---
title: "Kimi K2.5 for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using Kimi K2.5 with OpenClaw. Costs $0.60/M input, $3.00/M output. 262K context window. Here's when to use it and when to pick something else."
date: 2026-03-12
updatedDate: 2026-03-12
image: /images/models/provider-moonshotai-hero.jpg
keywords: "kimi k2.5 openclaw, kimi k2.5 pricing, kimi k2.5 review, moonshot ai openclaw, kimi k2.5 api"
category: model-guide
modelData:
  provider: "Moonshot AI"
  provider_slug: "moonshotai"
  full_id: "moonshotai/kimi-k2.5"
  input_cost_per_1m: 0.6
  output_cost_per_1m: 3
  max_input_tokens: 262144
  max_output_tokens: 262144
  features: ["function_calling", "vision"]
  parameter_count: 1058589420528
  architecture: "KimiK25ForConditionalGeneration"
  license: "other"
faq:
  - question: "What is the exact cost per million tokens?"
    answer: "Input tokens cost $0.6 per million and output tokens cost $3 per million."
  - question: "How large is the context window?"
    answer: "Kimi K2.5 supports up to 262,144 tokens for both input and output sequences."
  - question: "Does it support tool use?"
    answer: "Yes, it has native support for function calling and vision, making it compatible with OpenClaw's tool-using agents."
---

Current as of March 2026. Kimi K2.5 is a 1.1T parameter MoE model from Moonshot AI. The stat that stands out is 262K tokens for both input and output — you can feed it a massive document set and get a similarly long response back. At $0.60/M input, that's accessible. The $3.00/M output is where you need to budget carefully.

## Specs

| | |
|---|---|
| **Provider** | Moonshot AI |
| **Input cost** | $0.60 / M tokens |
| **Output cost** | $3.00 / M tokens |
| **Context window** | 262K tokens |
| **Max output** | 262K tokens |
| **Parameters** | 1.1T |
| **Features** | function_calling, vision |

## What it's good at

#### 262K Context + 262K Output

This combination is genuinely rare. Most models with a large context window cap output at 8K or 16K. K2.5 lets you transform or generate long artifacts from long inputs.

#### Input Pricing for the Parameter Count

$0.60/M is cheap for a 1.1T parameter model. You're getting a lot of model for the input cost — the output side is where the price reflects the scale.

#### Multimodal

Vision and function calling are both native. Useful for OpenClaw agents that need to process screenshots alongside text or hit external APIs.

## Where it falls short

#### Output Cost

$3.00/M output is 5x the input rate. If you're using the full 262K output window regularly, the bill climbs fast. Budget the output side carefully.

#### Latency

1.1T parameters means slow inference. TTFT is high, and it doesn't improve much under load.

#### API Location

The endpoint is at api.moonshot.cn. Users outside Asia will see higher latency and occasional jitter. Not ideal for time-sensitive workflows.

## Best use cases with OpenClaw

- **Large Document Transformation** — Big input, big output, reasonable input cost. This is the core use case.
- **Visual Reasoning Tasks** — The parameter scale handles complex vision tasks that stumble smaller models.

## Not ideal for

- **Real-time Chatbots** — TTFT is too high for anything interactive.
- **High-Volume Simple Tasks** — You're paying for 1.1T parameters. Use a smaller model for classification or basic summarization.

## Run it through Haimaker

Skip juggling API keys. One Haimaker key gives you access to every model on the platform. Tell OpenClaw:

```
Add Haimaker as a custom provider to my OpenClaw config. Use these details:

- Provider name: haimaker
- Base URL: https://api.haimaker.ai/v1
- API key: [PASTE YOUR HAIMAKER API KEY HERE]
- API type: openai-completions

Add the auto-router model:
- haimaker/auto (reasoning: false, context: 128000, max tokens: 32000)

Create an alias "auto" for easy switching. Apply the config when done.
```

Or skip model selection entirely — Haimaker's [auto-router](/blog/openclaw-auto-router-setup/) picks the best model for each task so you don't have to.

## OpenClaw setup

You must configure a custom provider in OpenClaw pointing to https://api.moonshot.cn/v1. Ensure your timeout settings are increased to account for the model's processing time on large context inputs.

```json
{
  "models": {
    "mode": "merge",
    "providers": {
      "moonshotai": {
        "baseUrl": "https://api.moonshot.cn/v1",
        "apiKey": "YOUR-MOONSHOTAI-API-KEY",
        "api": "openai-completions",
        "models": [
          {
            "id": "kimi-k2.5",
            "name": "Kimi K2.5",
            "cost": {
              "input": 0.6,
              "output": 3
            },
            "contextWindow": 262144,
            "maxTokens": 262144
          }
        ]
      }
    }
  }
}
```

## How it compares

- **vs GPT-4o-mini** — 4o-mini is cheaper on output but caps at 128K context. K2.5 wins when you need 262K of either.
- **vs Claude 3.5 Sonnet** — Claude is better at coding and costs $3/M input. K2.5 is cheaper to read from, worse to generate with.
- **vs DeepSeek-V3** — Both are strong. K2.5's 262K output limit is the specific differentiator for long-form generation tasks.

## Bottom line

Use it when you need both long input and long output in the same request. Watch the $3.00/M output cost — that's where this model gets expensive if you're not careful.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=kimi-k2.5-openclaw" class="cta-button">TRY KIMI K2.5 ON HAIMAKER</a>

---

*For setup instructions, see our [API key guide](/blog/openclaw-api-key-setup). For all available models, see the [complete models guide](/blog/best-models-for-openclaw).*