Speed
Output tokens / second
Custom pricing for production workloads
inclusionAI's Ling-3.0-flash: fast, low-cost chat model with 262K-token context, reasoning and tool calling.
Ling-3.0-flash is a chat model from inclusionAI with reasoning, streaming responses and function calling for tool use.
inclusionai/ling-3.0-flashhttps://api.aimlapi.com/v1Ling-3.0-flash is inclusionAI's fast, cost-efficient chat model with a 262,144-token context window. It supports reasoning, streaming responses and tool/function calling.
Ling-3.0-flash is documented as suited for real-world productivity workflows with end-to-end closed-loop execution across coding, general, and deep research agent tasks. It is also documented for high-frequency agentic workflows such as coding agents, tool use, document processing, and long multi-turn conversations.
It is further suited to investment workflows, including source-grounded financial search, multi-document reasoning across regulatory filings and earnings reports, valuation modeling, and spreadsheet operations. It is also documented for health-focused assistants, including medical knowledge reasoning, clinical safety, evidence-based retrieval, and multi-step clinical question answering.
For visual tasks, it is documented as handling object counting, chart and document reading, multi-step reasoning with visual evidence, and interface understanding for task automation.
See how Ling-3.0-flash compares in generation speed and latency. Data retrieved Nov 3, 2026.
Output tokens / second
Compare 12 models available on AI/ML API across 5 independent benchmarks.
Expert-written questions across academic subjects.
Graduate-level science questions written to resist web search.
Agentic tasks completed in a command-line environment.
Reasoning across long documents of about 100k tokens.
Python coding tasks drawn from scientific research.
| Usage | Price | Unit |
|---|---|---|
| Input | $0.0794 | 1M tokens |
| Output | $0.2328 | 1M tokens |
| Cached input | $0.0159 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
| $0.0794 | $0.2328 | |
| $0.0635 | $0.1904 | |
| $0.0794 | $0.2116 | |
| $0.0529 | $0.2433 | |
| Nova Lite 1.0 | $0.0635 | $0.2539 |
Give your agent the model details, code and documentation.
export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "inclusionai/ling-3.0-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-3.0-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-3.0-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());Ling-3.0-flash has a 262,144 tokens context window and can return up to 32,768 tokens.
Ling-3.0-flash takes text as input and returns text.
Use inclusionai/ling-3.0-flash as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
Ling-3.0-flash became available on July 23, 2026.
Ling-3.0-flash costs $0.0794 input and $0.2328 output per 1M tokens on AI/ML API, billed as you go with no subscription.
Yes, Ling-3.0-flash can stream responses as they are generated.
Ling-3.0-flash was built by inclusionAI.
It supports reasoning, streaming, and function calling, making it suitable for chat-based tasks that need tool use.