OLMo 3.1 32B Think
Parameters
32B
Context Length
65.536K
Modality
Text
Architecture
Dense
License
Apache 2.0
Release Date
12 Dec 2025
Knowledge Cutoff
Dec 2024
Technical Specifications
Attention Structure
Multi-Head Attention
Hidden Dimension Size
-
Number of Layers
64
Attention Heads
-
Key-Value Heads
-
Activation Function
-
Normalization
-
Position Embedding
Absolute Position Embedding
System Requirements
VRAM requirements for different quantization methods and context sizes
OLMo 3.1 32B Think
OLMo 3.1 32B Think, developed by the Allen Institute for AI, is a large-scale language model specifically engineered for advanced reasoning and multi-step problem-solving. This variant is a core component of the broader OLMo 3 family, distinguished by its focus on transparent, interpretable intelligence. The model is built upon a decoder-only Transformer architecture, a widely adopted framework in contemporary large language models, and is designed to facilitate detailed logical progression in its outputs.
The training methodology for OLMo 3.1 32B Think involves a multi-stage process that extends beyond foundational pretraining. Following initial pretraining on the extensive Dolma 3 dataset, the model undergoes supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning from verifiable rewards (RLVR) using specialized Dolci-Think datasets. This post-training regimen is meticulously crafted to cultivate chain-of-thought reasoning capabilities, enabling the model to articulate its problem-solving steps explicitly. The architecture incorporates grouped-query attention to optimize computational efficiency, particularly for inference within single-GPU environments.
As a reasoning-oriented specialist, OLMo 3.1 32B Think demonstrates proficiency across a spectrum of demanding intellectual tasks, including advanced mathematics, complex coding challenges, and intricate logical inference. Its substantial context window supports analysis of extensive documents and multi-step analytical processes. The model's complete openness, encompassing not only its weights but also its training code, data, and detailed methodology, positions it as a robust platform for machine learning research and for applications requiring a verifiable and auditable artificial intelligence system.
About OLMo 3
OLMo (Open Language Model) is a series of fully open language models designed to enable the science of language models. Released by the Allen Institute for AI (Ai2), OLMo 3 provides complete access to training data (Dolma 3), code, checkpoints, logs, and evaluation methodologies. The family includes Base models for pretraining research, Instruct variants for chat and tool use, and Think variants with chain-of-thought reasoning capabilities. All models are trained with staged approach including pretraining, mid-training, and long-context phases.
Other OLMo 3 Models
Evaluation Benchmarks
Ranking is for Local LLMs.
No evaluation benchmarks for OLMo 3.1 32B Think available.
Rankings
Overall Rank
-
Coding Rank
-
GPU Requirements
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Context Size: 1,024 tokens