Intern S2 (free)
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Intern S2 (free)

intern-s2-freellms.txt
intern-ai
New
Intern-S2-Preview is InternLM’s 35B scientific multimodal foundation model, designed for professional scientific reasoning, research agents, multimodal understanding, and complex scientific workflows. It scales the difficulty, diversity, and coverage of scientific tasks across the full training pipeline, while strengthening scientific agent capabilities, time-series modeling, molecular and material structure understanding, and efficient reasoning. Compared with the trillion-parameter Intern-S1-Pro, Intern-S2-Preview achieves comparable performance on multiple core scientific tasks with only 35B parameters, while retaining strong general reasoning and multimodal capabilities.

Pricing

  • Input Tokens: $0 /M tokens
  • Output Tokens: $0 /M tokens
  • Cache Read: $0 /M tokens

Input Modalities

  • Text
  • Vision

Output Modalities

  • Text

Context length

  • 262K tokens

Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="intern-s2-free",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

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

Frequently asked questions

What is Intern S2 (free)?

Intern-S2-Preview is InternLM’s 35B scientific multimodal foundation model, designed for professional scientific reasoning, research agents, multimodal understanding, and complex scientific workflows. It scales the difficulty, diversity, and coverage of scientific tasks across the full training pipeline, while strengthening scientific agent capabilities, time-series modeling, molecular and material structure understanding, and efficient reasoning. Compared with the trillion-parameter Intern-S1-Pro, Intern-S2-Preview achieves comparable performance on multiple core scientific tasks with only 35B parameters, while retaining strong general reasoning and multimodal capabilities.