Answers from the documentation; checking which model is on duty
Hi - I answer from the OpenSmartRoute documentation: routing, the API, plans and quotas, self-hosting. Ask away, or open a support ticket if you need a person.
Grounded in the docs - follow a source before acting on it.
Kimi K2 Thinking - Model - OpenSmartRoute
Modelv1.0.0
Kimi K2 Thinking
Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning.
Reference model card from the OpenRouter listing - model moonshotai/kimi-k2-thinking. Prices are per million tokens as listed; ratings and installs below are from this marketplace.
Kimi K2 Thinking
Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...
Facts
Vendor
Moonshot AI
Context window
262,144 tokens
Max output
98,304 tokens
Input price
$0.60 / 1M tokens
Output price
$2.50 / 1M tokens
Input
text
Output
text
Tool calling
yes
Structured outputs
yes
Reasoning
yes
Open weights
moonshotai/Kimi-K2-Thinking
Endpoint: https://openrouter.ai/api/v1 (OpenAI-compatible), model moonshotai/kimi-k2-thinking
Use it
Copy one of these into your project. Installing also returns the manifest and these snippets.
# after Install: the listing is in your workspace's routing pool - nothing else to configurecurl-s-X POST https://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'# or pin it on the OpenAI-compatible endpoint: {"model": "model-moonshotai-kimi-k2-thinking", ...}
Installed into a catalogue, chosen by a router
Install Kimi K2 Thinking and it becomes one more candidate the router can pick - when it fits.
A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.
