Ant releases the first open-source trillion-parameter inference model, Ring-1T

🤗 Hugging Face   |   🤖 ModelScope

Ring-1T-preview, Deep Thinking, No Waiting

Recently, we have been fully occupied with the post-training of Ling 2.0's 1T foundational language model, striving to maximize the natural language reasoning potential of this trillion-scale base model. Conducting post-training on such a huge model, particularly the "training" involved in large-scale reinforcement learning, stands as one of the most technically challenging tasks the Ling Team has encountered since its establishment. On the other hand, it has also been a process that continuously reshapes our technical understanding and reinforces the belief that "scaling is all you need"

In the early stages of large-scale reinforcement learning training, Ring-1T, the thinking version of the 1T foundational language model, has already demonstrated powerful natural language reasoning capabilities. In AIME 2025 (American Invitational Mathematics Examination), the model achieved a high score of 92.6 through pure natural language reasoning, further approaching GPT-5 with thinking (no tools)'s score of 94.6. Additionally, the model has shown strong competitiveness in the Harvard-MIT Mathematics Tournament (HMMT) 2025, competition-level code generation tasks such as LiveCodeBench v6 and CodeForces, as well as the abstraction and reasoning benchmark ARC-AGI-1 task.

To further explore the reasoning limits of the early version of Ring-1T, we integrated it into the multi-agent framework AWorld (https://github.com/inclusionAI/AWorld ) and conducted pure natural language reasoning tests on the IMO 2025 (International Mathematical Olympiad, 6 problems in total).

Previously, we tested Ring-flash-2.0, using the same method: under the setting of three allowed reasoning attempts, Ring-flash-2.0 only managed to solve Problem 3 on the third try. In contrast, during this test, Ring-1T solved Problem 3 in just one attempt, and also produced partially correct answers on Problems 1, 2, 4, and 5 in a single try. This demonstrates advanced reasoning capabilities essential for top-tier math competitions—such as insight, constructive problem solving, counterexample generation, strategic thinking, and rigorous logical-chain reasoning—highlighting the stronger reasoning potential of large-scale thinking models.

IMO Cases

To facilitate early community exploration of the reasoning capabilities of the trillion-parameter thinking model Ring-1T, we have decided to open-source its preview version, Ring-1T-preview, ahead of schedule. This model retains the efficient MoE architecture of Ling 2.0, completed pre-training on 20T tokens of corpora, and underwent RLVR training tailored for reasoning abilities within our self-developed efficient reinforcement learning system ASystem (the AReaL framework of which has been open-sourced), leveraging the previously disclosed "icepop" method(https://ringtech.notion.site/icepop).

Ring-1T remains under continuous training. While the preview version already demonstrates powerful natural language reasoning capabilities, it still exhibits issues such as language mixing, repetitive reasoning and identity misperception. We look forward to community exploration and feedback to collectively accelerate the iterative refinement of this trillion-parameter foundation.

Quickstart

🤗 Hugging Face Transformers

Here is a code snippet to show you how to use the chat model with transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "inclusionAI/Ring-1T-preview" model = AutoModelForCausalLM.from_pretrained( model_name, dtype="auto", device_map="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt", return_token_type_ids=False).to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=8192 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

🤖 ModelScope

If you're in mainland China, we strongly recommend you to use our model from 🤖 ModelScope.

License

This code repository is licensed under the MIT License.

Tip

To facilitate academic research and downstream applications with customizable model naming, we did not conduct specific identity recognition training.