Is Olmo 3.1 truly free?

Yes, Olmo 3.1 is released under the Apache 2.0 license, meaning the weights, code, and data are free for both research and commercial use.

How good is Olmo 3.1 at math?

The "Think" variant is extremely strong, scoring 78.1% on the AIME 2025 benchmark, which places it ahead of most open-source models and competitive with proprietary frontier models.

Can I run Olmo 3.1 on my laptop?

The 7B version runs easily on most modern laptops. The 32B version requires a machine with significant RAM and GPU power (e.g., dual RTX 3090s or a Mac Studio with 64GB+ unified memory) to run effectively.

What is the difference between "Instruct" and "Think"?

"Instruct" is optimized for standard chat and following commands quickly. "Think" is designed to pause and generate a hidden "chain of thought" to reason through hard problems before giving a final answer, making it slower but smarter.

Olmo 3.1

The first truly “glass-box” reasoning model where everything is open.

From Allen Institute for AI (Ai2)

Founder: Allen Institute for AI Team (Led by Kyle Lo, Hannaneh Hajishirzi, et al.)

Text Generator

Dec 2025

35K+

Free

Pricing Model

Free

Starting Price

$0/Month

About This AI

Olmo 3.1 is a family of state-of-the-art open-source language models that redefines transparency in AI.

Unlike “open weight” models (like Llama) that hide their training data, Olmo 3.1 releases the entire “model flow” including the full 9.3 trillion token “Dolma 3” dataset, training code, training logs, and intermediate checkpoints.

The flagship “32B Think” variant is optimized for deep reasoning using Chain-of-Thought (CoT) methodology, allowing users to see not just the answer, but the step-by-step logic and the exact data sources the model learned from.

Chatbot Coding Conversation LLM

Pricing

Pricing Model

Free

Starting Price

$0/Month

Key Features

Glass-Box Transparency: Releases every artifact: code, weights, training logs, and the full training dataset.
Thinking Variant: The "Think" model uses explicit Chain-of-Thought processing to break down complex math and logic problems before answering.
OlmoTrace: A provenance tool that links the model's outputs back to the specific training documents in the Dolma 3 dataset.
Efficiency: The 32B model delivers performance rivaling much larger closed models while being efficient enough to run on single server-grade GPUs (or high-end consumer setups).
Verifiable RL: Uses Reinforcement Learning with Verifiable Rewards (RLVR) to ground its reasoning in ground-truth correctness (e.g., math proofs).
Mid-Training Checkpoints: Researchers can access hundreds of snapshots from the training process to study how the model learned over time.

Pros

The most transparent model in history; ideal for scientific research and auditing.
"Think" variant scores exceptionally high on math (78.1% on AIME 2025).
Completely free to use, modify, and commercialize (Apache 2.0 license).
"OlmoTrace" solves the "black box" problem of not knowing where an AI got its facts.
Strong performance for its size (32B), beating larger open models like Llama 3.1 70B in math.

Cons

Not as polished for creative writing or roleplay as models like Claude or GPT-4.
The 32B model requires significant VRAM (~64GB for FP16, less for quantized) to run locally.
"Think" models can be slower due to the extra tokens generated for reasoning.

Best for AI researchers, data scientists, and developers who need a fully audit-able, high performance reasoning model that can be legally scrutinized and deeply customized.

FAQs

Is Olmo 3.1 truly free?
Yes, Olmo 3.1 is released under the Apache 2.0 license, meaning the weights, code, and data are free for both research and commercial use.
How good is Olmo 3.1 at math?
The “Think” variant is extremely strong, scoring 78.1% on the AIME 2025 benchmark, which places it ahead of most open-source models and competitive with proprietary frontier models.
Can I run Olmo 3.1 on my laptop?
The 7B version runs easily on most modern laptops. The 32B version requires a machine with significant RAM and GPU power (e.g., dual RTX 3090s or a Mac Studio with 64GB+ unified memory) to run effectively.
What is the difference between “Instruct” and “Think”?
“Instruct” is optimized for standard chat and following commands quickly. “Think” is designed to pause and generate a hidden “chain of thought” to reason through hard problems before giving a final answer, making it slower but smarter.