이 모델의 강점은 무엇인가요?

Massive over 10 trillion parameter count Wide 256K context window Specialized for advanced reasoning capabilities

이 모델의 약점은 무엇인가요?

Extremely large model size over 1TB Closed distribution format Increased resource consumption

어떤 용도에 가장 적합한가요?

Executing complex logical reasoning Analyzing and processing large document sets Advanced problem-solving tasks

모델 목록으로

Moonshot AI독점

Kimi K2 Thinking

Name: Kimi K2 Thinking
Author: Moonshot AI

Kimi K2 Thinking is an inference model developed by Moonshot AI. It features a large model configuration with approximately 10.4 trillion parameters and a 256K long context window.

파라미터

10400.0B

컨텍스트

256K

라이선스

https://github.com/openai/gpt-2/blob/master/LICENSE

출시일

2025-11-06

API 가격

이 모델의 API 가격 정보는 현재 공개되지 않았습니다

강점

・Massive over 10 trillion parameter count
・Wide 256K context window
・Specialized for advanced reasoning capabilities

약점

・Extremely large model size over 1TB
・Closed distribution format
・Increased resource consumption

활용 사례

・Executing complex logical reasoning
・Analyzing and processing large document sets
・Advanced problem-solving tasks

심층 분석

Architecture

MoE (1T total, 32B active)

Mixture-of-Experts reasoning model

Context Window

256K tokens

API Pricing (Input/Output)

$0.15 / $0.60 per 1M tokens

Via Kimi API Platform

Release Date

November 2025

Training Cost

$4.6M

Reported by Implicator

Tool Calls

Up to 300 sequential

Agentic capability

강점

・Outperforms GPT-5 on HLE reasoning benchmark
・Open-weight model
・Supports up to 300 sequential tool calls for agentic tasks
・Competitive pricing at $0.15/$0.60 per 1M tokens
・Strong deep reasoning capabilities

약점

・No vision support
・Discontinued May 25 2026 (superseded by kimi-k2.6)
・Higher latency than turbo variants
・Limited to text-only modality

경쟁사 비교

Model	Arena	SWE	GPQA	Price
GPT-5	-	-	-	Higher
Claude Opus 4.5	-	-	-	Higher
DeepSeek V3.2	-	-	-	Comparable

개요

Kimi K2 Thinking is Moonshot AI's trillion-parameter reasoning model with 32B active parameters per token. It outperforms GPT-5 on HLE reasoning tests and supports up to 300 sequential tool calls, making it one of the most capable open-weight agentic reasoning models available.

벤치마크 및 성능

Outperforms GPT-5 on HLE benchmark; competitive on general reasoning, math, and coding benchmarks. Strong agentic performance with 300 sequential tool call support.

상세 비교

Competes with GPT-5, Claude Opus 4.5, and DeepSeek V3.2 on reasoning tasks. Cost of $4.6M for training is significantly lower than frontier closed-source models.

커뮤니티 평가

Well-received in the open-source community. Available on HuggingFace. Active development with turbo variants for production use.

활용 사례

Best suited for complex multi-step reasoning tasks, agentic workflows requiring deep analysis, coding with tool use, and scenarios where deep reasoning is prioritized over latency.