跳轉到主要內容

Kimi K2

KimiKimi開源權重MIT · 商用許可

描述

Kimi K2-Instruct-0905 is the latest, most capable version of Kimi K2, achieving state-of-the-art performance in frontier knowledge, math, and coding among non-thinking models. This Mixture-of-Experts model features 32 billion activated parameters and 1 trillion total parameters, meticulously optimized for agentic tasks. Key features include enhanced agentic coding intelligence, extended context length to 256K tokens, and a hybrid architecture trained with MuonClip optimizer on 15.5T tokens. The model achieves 65.8% on SWE-bench Verified (single attempt), 47.3% on SWE-bench Multilingual, and excels at tool use with 70.6% on Tau2-retail. It is a reflex-grade model without long thinking, designed to act and execute complex tasks seamlessly.

發布日期
2025-07-11
參數規模
1.0T
上下文長度
131K
支援模態
text

能力雷達圖

37
general
51
coding
68
reasoning
48
science
60
agents
0
multimodal

排行榜排名

領域#排名分數來源
智慧體能力模型榜102
39.0
LS
程式碼能力榜231
50.0
AA
通用能力榜240
51.0
AA
科學能力255
49.0
AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

Agents

Terminal-Bench30.0%自報
Terminus25.0%自報

Biology

GPQANYU + Cohere + Anthropic (2023)75.1%自報

Chemistry

SuperGPQA57.2%自報

Code

HumanEvalOpenAI (2021)93.3%自報
EvalPlus0.80 / 100自報
SWE-bench Verified (Agentic Coding)65.8%自報
SWE-Bench Verified65.8%自報
Aider-Polyglot60.0%自報
LiveCodeBench53.7%自報
SWE-bench Multilingual47.3%自報

Communication

Tau2 Retail70.6%自報
Tau2 Telecom65.8%自報
Tau2 Airline56.5%自報
Multi-Challenge54.1%自報

Factuality

SimpleQA31.0%自報

Finance

MMLU89.5%自報
MMLU-Pro81.1%自報
ACEBench76.5%自報

General

MMLU-Redux92.7%自報
C-Eval92.5%自報
MMLU-redux-2.090.2%自報
IFEvalGoogle Research (2023)89.8%自報
MultiPL-E85.7%自報
TriviaQA85.1%自報
CSimpleQA78.4%自報
LiveBench76.4%自報
LiveCodeBench v653.7%自報
SWE-bench Verified (Agentless)51.8%自報

Math

MATH-50097.4%自報
GSM8k97.3%自報
CBNSL95.6%自報
CNMO 202474.3%自報
MATH70.2%自報
AIME 202469.6%自報
PolyMath-en65.1%自報
AIME 202549.5%自報
HMMT 202538.8%自報
Humanity's Last Exam4.7%自報

Reasoning

AutoLogi89.5%自報
ZebraLogic89.0%自報
HumanEval-ER81.1%自報
MuSR76.4%自報
SWE-bench Verified (Multiple Attempts)71.6%自報
OJBench27.1%自報

AA 評測指數

(Artificial Analysis)
Math Index(Artificial Analysis)
57.0
Intelligence Index(Artificial Analysis)
19.7
Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))
1.0
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.8
Gpqa(NYU + Cohere + Anthropic (2023))
0.8
Aime(MAA (Mathematical Association of America))
0.7
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.6
Aime 25(MAA (Mathematical Association of America))
0.6
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.6
Lcr(Artificial Analysis)
0.5
Ifbench(Google Research (2023))
0.4
Scicode(UIUC + Argonne National Lab (2024))
0.3
Terminalbench Hard(Stanford × Laude Institute (2026))
0.2
Hle(Center for AI Safety + Scale AI (2025))
0.1

LLM Stats 分類評分

(LLM Stats (zeroeval))
Structured Output
90
Instruction Following
90
Language
90
Legal
80
Finance
80
Healthcare
80
Biology
80
Math
70
Physics
70
Frontend Development
70
Chemistry
70
Reasoning
60
General
60
Communication
60
Economics
60
Tool Calling
60
Code
50
Factuality
30
Agents
20
Vision
0

定價

輸入價格$0.57 / 1M tokens
輸出價格$2.3 / 1M tokens
混合價格(3:1)$1.002 / 1M tokens

速度

Tokens/秒0.0
首Token延遲0.00s
首回答延遲0.00s

供應商價格排行

供應商價格排行

7 個供應商

最便宜: OpenCode Zen最貴: LLM Gateway
供應商輸入輸出
1OpenCode Zen最便宜
$0.4
$2.5
2FastRouter
$0.55
$2.2
3Kimi主要
$0.57
$2.3
4OpenRouter
$0.57
$2.3
5Kilo Gateway
$0.57
$2.3
6Vercel AI Gateway
$0.57
$2.3
7LLM Gateway
$0.57
$2.3

比較該模型在不同 API 供應商之間的定價。

外部連結