Kimi K2.6 TEE

Kimi K2.6 is Moonshot AI's next-generation multimodal model designed for long-horizon coding and agentic orchestration. Running inside a TEE (Trusted Execution Environment), with provider attestation support.

Vision
Tool Calling

Added Apr 21, 2026

Model weights

Pricing

Auto routing · per 1M tokens

Input: $1.50
Output: $5.25
Cache read: $0.38

Compare provider prices

Specifications

Context window: 262.1K
Max output: 65.5K
Parameters: 1T / 32B; Total / active
Avg output (7d): 2.7K tokens; Longer than 96% of models

Benchmarks

Artificial Analysis

LMArena

Vectara

Design Arena

Sourced from Artificial Analysis.

Intelligence Index

27.0

Better than 81% of models compared

Coding Index

61.8

Better than 74% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

13.0%

Better than 36% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

84.1%

Better than 33% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

819 Elo

Better than 35% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

1040 Elo

Better than 49% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

13.0%

Better than 52% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

81.0%

Better than 90% of models compared

Reasoning

HLE

Humanity's Last Exam

37.5%

Better than 85% of models compared

IFBench

Instruction-following benchmark

76.0%

Better than 95% of models compared

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.5%

Better than 35% of models compared

SciCode

Python programming for scientific computing

51.5%

Better than 60% of models compared

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

91.1%

Better than 91% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

43.9%

Better than 91% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

95.9%

Better than 96% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation