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Automatic Speech Recognition Embeddings Reranker Text Generation Text To Image Text To Music Text To Speech Text To Video World Model Zero Shot Image Classification

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moonshotai/

Kimi-K2.6

$0.75

$3.50

out

$0.15

cached

/ 1M tokens

Tier	Input	Output	Cached input
Priority (1.5×)Learn More	$1.125	$5.25	$0.225

per 1M tokens

Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.

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Kimi-K2.6

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Model Information

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1. Model Introduction

Key Features

Long-Horizon Coding: K2.6 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization.
Coding-Driven Design: K2.6 is capable of transforming simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision.
Elevated Agent Swarm: Scaling horizontally to 300 sub-agents executing 4,000 coordinated steps, K2.6 can dynamically decompose tasks into parallel, domain-specialized subtasks, delivering end-to-end outputs from documents to websites to spreadsheets in a single autonomous run.
Proactive & Open Orchestration: For autonomous tasks, K2.6 demonstrates strong performance in powering persistent, 24/7 background agents that proactively manage schedules, execute code, and orchestrate cross-platform operations without human oversight.

2. Model Summary


Architecture	Mixture-of-Experts (MoE)
Total Parameters	1T
Activated Parameters	32B
Number of Layers (Dense layer included)	61
Number of Dense Layers	1
Attention Hidden Dimension	7168
MoE Hidden Dimension (per Expert)	2048
Number of Attention Heads	64
Number of Experts	384
Selected Experts per Token	8
Number of Shared Experts	1
Vocabulary Size	160K
Context Length	256K
Attention Mechanism	MLA
Activation Function	SwiGLU
Vision Encoder	MoonViT
Parameters of Vision Encoder	400M

3. Evaluation Results

Benchmark	^{Kimi K2.6}	^{GPT-5.4 ^(xhigh)}	^{Claude Opus 4.6 ^{(max effort)}}	^{Gemini 3.1 Pro ^{(thinking high)}}	^{Kimi K2.5}
Agentic
HLE-Full (w/ tools)	54.0	52.1	53.0	51.4	50.2
BrowseComp	83.2	82.7	83.7	85.9	74.9
BrowseComp (Agent Swarm)	86.3	82.7	83.7	85.9	78.4
DeepSearchQA (f1-score)	92.5	78.6	91.3	81.9	89.0
DeepSearchQA (accuracy)	83.0	63.7	80.6	60.2	77.1
WideSearch (item-f1)	80.8	-	-	-	72.7
Toolathlon	50.0	54.6	47.2	48.8	27.8
MCPMark	55.9	62.5*	56.7*	55.9*	29.5
Claw Eval (pass^3)	62.3	60.3	70.4	57.8	52.3
Claw Eval (pass@3)	80.9	78.4	82.4	82.9	75.4
APEX-Agents	27.9	33.3	33.0	32.0	11.5
OSWorld-Verified	73.1	75.0	72.7	-	63.3
Coding
Terminal-Bench 2.0 (Terminus-2)	66.7	65.4*	65.4	68.5	50.8
SWE-Bench Pro	58.6	57.7	53.4	54.2	50.7
SWE-Bench Multilingual	76.7	-	77.8	76.9*	73.0
SWE-Bench Verified	80.2	-	80.8	80.6	76.8
SciCode	52.2	56.6	51.9	58.9	48.7
OJBench (python)	60.6	-	60.3	70.7	54.7
LiveCodeBench (v6)	89.6	-	88.8	91.7	85.0
Reasoning & Knowledge
HLE-Full	34.7	39.8	40.0	44.4	30.1
AIME 2026	96.4	99.2	96.7	98.3	95.8
HMMT 2026 (Feb)	92.7	97.7	96.2	94.7	87.1
IMO-AnswerBench	86.0	91.4	75.3	91.0*	81.8
GPQA-Diamond	90.5	92.8	91.3	94.3	87.6
Vision
MMMU-Pro	79.4	81.2	73.9	83.0*	78.5
MMMU-Pro (w/ python)	80.1	82.1	77.3	85.3*	77.7
CharXiv (RQ)	80.4	82.8*	69.1	80.2*	77.5
CharXiv (RQ) (w/ python)	86.7	90.0*	84.7	89.9*	78.7
MathVision	87.4	92.0*	71.2*	89.8*	84.2
MathVision (w/ python)	93.2	96.1*	84.6*	95.7*	85.0
BabyVision	39.8	49.7	14.8	51.6	36.5
BabyVision (w/ python)	68.5	80.2*	38.4*	68.3*	40.5
V* (w/ python)	96.9	98.4*	86.4*	96.9*	86.9