P1-235B-A22B is the flagship model of the P1 series, a state-of-the-art open-source large language model specialized in physics reasoning. Built on Qwen3-235B-A22B-Thinking-2507 and tuned through multi-stage reinforcement learning on curated physics competition data, P1-235B-A22B marks a historic achievement as the first open-source model to win gold at the International Physics Olympiad (IPhO 2025).

Key Highlights

🏆 IPhO 2025 Gold Medal: First open-source model to achieve gold medal status (21.2/30 points)
🥇 HiPhO Benchmark Leader: 12 gold medals and 1 silver medal across 13 top international physics contests
🥇 Overall Champion: When paired with PhysicsMinions multi-agent system, achieves #1 ranking with 38.4 points, surpassing Gemini-2.5-Pro (37.7) and GPT-5 (37.4)

Performance Benchmarks

IPhO 2025 Results

Model	Score	Medal	Rank
P1-235B-A22B + PhysicsMinions	23.2	🥇 Gold	1st
Gemini-2.5-Pro	22.2	🥇 Gold	2nd
GPT-5	22.3	🥇 Gold	3rdh
P1-235B-A22B	21.2	🥇 Gold	4th

HiPhO Comprehensive Results

Category	P1-235B-A22B	P1-235B-A22B + PhysicsMinions	Gemini-2.5-Pro	GPT-5
Overall Score	35.9	38.4 🏆	37.7	37.4
Gold Medals (🥇)	12	12	12	11
Silver Medals (🥈)	1	1	1	2
Total Contests	13	13	13	13

Generalization to STEM Tasks

P1-235B-A22B demonstrates excellent general capabilities across various benchmarks. As shown below, P1-235B-A22B achieves better performance than its base model Qwen3-235B-A22B-Thinking-2507 on multiple tasks, further validating the strong generalization of P1 series models.

Model	AIME24	AIME25	HMMT	GPQA	HLE	LiveCodeBench	LiveBench
Qwen3-235B-A22B-Thinking-2507 (Base)	94.6	94.2	81.7	79.4	17.5	76.2	80.3
P1-235B-A22B	95.0	95.0	80.8	81.4	19.1	75.8	79.8

Usage

Basic Inference

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "P1-235B-A22B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Physics problem solving
prompt = """Solve this physics problem:

A block of mass m = 2.0 kg slides down a rough incline at angle θ = 30° 
with coefficient of friction μ = 0.2. Calculate the acceleration of the block.

Provide a detailed solution with reasoning steps."""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_length=81920,
    temperature=0.6,
    top_p=0.9,
    do_sample=True
)

solution = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(solution)

🙏 Acknowledgements

We are grateful to the open-source community for their invaluable contributions. Special thanks to:

Qwen3 - for providing the foundational base models that powered our research
slime - for their innovative work on efficient reinforcement learning framework that powered our training pipeline
verl - for the versatile reinforcement learning framework that enabled our training pipeline
sglang - for the efficient LLM serving and inference infrastructure
Megatron-LM - for the large-scale model training framework

Citation

bibtex

@misc{p1-2025,
    title={P1: Mastering Physics Olympiads with Reinforcement Learning},
    author={P1 Team},
    year={2025},
    url={https://prime-rl.github.io/P1/}
}

PRIME-RL/P1-235B-A22B

P1: Mastering Physics Olympiads with Reinforcement Learning

Model Description