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Kimi K2 Thinking

KimiKimi오픈 웨이트MIT · 상업적 사용 가능

설명

Kimi K2 Thinking is the latest, most capable version of open-source thinking model. Starting with Kimi K2, it is built as a thinking agent that reasons step-by-step while dynamically invoking tools. It sets a new state-of-the-art on Humanity's Last Exam (HLE), BrowseComp, and other benchmarks by dramatically scaling multi-step reasoning depth and maintaining stable tool-use across 200–300 sequential calls. At the same time, K2 Thinking is a native INT4 quantization model with 256k context window, achieving lossless reductions in inference latency and GPU memory usage. Key features include deep thinking & tool orchestration with end-to-end training to interleave chain-of-thought reasoning with function calls, native INT4 quantization via Quantization-Aware Training (QAT) achieving lossless 2x speed-up, and stable long-horizon agency maintaining coherent goal-directed behavior across up to 200–300 consecutive tool invocations.

출시일
2025-11-06
파라미터
1.0T
컨텍스트 길이
262K
모달리티
text

능력 레이더

41
general
85
coding
93
reasoning
65
science
50
agents
0
multimodal

랭킹

도메인#순위점수소스
코딩 랭킹145
74.0
AA
종합 랭킹101
67.0
AA
과학170
62.0
AA

벤치마크 점수 (LLM Stats)

(LLM Stats (zeroeval))

Finance

FinSearchComp-T347.4%자체 보고

Healthcare

HealthBench58.0%자체 보고

Language

MMLU-Redux94.4%자체 보고
MMLU-Pro84.6%자체 보고

Math

AIME 2025100.0%자체 보고
HMMT 202597.5%자체 보고
IMO-AnswerBench78.6%자체 보고

Reasoning

FRAMES87.0%자체 보고
GPQANYU + Cohere + Anthropic (2023)84.5%자체 보고
LiveCodeBench v683.1%자체 보고
SWE-Bench Verified71.3%자체 보고
BrowseComp-zh62.3%자체 보고
SWE-bench Multilingual61.1%자체 보고
BrowseCompOpenAI (2025)60.2%자체 보고
Seal-056.3%자체 보고
Humanity's Last Exam51.0%자체 보고
OJBench48.7%자체 보고
Terminal-Bench47.1%자체 보고
SciCode44.8%자체 보고
Multi-SWE-Bench41.9%자체 보고

Writing

WritingBench73.8%자체 보고

AA 평가 지수

(Artificial Analysis)
Math Index(Artificial Analysis)
94.7
Aime 25(MAA (Mathematical Association of America))
94.7
Tau2(Sierra + U Toronto + Vector Institute (2025))
93.0
Livecodebench(UC Berkeley + MIT + Cornell (2024))
85.3
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
84.8
Gpqa(NYU + Cohere + Anthropic (2023))
83.8
Lcr(Artificial Analysis)
72.0
Ifbench(Google Research (2023))
68.1
Terminalbench Hard(Stanford × Laude Institute (2026))
31.1
Hle(Center for AI Safety + Scale AI (2025))
23.8
Intelligence Index(Artificial Analysis)
22.0

LLM Stats 카테고리 점수

(LLM Stats (zeroeval))
Language
90
Legal
80
Math
80
Finance
80
Reasoning
70
Search
70
Frontend Development
70
General
70
Healthcare
70
Communication
70
Creativity
70
Writing
70
Physics
60
Biology
60
Chemistry
60
Agents
50
Code
50
Vision
50

가격

입력 가격$0.6 / 1M 토큰
출력 가격$2.5 / 1M 토큰
혼합 가격 (3:1)$1.075 / 1M 토큰
캐시 읽기 가격$0.15 / 1M 토큰

속도

토큰/초0.0
첫 토큰 지연0.00s
첫 응답 지연0.00s

공급자 가격 순위

공급자 가격 순위

14개 공급자

최저가: OpenCode Zen최고가: Charm Hyper
공급자입력출력
1OpenCode Zen최저가
$0.4
$2.5
2Vercel AI Gateway
$0.47
$2
3Helicone
$0.48
$2
4Alibaba (China)
$0.574
$2.294
5302.AI
$0.575
$2.3
6Kimi주요
$0.6
$2.5
7NanoGPT
$0.6
$2.5
8OpenRouter
$0.6
$2.5
9ZenMux
$0.6
$2.5
10NovitaAI
$0.6
$2.5
11Kilo Gateway
$0.6
$2.5
12DevPass (LLM Gateway)
$0.6
$2.5
13Merge Gateway
$0.6
$2.5
14Charm Hyper
$0.6
$2.5

이 모델의 다양한 API 공급자 간 가격 비교.

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