library_name: transformers
tags:
- prime-rl
- verifiers
- prime-intellect
- reinforcement-learning
- reasoning
- agentic
- mixture-of-experts
license: mit
language:
- en
base_model:
- zai-org/GLM-4.5-Air-Base
pipeline_tag: text-generation
INTELLECT-3
🚀 State-of-the-art 100B+ parameter Mixture-of-Experts model trained with large-scale reinforcement learning
📚 Trained with prime-rl infra and verifiers environments | 🌐 Environments on Environments Hub
📖 Read the Technical Report | 💬 Join our Discord
Introduction
INTELLECT-3 is a 106B (A12B) parameter Mixture-of-Experts reasoning model post-trained from GLM-4.5-Air-Base using supervised fine-tuning (SFT) followed by large-scale reinforcement learning (RL).
Training was performed with prime-rl using environments built with the verifiers library. All training and evaluation environments are available on the Environments Hub.
The model, training frameworks, and environments are open-sourced under fully-permissive licenses (MIT and Apache 2.0).
For more details, see the technical report.
Evaluation
INTELLECT-3 achieves best-in-class performance on math, coding, and reasoning benchmarks:
| Benchmark | Score |
|---|---|
| AIME 2025 | 88.0 |
| LiveCodeBench v6 | 69.3 |
| GPQA Diamond | 74.4 |
| HLE | 14.6 |
Model Variants
| Model | HuggingFace |
|---|---|
| INTELLECT-3 | PrimeIntellect/INTELLECT-3 |
| INTELLECT-3-FP8 | PrimeIntellect/INTELLECT-3-FP8 |
Serving with vLLM
The BF16 version can be served on 2x H200s:
vllm serve PrimeIntellect/INTELLECT-3 \
--tensor-parallel-size 2 \
--tool-call-parser qwen3_coder \
--reasoning-parser deepseek_r1
The FP8 version can be served on a single H200:
vllm serve PrimeIntellect/INTELLECT-3-FP8 \
--tool-call-parser qwen3_coder \
--reasoning-parser deepseek_r1
Citation
@misc{intellect3,
title={INTELLECT-3: Technical Report},
author={Prime Intellect Team},
year={2025},
url={https://huggingface.co/PrimeIntellect/INTELLECT-3}
}