# Ling 3.0 Flash

Ling 3.0 Flash activates roughly one sixty-fourth of its 124B parameters per token, so long agent runs stay inside tighter cost and latency budgets. Thinking is on by default and scales with how hard the task is.

- **Model ID:** `inclusionai/ling-3.0-flash`
- **Type:** chat
- **Providers:** novita
- **Context window:** 256,000
- **Maximum output tokens:** 32,000
- **Pricing:** $0.021/1M input tokens, $0.063/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/ling-3.0-flash

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

const result = streamText({
  model: 'inclusionai/ling-3.0-flash',
  prompt: 'Why is the sky blue?'
})
```

## About

Ling 3.0 Flash is Inclusionai's hybrid reasoning model, built around sparsity: 124 billion total parameters with about 5.1 billion active per token, roughly a one-in-sixty-four expert activation. Inclusionai positions it as matching or beating its own much larger flagship on most benchmarks while activating a fraction of the parameters.

The attention design is hybrid from the start rather than retrofitted. A repeating five-to-one stack alternates Kimi Delta Attention with multi-head latent attention, so linear attention keeps long inputs cheap while periodic full-attention layers preserve exact token-to-token recall. That combination is what lets a model this sparse hold up on reasoning tasks instead of only on throughput.

The context window is 256K tokens, with up to 32K tokens per response. Thinking mode is enabled by default and the model scales its thinking effort to the difficulty of the task, so simple prompts do not pay the full reasoning cost.

Ling 3.0 Flash scores 38 on the Artificial Analysis Intelligence Index, well above the median for open-weight models of comparable size. Weights are published on Hugging Face.

You can integrate Ling 3.0 Flash through AI SDK, Chat Completions API, Responses API, Messages API, or other API formats, from TypeScript or Python.

## What to consider

Inclusionai's launch comparison against a one-trillion-parameter flagship drew scrutiny. The comparison point appears to be a specific expert variant rather than the headline 1T model the launch text implied, and the launch chart was published without a readable data table. Treat the parameter-efficiency claim as directionally real and the precise margin as unverified.

Thinking is on by default. That is usually what you want on agent work, but it means a trivial prompt still spends reasoning tokens unless you turn it off, so check your output token budget on high-volume simple calls.

There is also a free routing tier, `ling-3.0-flash-free`, serving the same model. Compare the two on this page before you commit paid traffic, since for evaluation and prototyping the free tier is the cheaper path to the same output.

## When to use

### Best For

- **Token-efficient agent runs** where about 5.1B active parameters keep long loops affordable
- **Long-context reasoning** that hybrid attention keeps cheap without losing exact recall
- **Open-weight deployments** with published weights on Hugging Face
- **Difficulty-scaled thinking** that spends reasoning effort in proportion to the task
- **Cost-sensitive throughput** where a sparse model beats a dense one of similar capability

### Consider Alternatives When

- **Evaluation and prototyping** are the use case, where `ling-3.0-flash-free` serves the same model at no cost
- **High-volume simple calls** dominate, since thinking is on by default and spends tokens
- **Frontier-tier reasoning** is required, where its Artificial Analysis score sits below the leaders
- **Verified head-to-head claims** matter, since the launch comparison was not fully substantiated

## Best for

- **Token-Efficient Agent Runs:** About 5.1B active parameters keeping long loops affordable
- **Long-Context Reasoning:** Hybrid attention that stays cheap without losing exact recall
- **Open-Weight Deployments:** Published weights on Hugging Face
- **Difficulty-Scaled Thinking:** Reasoning effort proportional to the task
- **Cost-Sensitive Throughput:** Sparsity beating a dense model of similar capability

## Consider alternatives

- **Evaluation And Prototyping:** `ling-3.0-flash-free` serves the same model at no cost
- **High-Volume Simple Calls:** Thinking is on by default and spends tokens
- **Frontier-Tier Reasoning:** Its Artificial Analysis score sits below the leaders
- **Verified Comparisons:** The launch head-to-head claim was not fully substantiated

## Frequently asked questions

### What is Ling 3.0 Flash built for?

Token-efficient agentic inference. About 5.1 billion of its 124 billion parameters activate per token, so multi-step runs fit inside tighter cost and latency budgets.

### How is Ling 3.0 Flash different from ling-3.0-flash-free?

It is the same model on a paid routing tier. Use the free tier for evaluation and prototyping, and this one for production traffic. Compare both on this page.

### What is the context window for Ling 3.0 Flash?

The context window is 256K tokens, with up to 32K tokens per response.

### How does the hybrid attention design work?

A repeating five-to-one stack alternates Kimi Delta Attention with multi-head latent attention. Linear attention keeps long inputs cheap, and periodic full-attention layers preserve exact token-to-token recall.

### Is thinking mode on by default?

Yes, and the model scales thinking effort to task difficulty. On high-volume simple calls, check your output token budget, since reasoning tokens are still spent unless you disable it.

### Are the weights open?

Yes. Inclusionai publishes the weights on Hugging Face.

### Does Ling 3.0 Flash really match a one-trillion-parameter model?

The parameter-efficiency claim is directionally supported but the precise margin is not. The launch comparison point appears to be a specific expert variant rather than the headline flagship, and no readable data table accompanied the chart.

### Does Ling 3.0 Flash support Zero Data Retention?

Zero Data Retention is not currently available for this model. Zero Data Retention is offered on a per-provider basis. See https://vercel.com/docs/ai-gateway/capabilities/zdr for details.

## Links

- [Model page](https://vercel.com/ai-gateway/models/ling-3.0-flash)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://novita.ai/models/model-detail/inclusionai-ling-3.0-flash)