LLM Reference

Kimi K2

kimi-k2

Researched 32d ago

Last refreshed 2026-05-11. Next refresh: weekly.

ProprietaryRAGAgentsLong contextClassificationJSON / Tool use

Kimi K2 is worth evaluating for rag, agents, and long context when its provider route and context window match the workload.

Decision context: RAG task fit, 3 tracked provider routes, and research from 2026-04-19.

Use it for

  • Teams evaluating rag, agents, and long context
  • Workloads that can use a 262K context window
  • Buyers comparing 3 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads

Cheapest output

$2.00

AWS Bedrock per 1M tokens

Provider routes

3

Tracked API hosts

Quality / dollar

Unknown

No task benchmark coverage yet

Freshness

2026-04-19

Researched 32d ago

aging

Top use-case fit

RAG

Included by capability and metadata signals in the decision map.

Agents

Included by capability and metadata signals in the decision map.

Long context

Included by capability and metadata signals in the decision map.

Provider price ladder

Compare all 3
ProviderInput / 1MOutput / 1MRoute
AWS Bedrock$0.500$2.00
Serverless
GCP Vertex AI$0.500$2.00
Serverless
OpenRouter$0.570$2.30
Serverless

Benchmark peer barsfor RAG

No task-mapped benchmark peers are available for this model yet.

Migration checks

No linked migration route is available for this model yet.

About

Flagship model from Moonshot AI / Kimi. Mixture of Experts architecture with 32B active parameters. Closed-source, API-only from platform.kimi.ai.

Kimi K2 has a 256K-token context window.

Kimi K2 input tokens at $0.5/1M, output at $2/1M.

Capabilities

Function CallingStructured Outputs

Rankings

Specifications

FamilyKimi K2
Released2025-07-11
Parameters1K
Context262K

Created by

Lossless long-context AI innovation

Beijing, China
Founded 2023
Website