I remember verifying before that Ant Ling gives a certain amount of free tokens every day (maybe 1 million?), and it’s especially good at healthcare. If you’re interested, give it a try — it’s from a big tech company, so it’s reliable. https://developer.ant-ling.com/en-US/docs/getting-started/quickstart/…

Quickstart

Source: https://developer.ant-ling.com/en-US/docs/getting-started/quickstart/

Quickstart

You can access the Ant Ling API directly using the OpenAI SDK, or via standard HTTP requests for seamless migration.

Access via SDKhttps://developer.ant-ling.com/en-US/docs/getting-started/quickstart/#via-sdk

1. Install OpenAI SDKhttps://developer.ant-ling.com/en-US/docs/getting-started/quickstart/#1-install-openai-sdk

2. Call the APIhttps://developer.ant-ling.com/en-US/docs/getting-started/quickstart/#2-call-the-api

`` from openai import OpenAI

client = OpenAI( base_url=“https://api.ant-ling.com/v1/”, api_key=“YOUR_API_KEY” )

response = client.chat.completions.create( model=“Ling-2.6-1T”, # It is recommended to use Ant Ling’s latest models — Ling-2.6-1T / Ling-2.6-flash messages=[ {“role”: “system”, “content”: “You are a helpful assistant.”}, {“role”: “user”, “content”: “Hello, please introduce Ant Ling large model”} ], max_tokens=1000 )

print(response.choices[0].message.content) ``

3. Example Outputhttps://developer.ant-ling.com/en-US/docs/getting-started/quickstart/#3-example-output

`` Ant Ling is a enterprise-level large model platform launched by Ant Group, comprising three model families:

1. Ling (General Model): Suitable for dialogue, text generation, content creation, etc.
2. Ring (Reasoning Model): Focused on deep thinking tasks such as mathematical computation, code generation, and logical reasoning.
3. Ming (Multimodal Model): Supports cross-modal understanding and generation of images, audio, and video.

The Ant Ling large model uses a MoE architecture, delivering high performance with efficient inference, and shares research results through the open-source community Inclusion AI. ``

Access via HTTP Requesthttps://developer.ant-ling.com/en-US/docs/getting-started/quickstart/#access-via-http-request

You can also call the API directly via HTTP requests:

curl --request POST \ --url https://api.ant-ling.com/v1/chat/completions \ --header 'Authorization: Bearer YOUR_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "model": "Ling-2.6-1T", "stream": true, "messages": [ { "role": "user", "content": "Hello, please introduce Ant Ling large model" } ] }'

Streaming Responsehttps://developer.ant-ling.com/en-US/docs/getting-started/quickstart/#streaming-response

For streaming output, simply add "stream": true to your request.

`` response = client.chat.completions.create( model=“Ling-2.6-1T”, messages=[ {“role”: “user”, “content”: “Hello”} ], stream=True )

for chunk in response: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end=“”) ``

Next Stepshttps://developer.ant-ling.com/en-US/docs/getting-started/quickstart/#next-steps

• Choose the right model (https://developer.ant-ling.com/en-US/docs/models/) – Learn about the use cases of Ling, Ring, and Ming model families
• Check the API reference (https://developer.ant-ling.com/en-US/docs/api-reference/) – View full API parameter descriptions
• Learn best practices (https://developer.ant-ling.com/en-US/docs/tutorials/) – Understand how to optimize prompts and invocation strategies