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Kimi K2 Thinking

KimiKimi开源权重MIT · 商用许可

描述

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.

发布日期
2025-11-06
参数规模
1.0T
上下文长度
262K
支持模态
text

能力雷达图

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

排行榜排名

领域#排名分数来源
代码能力榜150
74.0
AA
通用能力榜106
67.0
AA
科学能力175
62.0
AA

基准测试分数 (LLM Stats)

(LLM Stats (zeroeval))

Finance

FinSearchComp-T347.4%自报

Healthcare

HealthBench58.0%自报

Language

MMLU-Redux94.4%自报
MMLU-Pro84.6%自报

Math

AIME 2025100.0%自报
HMMT 202597.5%自报
IMO-AnswerBench78.6%自报

Reasoning

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

Writing

WritingBench73.8%自报

AA 评测指数

(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 分类评分

(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

定价

输入价格$0.6 / 1M tokens
输出价格$2.5 / 1M tokens
混合价格(3:1)$1.075 / 1M tokens
缓存读取价格$0.15 / 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

14 个供应商

最便宜: OpenCode Zen最贵: Charm Hyper
供应商输入输出
1OpenCode Zen最便宜
$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主要
$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

比较该模型在不同 API 供应商之间的定价。

外部链接