Ling 3.0 Flash
Ling 3.0 Flash is a 124B Mixture-of-Experts model from Inclusionai activating about 5.1B parameters per token, built for token-efficient agent runs across a context window of 256K tokens.
import { streamText } from 'ai'
const result = streamText({ model: 'inclusionai/ling-3.0-flash', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out Ling 3.0 Flash by Inclusionai. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
Ling 3.0 Flash
Copy link to headingProviders
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
Copy link to headingThroughput24 hours
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
Copy link to headingLatency24 hours
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Copy link to headingUptime24 hours
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
Copy link to headingAbout Ling 3.0 Flash
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.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: 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.
- Configuration: 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.
- Configuration: 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. - Zero Data Retention: Zero Data Retention is offered on a per-provider and model basis. See the documentation for details.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Copy link to headingWhen to Use Ling 3.0 Flash
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 when
- Evaluation And Prototyping:
ling-3.0-flash-freeserves 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
Copy link to headingConclusion
Ling 3.0 Flash is a sparse Mixture-of-Experts model that keeps long agent runs cheap, activating about 5.1B of 124B parameters per token across a 256K tokens window. Point inclusionai/ling-3.0-flash at AI Gateway for paid traffic, and use ling-3.0-flash-free while you are still evaluating.