Skip to main content
Model comparison

Kimi K3 vs Kimi K2: what actually changed

Kimi K3 is not a small refresh of the original Kimi K2 Instruct model. It expands total scale, context, modality, reasoning, and architecture for a different class of long-running work.

Side-by-side comparison

AreaKimi K3Kimi K2
Total parameters2.8 trillion1 trillion
Activated parametersNot used for this comparison32 billion
Published context1 million tokens128,000 tokens
InputText and imageText
Reasoning modeMaximum reasoningReflex-grade, non-thinking
Attention designKDA plus Attention ResidualsMLA
Best fitLong coding, knowledge work, multimodal agentsFast text, coding, and tool use

“Kimi K2” here means the original Kimi K2 Instruct generation, not later K2.5, K2.6, K2.7 Code, or K2 Thinking variants.

The biggest practical change

The context window grows from 128K to 1M tokens. That makes larger repository maps, longer evidence sets, and extended agent traces possible in one model call. It does not remove the need to select relevant context: duplicated or low-quality input still increases latency and cost.

Native image input also changes the workflow. Kimi K3 can reason about a screenshot, chart, or interface alongside text, while the original Kimi K2 Instruct was a text model.

When Kimi K3 is the better choice

  • Large repository investigation across many related files
  • Long research synthesis with conflicting evidence
  • Visual debugging from screenshots, diagrams, or charts
  • Agent workflows that need repeated planning and correction
  • Tasks where deeper reasoning matters more than lowest latency

When Kimi K2 can still make sense

The original Kimi K2 remains relevant for teams that self-host its open weights, have prompts tuned to its tool-call format, or need a lighter text-only path. Migrating should follow a real task evaluation instead of model size alone.

Review the current K3 specification in the official Kimi documentation and the original K2 architecture in Moonshot AI’s official Kimi K2 repository.