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Kimi K2 Thinking

KimiKimiオープンウエイトMIT · 商用利用可

説明

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.

リリース日
2025-11-06
パラメータ
1.0T
コンテキスト長
262K
モダリティ
text

能力レーダー

47
general
76
coding
93
reasoning
57
science
50
agents
0
multimodal

ランキング

ドメイン#順位スコアソース
エージェント能力27
57.0
LS
コーディングランキング77
75.0
AA
総合ランキング77
74.0
AA
科学112
66.0
AA

ベンチマークスコア (LLM Stats)

(LLM Stats (zeroeval))

Agents

BrowseCompOpenAI (2025)60.2%自己申告
Terminal-Bench47.1%自己申告

Biology

GPQANYU + Cohere + Anthropic (2023)84.5%自己申告
SciCode44.8%自己申告

Code

SWE-Bench Verified71.3%自己申告
SWE-bench Multilingual61.1%自己申告
Multi-SWE-Bench41.9%自己申告

Communication

WritingBench73.8%自己申告

Economics

FinSearchComp-T347.4%自己申告

Finance

MMLU-Pro84.6%自己申告

General

MMLU-Redux94.4%自己申告
LiveCodeBench v683.1%自己申告

Healthcare

HealthBench58.0%自己申告

Math

AIME 2025100.0%自己申告
HMMT 202597.5%自己申告
IMO-AnswerBench78.6%自己申告
Humanity's Last Exam51.0%自己申告

Reasoning

FRAMES87.0%自己申告
BrowseComp-zh62.3%自己申告
Seal-056.3%自己申告
OJBench48.7%自己申告

AA評価指数

(Artificial Analysis)
Math Index(Artificial Analysis)
94.7
Intelligence Index(Artificial Analysis)
33.5
Aime 25(MAA (Mathematical Association of America))
0.9
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.9
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.9
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.8
Gpqa(NYU + Cohere + Anthropic (2023))
0.8
Lcr(Artificial Analysis)
0.7
Ifbench(Google Research (2023))
0.7
Scicode(UIUC + Argonne National Lab (2024))
0.4
Terminalbench Hard(Stanford × Laude Institute (2026))
0.3
Hle(Center for AI Safety + Scale AI (2025))
0.2

LLM Statsカテゴリスコア

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

価格設定

入力価格$0.6 / 1Mトークン
出力価格$2.5 / 1Mトークン
混合価格(3:1)$1.075 / 1Mトークン
キャッシュ読み取り価格$0.15 / 1Mトークン

速度

トークン/秒0.0
初トークン遅延0.00s
初回答遅延0.00s

プロバイダー価格ランキング

プロバイダー価格ランキング

15 プロバイダー

最安: OpenCode Zen最高: Merge Gateway
プロバイダー入力出力
1OpenCode Zen最安
$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
6Kimiプライマリ
$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
12Moonshot AI (China)
$0.6
$2.5
13LLM Gateway
$0.6
$2.5
14Moonshot AI
$0.6
$2.5
15Merge Gateway
$0.6
$2.5

このモデルの異なるAPIプロバイダー間の価格を比較。

外部リンク