InclusionAI: Ling 2.6 Flash
inclusionai/ling-2.6-flash
Access Ling 2.6 Flash from InclusionAI using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "inclusionai/ling-2.6-flash"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "inclusionai/ling-2.6-flash"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="inclusionai/ling-2.6-flash",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "inclusionai/ling-2.6-flash",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Ling 2.6 Flash is a high-efficiency open-weights instruct model from InclusionAI (Ant Group), featuring 104B total parameters with only 7.4B active via a Mixture-of-Experts architecture. It supports a 262K-token context window and is purpose-built for agentic workflows, coding, and document processing.
The model scores 26 on the Artificial Analysis Intelligence Index — nearly double the median of 13 among comparable open-weight non-reasoning models, and a 10-point jump over its predecessor Ling-flash-2.0. It also achieves 59.3% on GPQA Diamond.
Trained with Agentic Reinforcement Learning, Ling 2.6 Flash is optimized for tool use, terminal operations, and multi-step agent tasks while keeping token consumption notably low. A strong choice for developers building cost-sensitive agent pipelines or high-throughput automation that still demands capable reasoning and code generation.
Context Window 262K
tokens
Max Output 33K
tokens
Input Cost $0.01
per million tokens
Output Cost $0.03
per million tokens
Release Date Apr 21, 2026
Model Playground
Try Ling 2.6 Flash instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Ling 2.6 Flash performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 59.3% |
| Humanity's Last Exam Cross-domain reasoning | 6.2% |
| SciCode Scientific programming | 27.1% |
| IFBench Instruction following | 57.4% |
| LCR Long-context reasoning | 25.0% |
| Terminal-Bench Hard Agentic terminal tasks | 21.2% |
| τ²-Bench Tool use / agents | 86.0% |
Scores sourced from Artificial Analysis.
Find other InclusionAI models →
Ring 2.6 1T
Ring 2.6 1T is a trillion-parameter open-weights reasoning model from InclusionAI (Ant Group), released under the MIT license. It uses a Mixture-of-Experts architecture with approximately 63B active parameters per token and supports a 262K context window with up to 66K output tokens. The model offers adaptive reasoning effort through "high" and "xhigh" modes, letting developers tune thinking depth against token cost based on task complexity. It is purpose-built for agentic workflows, coding agents, tool use, and long-horizon multi-step task execution. Ring 2.6 1T scores 95.83 on AIME 2026, 88.27 on GPQA Diamond, and 87.60 on PinchBench in agent mode — surpassing GPT-5.4 and Gemini 3.1 Pro on that benchmark. A strong pick for developers building autonomous agent systems or complex reasoning pipelines.
ChatLing 2.6 1T
Ling 2.6 1T is InclusionAI's trillion-parameter flagship non-reasoning model, built by Ant Group's AGI initiative. It uses a Mixture-of-Experts architecture with approximately 50 billion active parameters per token, employing a "fast thinking" approach that reduces token costs to roughly a quarter of comparable models while maintaining top-tier output quality. The model targets advanced coding, complex reasoning, and large-scale agent workflows. It achieves state-of-the-art results on benchmarks like AIME 2025 and SWE-bench Verified, and ranks first among open-source models on ArtifactsBench for front-end code generation. On the Artificial Analysis Intelligence Index, it scores 34 — far above the median of 13 for comparable open-weight non-reasoning models. With a 262K context window and strong tool-use capabilities out of the box, Ling 2.6 1T is a strong fit for developers building autonomous agents or cost-sensitive pipelines that need flagship-level reasoning without a dedicated thinking model.
Frequently Asked Questions
You can access Ling 2.6 Flash by InclusionAI through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Ling 2.6 Flash to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.01 |
| Output | $0.03 |
Ling 2.6 Flash was created by InclusionAI and released on Apr 21, 2026.
Ling 2.6 Flash supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Ling 2.6 Flash can generate up to 33K tokens in a single response.
Ling 2.6 Flash scores 26.2 on the Artificial Analysis Intelligence Index, outperforming 62% of tracked models. On coding, it scores 23.2 (outperforms 56% of models).
Yes — the Ling 2.6 Flash API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Ling 2.6 Flash to your app without worrying about API keys or setup.
Read the Docs View Tutorials