Kimi K2
Prior Moonshot coding-strong MoE model, strong agent/tool use at competitive pricing.
Pricing
Specifications
Benchmark Scores
Strengths
- Strong coding
- Tool use
- Value pricing
Weaknesses
- Superseded by Kimi K3 on context/vision
Historical Intelligence
Pricing, context, and benchmark trends over time
Pricing History
Input and output price per million tokens over time
Context Window Growth
Context window tokens over time
Coding Score Progress
Benchmark score over time (0–100)
Reasoning Score Progress
Benchmark score over time (0–100)
Kimi K2 Evolution
Combined pricing trend and benchmark performance
Kimi K2 vs related models
Capability radar vs semantically related catalog models
Intelligence Graph
Provider hub →Provider Overview
Moonshot AI builds the Kimi model family, including Kimi K3 — a frontier open-class model with native vision and a 1M-token context window.
2 models tracked · Documentation
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Confidence91%DeepSeek V3 0324
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Confidence87%DeepSeek V3
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Recommended Comparisons
Side-by-side analysis with semantically similar models
Kimi K2 vs DeepSeek V4
Side-by-side pricing, benchmarks, and capabilities
Why: similar coding performance, similar reasoning scores
Confidence91%Kimi K2 vs DeepSeek V3 0324
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Confidence87%Kimi K2 vs DeepSeek V3
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Confidence87%Kimi K2 vs Codestral
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Confidence87%Kimi K2 vs Mistral Small
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Confidence86%Kimi K2 vs Llama 3.3 70B
Side-by-side pricing, benchmarks, and capabilities
Why: comparable context window
Confidence85%
Best Alternatives
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Why: +20 overall benchmark score, comparable capability profile
Confidence64%GPT-5
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Why: +19 overall benchmark score, similar coding performance
Confidence71%Claude Opus 4.1
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Cheaper Alternatives
Lower-cost models with comparable attributes
DeepSeek V4
Next-gen DeepSeek general model emphasizing coding and cost efficiency.
Why: Saves $1.60/M combined tokens vs Kimi K2
Confidence91%DeepSeek V3 0324
Updated V3 with improved instruction following and 128K context.
Why: Saves $1.73/M combined tokens vs Kimi K2
Confidence87%DeepSeek V3
Highly efficient MoE model rivaling frontier closed models.
Why: Saves $1.73/M combined tokens vs Kimi K2
Confidence87%Codestral
Code-specialized Mistral model for IDE and agent use.
Why: Saves $1.90/M combined tokens vs Kimi K2
Confidence87%
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Why: Token estimation for prompt workloads
Context Window Utilization
Calculate how much of a model's context window you're consuming.
Why: 131K context window analysis
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Timeline Highlights
Latest Releases
Recent launches from related providers