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Zelili AI

Intern-S1-Pro

From Shanghai AI Laboratory

Trillion-Scale Open-Source MoE Multimodal Scientific Reasoning Model – Leading AI4Science Performance with Strong General Capabilities
Tool Release Date

21 Aug 2025

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0.0

Pricing Model

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$0/Month

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About This AI

Intern-S1-Pro is a massive trillion-scale Mixture-of-Experts (MoE) multimodal foundation model developed by InternLM (Shanghai AI Laboratory).

It scales to 1 trillion total parameters with 512 experts, activating only 8 experts per token (approximately 22 billion active parameters) for efficient inference.

The model excels in advanced scientific reasoning across key AI4Science domains including chemistry, materials science, life sciences, earth sciences, and more, delivering state-of-the-art results competitive with leading closed-source models.

It maintains strong general multimodal capabilities, supporting text, image, and potentially other modalities for vision-language tasks, tool calling (e.g., real-time data fetching via APIs), and dynamic thinking mode switching for enhanced reasoning quality.

Key technical innovations include STE routing for stable MoE training, grouped routing for balanced parallelism, Fourier Position Encoding (FoPE) for better signal representation, and upgraded time-series modeling capable of handling long heterogeneous sequences (10^0 to 10^6 points).

Released open-source under Apache-2.0 license on August 21, 2025, with full weights on Hugging Face, it is deployable via frameworks like LMDeploy, vLLM, and SGLang.

The associated paper (arXiv:2508.15763) details its architecture, training on massive multimodal data, and superior performance on benchmarks like OpenCompass and VLMEvalKit.

Ideal for researchers, scientists, and developers needing powerful open-source tools for scientific discovery, multimodal analysis, and complex reasoning tasks without relying on proprietary APIs.

Key Features

  1. Trillion-scale MoE architecture: 1T total parameters, 512 experts, only 22B activated per token for high efficiency
  2. State-of-the-art scientific reasoning: Leads open-source models in AI4Science tasks across chemistry, materials, life-science, earth domains
  3. Strong multimodal capabilities: Handles image-text tasks, vision-language understanding, and general benchmarks competitively
  4. Tool calling support: Integrates external APIs for real-time data fetching and agentic workflows
  5. Dynamic thinking mode: Toggleable enhanced reasoning (enable_thinking=True/False) for better quality on complex queries
  6. Advanced time-series modeling: Processes long heterogeneous sequences (up to 10^6 points) with Fourier Position Encoding
  7. STE and grouped routing: Enables stable, efficient training and inference for massive MoE models
  8. OpenAI-compatible API: Supports tool use and chat templates in standard inference frameworks
  9. High benchmark performance: Top results on OpenCompass, VLMEvalKit, and scientific-specific evaluations
  10. Apache-2.0 license: Fully open-source weights, code, and deployment guides available

Price Plans

  1. Free ($0): Fully open-source model weights, code, and documentation under Apache-2.0 with no usage fees or limits (self-hosted)
  2. Self-Hosted (Hardware Costs): Inference requires GPUs/TPUs; no direct subscription but compute expenses apply
  3. Enterprise (Custom): Potential premium support or hosted options via Shanghai AI Laboratory partnerships (not detailed)

Pros

  1. Frontier open-source performance: Matches or exceeds closed models in scientific reasoning and multimodal tasks
  2. Highly efficient MoE design: Massive scale with low active parameters for feasible inference
  3. Strong scientific specialization: Excels in real-world AI4Science applications like compound synthesis, protein analysis
  4. Tool calling and agentic: Enables practical workflows with external data and multi-step reasoning
  5. Flexible thinking control: Switch modes for speed vs quality trade-offs
  6. Time-series excellence: Unique capability for long physical signal processing
  7. Community and deployment ready: Hugging Face hosting, LMDeploy/vLLM support, active Discord/WeChat channels
  8. Completely free and open: No usage costs, full access for research and commercial under Apache-2.0

Cons

  1. Extremely resource-intensive: Requires significant GPU/TPU resources for inference (even with MoE efficiency)
  2. Deployment complexity: Time-series module still under optimization; needs advanced frameworks setup
  3. Limited public user stats: As a very recent/research-oriented model, fewer casual users compared to consumer LLMs
  4. Multimodal focus on science: General text/multimodal may not match consumer models in creative tasks
  5. No hosted inference: Currently no built-in provider; users must self-host or use community demos
  6. Recent release: Ecosystem integrations, fine-tunes, and community examples still emerging
  7. Potential VRAM demands: 22B active parameters require high-end hardware for full performance

Use Cases

  1. Scientific research assistance: Analyze chemical structures, protein sequences, synthesis routes, or materials properties
  2. AI4Science tasks: Solve domain-specific problems in chemistry, biology, earth sciences with high accuracy
  3. Multimodal analysis: Interpret scientific images, diagrams, charts combined with text queries
  4. Time-series modeling: Process long physical or experimental data sequences for prediction or analysis
  5. Tool-augmented reasoning: Fetch real-time data (e.g., weather, databases) during complex scientific queries
  6. Advanced reasoning benchmarks: Test and develop on GPQA, scientific QA, or multimodal evaluations
  7. Academic and open research: Fine-tune or extend for specialized scientific applications

