# Cache read pricing is now live on Lilac

By Lucas Ewing

---

## TL;DR

Lilac now publishes **cache read pricing** for models that support it. When repeated input tokens are served from cache, those cached tokens are billed at a lower rate than standard input tokens.

Current cache read rates:

| Model          | Input         | Cache read   | Output         |
|----------------|---------------|--------------|----------------|
| MiniMax M2.7  | $0.30 / M tokens | $0.055 / M tokens | $1.20 / M tokens |
| Kimi K2.6     | $0.70 / M tokens | $0.20 / M tokens  | $3.50 / M tokens |
| GLM 5.1       | $0.90 / M tokens | $0.27 / M tokens  | $3.00 / M tokens |

You can see the updated pricing on the [homepage](/#pricing), [MiniMax M2.7 API](/content/minimax-m2-7-api/index.html), [Kimi K2.6 API](/content/kimi-k2-6-api/index.html), and [GLM 5.1 API](/content/glm-5-1-api/index.html) pages.

---

## Why cache pricing matters

Many production LLM workloads repeat a large amount of context across requests:

- Agent instructions and tool schemas
- Repository or documentation context
- Long system prompts
- RAG prefixes that stay stable across a session
- Multi-turn workflows where most context is unchanged

Without cache pricing, every repeated token is billed like a fresh input token. With cache read pricing, repeated context can be billed at a lower rate on supported models, reducing the cost of long-context workloads without changing the API shape.

## What changed

Lilac now shows three token rates where a model supports prompt/cache reads:

- **Input** — fresh prompt tokens
- **Cache read** — repeated input tokens served from cache
- **Output** — generated completion tokens

This keeps pricing visible before you send traffic. If a model does not support cache reads, we do not show a cache price for it.

## Supported models

Cache read pricing is live for:

- **MiniMax M2.7** — $0.055 / M cached input tokens
- **Kimi K2.6** — $0.20 / M cached input tokens
- **GLM 5.1** — $0.27 / M cached input tokens

Gemma 4 remains priced with standard input and output token rates.

## Same OpenAI-compatible API

You still call Lilac through the same OpenAI-compatible endpoint:

```python
from openai import OpenAI

client = OpenAI(
    base_url="https://api.getlilac.com/v1",
    api_key="lilac_sk_...",
)

response = client.chat.completions.create(
    model="moonshotai/kimi-k2.6",
    messages=[\
        {"role": "system", "content": "You are a helpful coding assistant..."},\
        {"role": "user", "content": "Review this file for bugs."},\
    ],
)
```

For teams running long-context coding agents, document QA, support bots, or repeated evaluation workloads, cache read pricing is a direct way to make repeated context cheaper.

**Ready to try it?** [Sign up at console.getlilac.com](https://console.getlilac.com/) or start from the [cheap inference API](/content/cheap-inference-api/index.html) page.