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Kimi K2 Thinking vs Kimi K2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Kimi K2 Thinking vs Kimi K2: Benchmarks, Pricing & Speed Comparison

Kimi K2 Thinking von Moonshot AI und Kimi K2 von Moonshot AI gehören zu den führenden KI-Modellen. Kimi K2 Thinking bietet ein Kontextfenster von 262.1K Tokens bei API-Preisen von $0.00/$0.00 pro 1M Tokens, während Kimi K2 131.1K Tokens Kontext zu $0.00/$0.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Moonshot AI

Kimi K2 Thinking

Open-Weights-Reasoning-Modell

Kimi K2 Thinking von Moonshot AI ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 262K Kontextfenster, 55 Token/s Durchsatz, einem Intelligenz-Index von 85/100 und kostenlosem

Intelligence Index85 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

50%

Moonshot AI

Kimi K2

Autonomes Agenten-Modell

Kimi K2 von Moonshot AI ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 131K Kontextfenster, 62 Token/s Durchsatz, einem Intelligenz-Index von 83/100 und kostenlosem Open-Weig

Intelligence Index83 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

46%

Wichtigste Unterschiede auf einen Blick

  • Both models cost the same under this workload — choose on capability, not price.
  • Kimi K2 Thinking has a larger context window (262K vs 131K tokens).
  • Kimi K2 is faster at 62 tok/s vs 55 tok/s.
  • Kimi K2 Thinking leads on SWE-bench coding (50.0% vs 46.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureKimi K2 ThinkingKimi K2
Intelligence Index
Composite reasoning & knowledge score (0-100)
8583
Provider
Moonshot AIMoonshot AI
Input Price
Cost per million input tokens
$0.00 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
32K16K
Throughput Speed
Tokens generated per second
55 tok/s62 tok/s
SWE-bench Verified
Real-world software engineering
50%46%
GPQA Diamond
Graduate-level reasoning
70%66.5%

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Kimi K2 Thinking
+4.0% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
Kimi K2
+13% faster generation
Context
Kimi K2 Thinking
262.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Kimi K2 ThinkingKimi K2
SWE-bench Verified (Autonomous Coding)

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

50%vs46%
GPQA Diamond (Doctorate-Level Science & Math)

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

70%vs66.5%
MMLU-Pro (Multi-task Language Understanding)

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

82.5%vs80.5%
LMSYS Chatbot Arena Elo

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

1375ptsvs1360pts
AIME (Advanced Math Competition)

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

82.5%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

55 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 Thinking0.95s
Kimi K20.64s
Est. End-to-End Response (500 tokens)TTFT + generation time
Kimi K2 Thinking10.04s
Kimi K28.70s

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 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Kimi K2 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
These models cost the same under this workload — no savings either way.Break-even

Kimi K2 Thinking · Strengths & Key Advantages

  • 262K Kontextfenster und bis zu 32K maximale Output-Token (SWE-bench Verified 50% · GPQA Diamond 70%)
  • Hohe Generierungsgeschwindigkeit von 55 Token/Sekunde bei nur 950 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (1T total (32B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Kimi K2 · Strengths & Key Advantages

  • 131K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 46% · GPQA Diamond 66.5%)
  • Hohe Generierungsgeschwindigkeit von 62 Token/Sekunde bei nur 640 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (1T total (32B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • 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.

MetricKimi K2 ThinkingKimi K2
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
28K22K
Reasoning Tokens / Task
Thinking tokens burned per task
16K8K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureKimi K2 ThinkingKimi K2
Official API Model ID (SDK)kimi-k2-thinkingkimi-k2
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-112025-07
Knowledge CutoffSeptember 2025May 2025
ArchitectureDeliberate Reasoning Open Sparse Mixture-of-ExpertsTrillion-Scale Sparse Mixture-of-Experts Transformer
Parameters1T total (32B active MoE)1T total (32B active MoE)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Free across full context window (only KV-cache VRAM scales)Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)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 & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 620 GB VRAM (4-bit Quantized) · 2150 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 620 GB VRAM (4-bit Quantized) · 2150 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Kimi K2 Thinking vs Kimi K2: Schnelle Antworten (FAQ)

Which is more intelligent, Kimi K2 Thinking or Kimi K2?

Kimi K2 Thinking is more intelligent. It scores 85 compared with 83 on the composite Intelligence Index.

Which model is faster?

Kimi K2 is faster, generating 62 tokens per second compared with 55 tokens per second.

Which model has lower latency?

Kimi K2 has lower latency, with a time to first token of 0.64s compared with 0.95s.

Which model has a larger context window?

Kimi K2 Thinking has a larger context window, supporting 262.1K tokens compared with 131.1K tokens.

Fazit

Sowohl Kimi K2 Thinking (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Kimi K2 (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Kimi K2 Thinking führt bei komplexem Reasoning und Coding, Kimi K2 liefert den höheren Token-Durchsatz und Kimi K2 Thinking ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Kimi K2 Thinking oder Kimi K2?+

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

Wie unterscheiden sich die API-Preise von Kimi K2 Thinking und Kimi K2?+

Kimi K2 Thinking kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Kimi K2 liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Kimi K2 Thinking günstiger.

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

Kimi K2 Thinking bietet 262.1K Tokens Kontext gegenüber 131.1K Tokens bei Kimi K2. Bei der Generierungsgeschwindigkeit liegt Kimi K2 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 Moonshot AI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).