Kimi K2 Thinking vs Kimi K2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Kimi K2 Thinking by Moonshot AI and Kimi K2 by Moonshot AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Kimi K2 Thinking provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Kimi K2 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
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Kimi K2 Thinking
오픈 웨이트 모델Moonshot AI의 Kimi K2 Thinking 모델은 262K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
50%
Kimi K2
오픈 웨이트 모델Moonshot AI의 Kimi K2 모델은 131K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
46%
한눈에 보는 핵심 차이점
- 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%).
| Feature | Kimi K2 Thinking | Kimi K2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 85 | 83 |
Provider | Moonshot AI | Moonshot 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 text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 32K | 16K |
Throughput Speed Tokens generated per second | 55 tok/s | 62 tok/s |
SWE-bench Verified Real-world software engineering | 50% | 46% |
GPQA Diamond Graduate-level reasoning | 70% | 66.5% |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K2 Thinking · Strengths & Key Advantages
- Moonshot AI의 첨단 Deliberate Reasoning Open Sparse Mixture-of-Experts 아키텍처와 최대 262K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Kimi K2 · Strengths & Key Advantages
- Moonshot AI의 첨단 Trillion-Scale Sparse Mixture-of-Experts Transformer 아키텍처와 최대 131K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Kimi K2 Thinking | Kimi K2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 28K | 22K |
Reasoning Tokens / Task Thinking tokens burned per task | 16K | 8K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K2 Thinking | Kimi K2 |
|---|---|---|
| Official API Model ID (SDK) | kimi-k2-thinking | kimi-k2 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-11 | 2025-07 |
| Knowledge Cutoff | September 2025 | May 2025 |
| Architecture | Deliberate Reasoning Open Sparse Mixture-of-Experts | Trillion-Scale Sparse Mixture-of-Experts Transformer |
| Parameters | 1T 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 Cloud | Unlimited (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 & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / 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 Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full 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 Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / 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 Classification | Open-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 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Kimi K2 Thinking vs Kimi K2: 핵심 요약 문답 (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.
종합 평가
Verdict & Model Selection Recommendation: Choose Kimi K2 Thinking if your priority is Formal mathematical proofs, algorithmic architecture, and comprehensive scientific research. Its performance in demanding tasks justifies its operational token cost. Choose Kimi K2 if your workflow prioritizes Enterprise autonomous agents, private software engineering pipelines, and complex tool calling. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does Kimi K2 Thinking compare to Kimi K2 in API token pricing and cost per token?+
Both Kimi K2 Thinking and Kimi K2 are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Kimi K2 Thinking or Kimi K2?+
Kimi K2 Thinking supports a context window of 262K tokens (~197k words), while Kimi K2 supports 131K tokens (~98k words). Therefore, Kimi K2 Thinking can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Kimi K2 Thinking or Kimi K2?+
Kimi K2 Thinking scores 50% on SWE-bench Verified while Kimi K2 scores 46%. For autonomous software engineering and multi-file debugging, Kimi K2 Thinking holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Kimi K2 Thinking and Kimi K2?+
Kimi K2 Thinking features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Kimi K2 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Kimi K2 Thinking and Kimi K2?+
Kimi K2 Thinking generates approximately 55 tokens per second, while Kimi K2 delivers 62 tokens per second. In latency-sensitive production environments, Kimi K2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Kimi K2 Thinking and Kimi K2 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Kimi K2, and route deep architectural or complex reasoning queries to Kimi K2 Thinking. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs