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Head-to-Head Model Comparison

Gemini 3.1 Pro vs Claude Sonnet 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
Gemini 3.1 Pro vs Claude Sonnet 4.5: Benchmarks, Pricing & Speed Comparison

Gemini 3.1 Pro von Google und Claude Sonnet 4.5 von Anthropic gehören zu den führenden KI-Modellen. Gemini 3.1 Pro bietet ein Kontextfenster von 1.05M Tokens bei API-Preisen von $2.00/$12.00 pro 1M Tokens, während Claude Sonnet 4.5 1M Tokens Kontext zu $3.00/$15.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Google

Gemini 3.1 Pro

Spitzenmodell für Programmierung

Gemini 3.1 Pro von Google ist ein KI-Modell für autonomes Software-Engineering, Code-Refactoring und komplexe Fehleranalyse mit 1.0M Kontextfenster, 110 Token/s Durchsatz, einem Intelligenz-Index von 86/100 und $2/$12 pro 1M Token.

Intelligence Index86 / 100
Input Token:

$2.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.5%

Anthropic

Claude Sonnet 4.5

Frontier-Flaggschiff-Modell

Claude Sonnet 4.5 von Anthropic ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 90 Token/s Durchsatz, einem Intelligenz-Index von 91/100 und $3/$15 pro 1M To

Intelligence Index91 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

77.8%

Wichtigste Unterschiede auf einen Blick

  • Gemini 3.1 Pro is 1.3x cheaper for API usage (input + output tokens combined).
  • Gemini 3.1 Pro has a larger context window (1M vs 1M tokens).
  • Gemini 3.1 Pro is faster at 110 tok/s vs 90 tok/s.
  • Claude Sonnet 4.5 leads on SWE-bench coding (77.8% vs 74.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 3.1 ProClaude Sonnet 4.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
8691
Provider
GoogleAnthropic
Input Price
Cost per million input tokens
$2.00 / 1M$3.00 / 1M
Output Price
Cost per million output tokens
$12.00 / 1M$15.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.95 / 1M$2.31 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
66K64K
Throughput Speed
Tokens generated per second
110 tok/s90 tok/s
SWE-bench Verified
Real-world software engineering
74.5%77.8%
GPQA Diamond
Graduate-level reasoning
86.5%70.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.30 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Claude Sonnet 4.5
+3.3% SWE-bench lead
Cost
Gemini 3.1 Pro
1.3x cheaper monthly runtime
Speed
Gemini 3.1 Pro
+22% faster generation
Context
Gemini 3.1 Pro
1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemini 3.1 ProClaude Sonnet 4.5
SWE-bench Verified (Autonomous Coding)

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

74.5%vs77.8%
GPQA Diamond (Doctorate-Level Science & Math)

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

86.5%vs70.2%
MMLU-Pro (Multi-task Language Understanding)

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

89.2%vs84.5%
LMSYS Chatbot Arena Elo

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

1475ptsvs1355pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs10.4%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs50.1%
SciCode (Scientific Computing)

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

N/Avs43.9%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs72.6%
AIME (Advanced Math Competition)

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

94%vs84.7%
Long-Context Reasoning

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

N/Avs74.8%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs90 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
Gemini 3.1 Pro0.78s
Claude Sonnet 4.50.90s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 3.1 Pro5.33s
Claude Sonnet 4.56.46s

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)
Gemini 3.1 Pro Monthly API Bill
$14.55 / month
$2/1M in · $12/1M out
Claude Sonnet 4.5 Monthly API Bill
$18.45 / month
$3/1M in · $15/1M out
Gemini 3.1 Pro saves approximately $3.90 / month (21%) under this workload.1.3x Savings

Gemini 3.1 Pro · Strengths & Key Advantages

  • 1.0M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 74.5% · GPQA Diamond 86.5%)
  • Hohe Generierungsgeschwindigkeit von 110 Token/Sekunde bei nur 780 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$12 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 Sonnet 4.5 · Strengths & Key Advantages

  • 1M Kontextfenster und bis zu 64K maximale Output-Token (SWE-bench Verified 77.8% · GPQA Diamond 70.2%)
  • Hohe Generierungsgeschwindigkeit von 90 Token/Sekunde bei nur 900 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

Token Use & Task Economics

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

MetricGemini 3.1 ProClaude Sonnet 4.5
Cost per Task
Estimated USD to finish one task
$1.85$2.10
Output Tokens / Task
Tokens generated per task
52K55K
Reasoning Tokens / Task
Thinking tokens burned per task
26K30K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 3.1 ProClaude Sonnet 4.5
Official API Model ID (SDK)models/gemini-3-1-proclaude-sonnet-4-5-20251101
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-022025-11
Knowledge CutoffNovember 2025March 2025
ArchitecturePathways High-Capacity Reasoning ArchitectureHybrid Reasoning
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.5/1M with configurable TTL$0.3/1M 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 & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationCustom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)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 ClassificationGoogle DeepMind Frontier Safety Framework VerifiedAnthropic 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, vision, audio, videotext, vision
Output Modalitiestexttext

Gemini 3.1 Pro vs Claude Sonnet 4.5: Schnelle Antworten (FAQ)

Which is more intelligent, Gemini 3.1 Pro or Claude Sonnet 4.5?

Claude Sonnet 4.5 is more intelligent. It scores 91 compared with 86 on the composite Intelligence Index.

Which model is faster?

Gemini 3.1 Pro is faster, generating 110 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

Gemini 3.1 Pro is cheaper at $1.95 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.1 Pro has lower latency, with a time to first token of 0.78s compared with 0.90s.

Which model has a larger context window?

Gemini 3.1 Pro has a larger context window, supporting 1.05M tokens compared with 1M tokens.

Fazit

Sowohl Gemini 3.1 Pro (Optimal für autonomes Software-Engineering, Code-Refactoring und komplexe Fehleranalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Claude Sonnet 4.5 (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Claude Sonnet 4.5 führt bei komplexem Reasoning und Coding, Gemini 3.1 Pro liefert den höheren Token-Durchsatz und Gemini 3.1 Pro ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 3.1 Pro oder Claude Sonnet 4.5?+

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

Wie unterscheiden sich die API-Preise von Gemini 3.1 Pro und Claude Sonnet 4.5?+

Gemini 3.1 Pro kostet $2.00 / $12.00 pro 1M Input-/Output-Tokens, Claude Sonnet 4.5 liegt bei $3.00 / $15.00. Beim gewichteten Gesamtpreis ist Gemini 3.1 Pro günstiger.

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

Gemini 3.1 Pro bietet 1.05M Tokens Kontext gegenüber 1M Tokens bei Claude Sonnet 4.5. Bei der Generierungsgeschwindigkeit liegt Gemini 3.1 Pro 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 Google und Anthropic, Systemkarten und unabhängige Benchmark-Auswertungen (2026).