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Kimi K2

KimiKimi开源权重MIT · 商用许可

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.

发布日期
2025-07-11
参数规模
1.0T
上下文长度
131K
支持模态
text

能力雷达图

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

排行榜排名

领域#排名分数来源
智能体能力模型榜102
39.0
LS
代码能力榜231
50.0
AA
通用能力榜240
51.0
AA
科学能力255
49.0
AA

基准测试分数 (LLM Stats)

(LLM Stats (zeroeval))

Agents

Terminal-Bench30.0%自报
Terminus25.0%自报

Biology

GPQANYU + Cohere + Anthropic (2023)75.1%自报

Chemistry

SuperGPQA57.2%自报

Code

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

Communication

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

Factuality

SimpleQA31.0%自报

Finance

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

General

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

Math

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

Reasoning

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

AA 评测指数

(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 分类评分

(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

定价

输入价格$0.57 / 1M tokens
输出价格$2.3 / 1M tokens
混合价格(3:1)$1.002 / 1M tokens

速度

Tokens/秒0.0
首Token延迟0.00s
首回答延迟0.00s

供应商价格排行

供应商价格排行

7 个供应商

最便宜: OpenCode Zen最贵: LLM Gateway
供应商输入输出
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

比较该模型在不同 API 供应商之间的定价。

外部链接