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Head-to-Head Model Comparison

Kimi K2 Thinking Turbo 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
Kimi K2 Thinking Turbo vs Claude Opus 4.5: Benchmarks, Pricing & Speed Comparison

Kimi K2 Thinking Turbo von Moonshot AI und Claude Opus 4.5 von Anthropic gehören zu den führenden KI-Modellen. Kimi K2 Thinking Turbo bietet ein Kontextfenster von 262.1K Tokens bei API-Preisen von $0.60/$2.40 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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Popular Matchups:
Moonshot AI

Kimi K2 Thinking Turbo

Deep-Reasoning-Engine

Kimi K2 Thinking Turbo von Moonshot AI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 262K Kontextfenster, 90 Token/s Durchsatz, einem Intelligenz-Index von 84/100 und $0.6/$2.4 p

Intelligence Index84 / 100
Input Token:

$0.60 / 1M

Output Token:

$2.40 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

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

  • Kimi K2 Thinking Turbo is 9.8x cheaper for API usage (input + output tokens combined).
  • Kimi K2 Thinking Turbo has a larger context window (262K vs 200K tokens).
  • Kimi K2 Thinking Turbo is faster at 90 tok/s vs 62 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureKimi K2 Thinking TurboClaude Opus 4.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
8463
Provider
Moonshot AIAnthropic
Input Price
Cost per million input tokens
$0.60 / 1M$5.00 / 1M
Output Price
Cost per million output tokens
$2.40 / 1M$25.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.40 / 1M$3.85 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text200K tokens~300 A4 pages of text
Max Output Tokens
Per single request
16K64K
Throughput Speed
Tokens generated per second
90 tok/s62 tok/s
SWE-bench Verified
Real-world software engineering
N/A77.2%
GPQA Diamond
Graduate-level reasoning
68.5%74.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.06 / 1M$0.50 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Kimi K2 Thinking Turbo
9.8x cheaper monthly runtime
Speed
Kimi K2 Thinking Turbo
+45% faster generation
Context
Kimi K2 Thinking Turbo
262.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Kimi K2 Thinking TurboClaude Opus 4.5
SWE-bench Verified (Autonomous Coding)

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

N/Avs77.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

68.5%vs74.8%
MMLU-Pro (Multi-task Language Understanding)

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

81.5%vs87.3%
LMSYS Chatbot Arena Elo

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

1365ptsvs1376pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs13.7%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs49.2%
SciCode (Scientific Computing)

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

N/Avs45.6%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs74.1%
AIME (Advanced Math Competition)

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

81%vs88.5%
Long-Context Reasoning

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

N/Avs79.4%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 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
Kimi K2 Thinking Turbo0.52s
Claude Opus 4.52.10s
Est. End-to-End Response (500 tokens)TTFT + generation time
Kimi K2 Thinking Turbo6.08s
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)
Kimi K2 Thinking Turbo Monthly API Bill
$3.15 / month
$0.6/1M in · $2.4/1M out
Claude Opus 4.5 Monthly API Bill
$30.75 / month
$5/1M in · $25/1M out
Kimi K2 Thinking Turbo saves approximately $27.60 / month (90%) under this workload.9.8x Savings

Kimi K2 Thinking Turbo · Strengths & Key Advantages

  • 262K Kontextfenster und bis zu 16K maximale Output-Token (GPQA Diamond 68.5% · MMLU-Pro 81.5%)
  • Hohe Generierungsgeschwindigkeit von 90 Token/Sekunde bei nur 520 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.6/$2.4 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

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.

MetricKimi K2 Thinking TurboClaude Opus 4.5
Cost per Task
Estimated USD to finish one task
$0.18$5.86
Output Tokens / Task
Tokens generated per task
22K73K
Reasoning Tokens / Task
Thinking tokens burned per task
10K43K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureKimi K2 Thinking TurboClaude Opus 4.5
Official API Model ID (SDK)kimi-k2-thinking-turboclaude-opus-4-5-20251101
API ProtocolMoonshot AI REST API (OpenAI-Compatible)Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2025-112025-11
Knowledge CutoffSeptember 2025March 2025
ArchitectureHosted Accelerated Reasoning EngineHybrid Reasoning
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 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 ($0.6/1M in) across full 262K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.06/1M tokens$0.5/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)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 & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationCustom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token)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 ClassificationMoonshot AI Production Safety & Alignment 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 Modalitiestexttext, vision
Output Modalitiestexttext

Kimi K2 Thinking Turbo vs Claude Opus 4.5: Schnelle Antworten (FAQ)

Which is more intelligent, Kimi K2 Thinking Turbo or Claude Opus 4.5?

Kimi K2 Thinking Turbo is more intelligent. It scores 84 compared with 63 on the composite Intelligence Index.

Which model is faster?

Kimi K2 Thinking Turbo is faster, generating 90 tokens per second compared with 62 tokens per second.

Which model is cheaper to run?

Kimi K2 Thinking Turbo is cheaper at $0.40 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Kimi K2 Thinking Turbo has lower latency, with a time to first token of 0.52s compared with 2.10s.

Which model has a larger context window?

Kimi K2 Thinking Turbo has a larger context window, supporting 262.1K tokens compared with 200K tokens.

Fazit

Sowohl Kimi K2 Thinking Turbo (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: Kimi K2 Thinking Turbo führt bei komplexem Reasoning und Coding, Kimi K2 Thinking Turbo liefert den höheren Token-Durchsatz und Kimi K2 Thinking Turbo ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Kimi K2 Thinking Turbo oder Claude Opus 4.5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Kimi K2 Thinking Turbo vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Kimi K2 Thinking Turbo und Claude Opus 4.5?+

Kimi K2 Thinking Turbo kostet $0.60 / $2.40 pro 1M Input-/Output-Tokens, Claude Opus 4.5 liegt bei $5.00 / $25.00. Beim gewichteten Gesamtpreis ist Kimi K2 Thinking Turbo günstiger.

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

Kimi K2 Thinking Turbo bietet 262.1K Tokens Kontext gegenüber 200K Tokens bei Claude Opus 4.5. Bei der Generierungsgeschwindigkeit liegt Kimi K2 Thinking Turbo 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 Moonshot AI und Anthropic, Systemkarten und unabhängige Benchmark-Auswertungen (2026).