Speed
Output tokens / second
Ling 3.1 Flash overview
Ling 3.1 Flash by inclusionAI
inclusionAI built Ling 3.1 Flash for coding, tool use, planning and long-context agent work: a hybrid-reasoning mixture-of-experts model with about 560B total parameters and roughly 25B active per token, served with a 256K context window.
Ling 3.1 Flash specs
- Model ID
inclusionai/ling-3.1-flash- Developer
- inclusionAI
- Context window
- 262,144 tokens
- Max output
- 32,768 tokens
- Input
- Text
- Output
- Text
- API
- OpenAI-compatible, base URL
https://api.aimlapi.com/v1
Ling 3.1 Flash is a hybrid reasoning mixture-of-experts model from inclusionAI, the model team at Ant Group. inclusionAI positions it for coding, tool use, planning and long-context agent workflows, work that runs in several steps rather than a single answer.
It carries roughly 560 billion total parameters with about 25 billion active per token. That ratio is the point of the design: the model has the capacity of a very large network while only paying to run a small slice of it per token, which is what lets it answer at the latency its name suggests.
Reasoning is part of the model rather than a separate mode. According to inclusionAI, thinking is enabled by default and can be switched off, so the same model serves both deliberate multi-step work and quick extraction where the extra tokens are not worth paying for.
The model takes text and returns text, and supports tool calling, so it can be pointed at pipelines that hand it a toolset and let it work through a task. inclusionAI targets a context window of up to one million tokens; the window served here is 256K, and that is the figure listed below.
Weights are not published yet, inclusionAI has said it plans to open-source the model, but as of this writing there is no downloadable checkpoint and no evaluation from outside Ant Group. Every benchmark figure on this page is the vendor's own.
Specification
- Total parameters: ~560B, with ~25B active per token (mixture of experts)
- Context window: 256K tokens (1M targeted by inclusionAI)
- Capabilities: tool calling, hybrid reasoning (on by default, can be disabled), streaming
Use cases
inclusionAI positions Ling 3.1 Flash for low-latency assistance, extraction and routine automation, work where a request arrives often and the answer has to come back quickly, and where the cost of a frontier-sized dense model would not be justified.
The published figures lean agentic rather than conversational: the model is measured on skill execution, automation, terminal work and codebase question answering, alongside domain suites in security, finance and health. Tool calling and hybrid reasoning are what make that range possible, so pipelines that hand the model a toolset and let it work through a task are the intended shape.
Capabilities
- Reasoning
- Streaming
- Function calling
Ling 3.1 Flash benchmarks by 
Artificial Analysis
See how Ling 3.1 Flash compares in generation speed and latency. Data retrieved Nov 3, 2026.
Benchmark results
Compare 10 models available on AI/ML API across 4 independent benchmarks.
Humanity's Last Exam
Expert-written questions across academic subjects.
Terminal-Bench 4.0
Agentic tasks completed in a command-line environment.
AA-LCR
Reasoning across long documents of about 100k tokens.
SciCode
Python coding tasks drawn from scientific research.
How to use the Ling 3.1 Flash API
View docs
Build with your agent
Give your agent the model details, code and documentation.
Paste it into your coding agent to build the integration.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "inclusionai/ling-3.1-flash",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "inclusionai/ling-3.1-flash",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "inclusionai/ling-3.1-flash",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to Ling 3.1 Flash
Ling 3.1 Flash FAQ
Is Ling 3.1 Flash available on AI/ML API?
Yes. The model was announced by inclusionAI on 30 September 2026 and is available through AI/ML API's chat completions endpoint as inclusionai/ling-3.1-flash. The model feed currently lists no per-token charge for it.
How large is Ling 3.1 Flash?
It is a mixture-of-experts model with roughly 560 billion total parameters, of which about 25 billion are active for any given token. That ratio is the point of the design: the model has the capacity of a very large network while only paying to run a small slice of it per token, which is what keeps latency low.
What is the context window?
256K tokens as served at launch, with a maximum output of 32,768 tokens. inclusionAI has said it targets a context window of up to one million tokens; until that is actually served, 256K is the figure to plan against.
What is Ling 3.1 Flash good at?
inclusionAI positions it for low-latency assistance, extraction and routine automation. Its published figures are mostly agentic rather than conversational, skill execution, automation, terminal work and codebase question answering, plus domain suites in security, finance and health, so it is aimed at pipelines that hand the model a toolset and let it work through a task.
Are the benchmark scores independently verified?
No. Every figure on this page was reported by inclusionAI at launch and none has been reproduced by a third party. The model's weights are not published either, so independent evaluation is not yet possible. Treat the numbers as the vendor's own claims.
How does it differ from Ling 3.0 Flash?
Ling 3.0 Flash is a far smaller mixture-of-experts model, roughly 124 billion total parameters with about 5 billion active, and it is available today. Ling 3.1 Flash raises both figures by roughly four to five times, to about 560 billion total and 25 billion active, and adds hybrid reasoning. Both are served with a 256K context window.
What is the Ling 3.1 Flash model ID?
Use inclusionai/ling-3.1-flash as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.