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Kimi K2.7 Code TEE

TEE/kimi-k2.7-code
BackTry Model
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Kimi K2.7 Code TEE

TEE/kimi-k2.7-code
BackTry Model

Kimi K2.7 Code is Moonshot AI's coding-focused agentic model built for long-horizon software engineering workflows. It supports native image input, tool calling, and forced thinking mode while running inside a Trusted Execution Environment (TEE) with attestation support.

Added Aug 15, 2026

Model weights

Context Window

262.1K

Max Output

65.5K

Avg output tokens (7d)

1.7K tokens

86%

Input Price (Auto)

$0.95/1M

Output Price (Auto)

$4.00/1M

Cache Read (Auto)

$0.19/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

26.3

Better than 80% of models compared

Coding Index

60.8

Better than 73% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

24.5%

Better than 50% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

856 Elo

Better than 43% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

1114 Elo

Better than 57% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

11.2%

Better than 50% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

79.3%

Better than 87% of models compared

Reasoning

HLE

Humanity's Last Exam

35.0%

Better than 86% of models compared

IFBench

Instruction-following benchmark

63.1%

Better than 74% of models compared

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

1.0%

Better than 46% of models compared

SciCode

Python programming for scientific computing

47.8%

Better than 50% of models compared

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

89.6%

Better than 88% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

44.7%

Better than 92% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

90.1%

Better than 84% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

79.3%

Better than 87% of models compared

Last updated Sep 11, 2026

Artificial Analysis

Providers

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