutomatic prefix caching at $1.25/1M (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 Citations + Code Execution Tool + MCP ConnectorsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportCustom Model Tuning via Amazon Bedrock & Prompt DistillationSupervised 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) · Audio (~25–32 tokens/sec)
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 ClassificationAnthropic RSP ASL-2 / ASL-3 Constitutional Safety EvaluatedOpenAI 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, audio
Output Modalitiestexttext, audio

Claude 3.7 Sonnet vs GPT-4o: Schnelle Antworten (FAQ)

Which is more intelligent, Claude 3.7 Sonnet or GPT-4o?

Claude 3.7 Sonnet is more intelligent. It scores 48 compared with 41 on the composite Intelligence Index.

Which model is faster?

GPT-4o is faster, generating 105 tokens per second compared with 72 tokens per second.

Which model is cheaper to run?

Claude 3.7 Sonnet is cheaper at $2.31 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.52s.

Which model has a larger context window?

Claude 3.7 Sonnet has a larger context window, supporting 200K tokens compared with 128K tokens.

Fazit

Sowohl Claude 3.7 Sonnet (Optimal für autonomes Software-Engineering, Code-Refactoring und komplexe Fehleranalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-4o (Optimal für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Claude 3.7 Sonnet führt bei komplexem Reasoning und Coding, GPT-4o liefert den höheren Token-Durchsatz und Claude 3.7 Sonnet ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Claude 3.7 Sonnet oder GPT-4o?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Claude 3.7 Sonnet vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Claude 3.7 Sonnet und GPT-4o?+

Claude 3.7 Sonnet kostet $3.00 / $15.00 pro 1M Input-/Output-Tokens, GPT-4o liegt bei $2.50 / $10.00. Beim gewichteten Gesamtpreis ist Claude 3.7 Sonnet günstiger.

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

Claude 3.7 Sonnet bietet 200K Tokens Kontext gegenüber 128K Tokens bei GPT-4o. Bei der Generierungsgeschwindigkeit liegt GPT-4o 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 Anthropic und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).