Target Audience

  1. AI and scientific researchers: Working on multimodal foundation models or domain-specific AI
  2. Scientists in chemistry/materials/biology: Needing reasoning over structures, sequences, and data
  3. Academic institutions: Using open-source models for research without API costs
  4. AI developers/engineers: Deploying large MoE models with tool calling and efficiency
  5. Open-source enthusiasts: Experimenting with frontier trillion-scale open weights
  6. Enterprise R&D teams: In pharma, materials, energy sectors for scientific discovery

How To Use

  1. Download model: Clone from Hugging Face: git clone https://huggingface.co/internlm/Intern-S1-Pro
  2. Install dependencies: Use LMDeploy, vLLM, or SGLang for inference (follow deployment_guide.md)
  3. Load model: Example with transformers: from transformers import AutoModelForCausalLM, AutoTokenizer; model = AutoModelForCausalLM.from_pretrained('internlm/Intern-S1-Pro')
  4. Enable thinking mode: Default on; disable with enable_thinking=False in chat template or API kwargs
  5. Prompt examples: Use recommended sampling: top_p=0.95, top_k=50, temperature=0.8
  6. Tool calling: Format prompts for OpenAI-compatible API to invoke external tools
  7. Deploy locally: Run with vLLM or LMDeploy for efficient serving; check time-series support status

How we rated Intern-S1-Pro

  • Performance: 4.9/5
  • Accuracy: 4.8/5
  • Features: 4.7/5
  • Cost-Efficiency: 5.0/5
  • Ease of Use: 4.2/5
  • Customization: 4.6/5
  • Data Privacy: 5.0/5
  • Support: 4.4/5
  • Integration: 4.5/5
  • Overall Score: 4.7/5

Intern-S1-Pro integration with other tools

  1. Hugging Face Transformers: Direct loading and inference with standard library for research workflows
  2. LMDeploy / vLLM / SGLang: High-performance inference engines for fast serving and deployment
  3. OpenAI-Compatible API: Supports tool calling and chat templates for agent frameworks like LangChain
  4. InternLM Ecosystem: Integrates with InternViT vision encoder and other Intern series components
  5. Community Tools: Discord/WeChat for support; potential future extensions via GitHub contributions

Best prompts optimised for Intern-S1-Pro

  1. Analyze this chemical structure image and propose a synthesis route for the target compound, step-by-step with reasoning: [image upload]
  2. Interpret the protein sequence [insert sequence] and predict its secondary structure, function, and potential binding sites using scientific reasoning.
  3. Given this time-series data from [describe experiment], forecast the next 100 points and explain the underlying physical principles.
  4. Solve this GPQA-level quantum chemistry question: [paste question] with detailed chain-of-thought reasoning and tool use if needed.
  5. Explain the materials science implications of this phase diagram image and suggest experimental validations: [image upload]
Intern-S1-Pro is a groundbreaking open-source trillion-scale MoE model specializing in scientific multimodal reasoning, delivering SOTA results in AI4Science domains while remaining efficient and fully accessible under Apache-2.0. Ideal for researchers pushing boundaries in chemistry, materials, and beyond, though it demands serious hardware. A major win for open science.

FAQs

  • What is Intern-S1-Pro?

    Intern-S1-Pro is a 1-trillion-parameter Mixture-of-Experts multimodal scientific reasoning model from InternLM, with 22B active parameters, leading in AI4Science tasks and general multimodal capabilities.

  • When was Intern-S1-Pro released?

    The model and technical paper were released on August 21, 2025 (arXiv:2508.15763), with weights available on Hugging Face.

  • Is Intern-S1-Pro open-source?

    Yes, fully open-source under Apache-2.0 license with weights, code, and deployment guides on Hugging Face.

  • How many parameters does Intern-S1-Pro have?

    1 trillion total parameters with 512 experts, activating 8 experts per token (22B active parameters) via efficient MoE design.

  • What makes Intern-S1-Pro special?

    It achieves top-tier performance in scientific reasoning (chemistry, materials, life-science, earth) while supporting multimodal tasks, tool calling, thinking mode, and long time-series modeling.

  • How do I run Intern-S1-Pro?

    Download from Hugging Face and deploy with LMDeploy, vLLM, or SGLang; follow the deployment guide for inference and tool calling setup.

  • Does Intern-S1-Pro support tool calling?

    Yes, it supports tool calling via OpenAI-compatible API format for fetching real-time data or external functions during reasoning.

  • Is Intern-S1-Pro suitable for general use?

    While excellent in science, it maintains strong general multimodal and text capabilities, making it versatile beyond pure research tasks.

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