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CN
Moonshot

Kimi K2 Instruct

Moonshot
Overview

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Quick Links
Key Metrics
Max Input
128,000
Tokens
Max Output
128,000
Tokens
Throughput
-
tok/s
Latency
-
ms
Input Price
$0.6
Per 1M Tokens
Cached Input Price
$0.15
Per 1M Tokens
Output Price
$2.5
Per 1M Tokens
Blended Price
$2.025
Per 1M Tokens
Model Information
Release Details

Released

11 Jul 2025

Knowledge Cutoff

Apr 2025

License

Modified MIT

Model Architecture

Parameters

-

Training Data

-

Context Window

Input Context Length

128,000 tokens

Output Context Length

128,000 tokens

Key Features
Web Access
No

Real-time access to current web information

Multimodal
No

Ability to process multiple data types (text, images, etc.)

Reasoning
No

Advanced logical and deductive reasoning capabilities

Fine-Tunable
Yes

Can be customized for specific use cases

Model Release & Updates
11 Jul 2025
Model Released
Model first made available to the public
11 Jul 2025
Model Announced
Model first introduced to the public
20 Jan 2025
Previous version
Benchmarks & Performance Comparison
ACEBench
76.50%
#1/1
AI Stats Score
2119.83
#21/205
Aider-Polyglot
60.00%
#14/28
AIME 2024
69.60%
#27/40
AIME 2025
49.50%
#26/29
AutoLogi
89.50%
#1/1
CNMO 2024
74.30%
#1/1
Confabulations
20.38
Lower is better
#27/39
Elimation Game
3.77
#28/29
EQ-Bench 3
1549.10
#1/30
GPQA Diamond
75.10%
#25/98
HMMT 2025
38.80%
#3/3
IFEval
89.80%
#1/5
LiveBench
76.40%
#1/15
MATH 500
97.40%
#1/1
MMLU
89.50%
#2/13
MMLU Pro
51.10%
#1/1
MMLU Redux
92.70%
#1/1
MMLU-Pro
81.10%
#4/8
PolyMath-en
65.10%
#1/1
SimpleQA
31.00%
#6/13
SuperGPQA
57.20%
#1/2
SWE-Bench
71.60%
#2/15
Tau 2 Airline
56.50%
#1/1
Tau 2 Retail
71.00%
#1/1
Tau 2 Telecom
65.80%
#1/1
Terminal Bench
30.00%
#2/2
Thematic Generalisation
1.94
Lower is better
#18/36
ZebraLogic
89.00%
#1/1