Kimi uncensored and abliterated models
Kimi K3.1 Pro Uncensored is a server-managed instruction variant of Moonshot AI’s Kimi K3.1 Pro. Compare the standard and uncensored models by token price, context and API setup.
Compare Kimi API IDs and prices
| Model / exact API ID | Input / 1M | Output / 1M | Actions |
|---|---|---|---|
 Kimi K3.1 Prokimi-k3.1-pro |
Chat | ||
Kimi K3.1 Pro UncensoredUncensoredkimi-k3.1-pro-uncensored |
Chat |
Standard, uncensored or abliterated?
Uncensored models are reduced-refusal language model variants for open-ended generation, coding and creative work. Uncensored variants use prompting, fine-tuning, weight changes, or a combination of these techniques.
Abliteration reduces refusal-related behavior by modifying model weights. RouterHub serves uncensored and abliterated variants under separate API IDs, with their own token prices and context limits.
RouterHub’s prompt-profile uncensored variants use a server-managed instruction profile. Customer system/developer instructions can be replaced by that profile; put task-specific requirements in the user message.
Connect Kimi to your application
For a model that lists chat, use https://routerhub.si/v1/chat/completions with an OpenAI-compatible SDK. OpenCode, Continue and compatible Cline configurations support Chat Completions; Claude Code uses Messages-compatible models.
How to choose between Kimi variants
- Fix the task. Use the same inputs, output format, reasoning setting and output budget for each exact model ID.
- Separate refusal from failure. Record an explicit refusal separately from a timeout, empty response, invalid JSON, wrong answer or truncated generation.
- Keep every attempt. Repeat your tests and retain unsuccessful outputs, not just the best answer.
- Measure useful work. Compare task acceptance, instruction following, tool-call validity and cost per successful task, not token price alone.
- Re-test changes. A hosted alias or model update can change behavior even when your client configuration stays the same.
For ordinary uncached text usage, estimate cost as input tokens × input price / 1,000,000 + output tokens × output price / 1,000,000. Reported cache reads and writes use their own published rates. The final Usage record, not time spent waiting or a headline token limit, is the billing reference.
Example: at $0.20 per million input tokens and $0.60 per million output tokens, 10,000 uncached input tokens and 2,000 output tokens cost an estimated $0.0032. This is an illustrative calculation, not a quoted price for a particular model.
For model selection, divide the total cost of all measured attempts by the number of acceptable results. A lower token rate is not automatically a lower cost per completed task.