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Claude 3.7 Sonnet vs o3-mini

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 o3-mini: Benchmarks, Pricing & Speed Comparison

Claude 3.7 Sonnet von Anthropic und o3-mini 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 o3-mini 200K Tokens Kontext zu $1.10/$4.40 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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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

o3-mini

Deep-Reasoning-Engine

o3-mini von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 85 Token/s Durchsatz, einem Intelligenz-Index von 51/100 und $1.1/$4.4 pro 1M Token.

Intelligence Index51 / 100
Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

49%

Wichtigste Unterschiede auf einen Blick

  • o3-mini is 2.9x cheaper for API usage (input + output tokens combined).
  • o3-mini is faster at 85 tok/s vs 72 tok/s.
  • Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 49.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude 3.7 Sonneto3-mini
Intelligence Index
Composite reasoning & knowledge score (0-100)
4851
Provider
AnthropicOpenAI
Input Price
Cost per million input tokens
$3.00 / 1M$1.10 / 1M
Output Price
Cost per million output tokens
$15.00 / 1M$4.40 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.31 / 1M$1.05 / 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
64K100K
Throughput Speed
Tokens generated per second
72 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
70.3%49%
GPQA Diamond
Graduate-level reasoning
65%79.7%
Cached Input Price
Prompt caching discount per 1M tokens
$0.30 / 1M$0.55 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Claude 3.7 Sonnet
+21.3% SWE-bench lead
Cost
o3-mini
2.9x cheaper monthly runtime
Speed
o3-mini
+18% faster generation
Context
Tied
200K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Claude 3.7 Sonneto3-mini
SWE-bench Verified (Autonomous Coding)

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

70.3%vs49%
GPQA Diamond (Doctorate-Level Science & Math)

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

65%vs79.7%
MMLU-Pro (Multi-task Language Understanding)

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

78.2%vs82.4%
LMSYS Chatbot Arena Elo

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

1335ptsvs1310pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

8.9%vs12.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

35.2%vs32.3%
SciCode (Scientific Computing)

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

40.1%vs29.4%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

64.8%vs51.6%
AIME (Advanced Math Competition)

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

61.3%vs91.7%
Long-Context Reasoning

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

71.5%vs74.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

72 tok/s vs85 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
o3-mini1.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude 3.7 Sonnet7.46s
o3-mini7.28s

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
o3-mini Monthly API Bill
$6.44 / month
$1.1/1M in · $4.4/1M out
o3-mini saves approximately $12.01 / month (65%) under this workload.2.9x 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

o3-mini · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 49% · GPQA Diamond 79.7%)
  • Hohe Generierungsgeschwindigkeit von 85 Token/Sekunde bei nur 1400 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($1.1/$4.4 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.

MetricClaude 3.7 Sonneto3-mini
Cost per Task
Estimated USD to finish one task
$1.42$1.30
Output Tokens / Task
Tokens generated per task
41K45K
Reasoning Tokens / Task
Thinking tokens burned per task
22K25K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureClaude 3.7 Sonneto3-mini
Official API Model ID (SDK)claude-3-7-sonnet-20250201o3-mini
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIOpenAI Responses API & Chat Completions v1
Release Date2025-022025-01
Knowledge CutoffNovember 2024October 2023
ArchitectureHybrid Reasoning MoEChain-of-Thought Reasoning 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 ($1.1/1M in) across full 200K window
Prompt Cache Policy & TTL$0.3/1M cached read (90% off); 5-min default & 1-hour extended TTLAutomatic prefix caching at $0.55/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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)
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
Output Modalitiestexttext

Claude 3.7 Sonnet vs o3-mini: Schnelle Antworten (FAQ)

Which is more intelligent, Claude 3.7 Sonnet or o3-mini?

o3-mini is more intelligent. It scores 51 compared with 48 on the composite Intelligence Index.

Which model is faster?

o3-mini is faster, generating 85 tokens per second compared with 72 tokens per second.

Which model is cheaper to run?

o3-mini is cheaper at $1.05 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude 3.7 Sonnet has lower latency, with a time to first token of 0.52s compared with 1.40s.

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 o3-mini (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: o3-mini führt bei komplexem Reasoning und Coding, o3-mini liefert den höheren Token-Durchsatz und o3-mini 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 o3-mini?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt o3-mini 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 o3-mini?+

Claude 3.7 Sonnet kostet $3.00 / $15.00 pro 1M Input-/Output-Tokens, o3-mini liegt bei $1.10 / $4.40. Beim gewichteten Gesamtpreis ist o3-mini günstiger.

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

Claude 3.7 Sonnet bietet 200K Tokens Kontext gegenüber 200K Tokens bei o3-mini. Bei der Generierungsgeschwindigkeit liegt o3-mini 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).