Olmo 3.1 32B Instruct

Olmo 3.1 32B Instruct is the instruction-tuned member of the Olmo 3 family from Ai2 (the Allen Institute for AI). Olmo is a series of Open Language Models built to advance the science of language modeling. The Olmo 3 release is fully open: the training code, model checkpoints, datasets, and associated training details are all published, making this a strong foundation for reproducible research and transparent deployment.

This 32B dense model is derived from the Olmo-3-32B base, which is pre-trained on the Dolma 3 dataset and then post-trained on the Dolci data suite. It is designed for direct instruction following and conversational use, delivering high-quality responses across math, coding, reasoning, and general chat without requiring an explicit chain-of-thought thinking mode.

Key Features

• Fully open model - training code, checkpoints, datasets, and training details are all released by Ai2.
• Dense 32B architecture - a Transformer-style autoregressive language model in a single-GPU-class size for its precision.
• Instruction tuned - optimized for dialogue, instruction following, and assistant-style interaction.
• Strong reasoning and coding - competitive results on math, code, and knowledge benchmarks.
• English language - trained and evaluated primarily on English text.

Post-training Pipeline

Olmo 3.1 32B Instruct is produced through a multi-stage post-training recipe on top of the Olmo-3-32B base:

• Supervised fine-tuning (SFT) on the Dolci instruction datasets covering math, code, chat, and general knowledge.
• Direct preference optimization (DPO) to align responses with human preferences.
• Reinforcement learning from verifiable rewards (RLVR) to sharpen math, code, instruction-following, and chat behavior.

Benchmark Highlights

On the official Ai2 evaluations, Olmo 3.1 32B Instruct shows strong results across categories. In math it reaches 93.4 on MATH, 67.8 on AIME 2024, and 57.9 on AIME 2025. On reasoning it scores 84.0 on BigBenchHard and 61.7 on ZebraLogic. For coding it reaches 86.7 on HumanEvalPlus, 65.1 on MBPP+, and 54.7 on LiveCodeBench v3. On instruction following it scores 88.8 on IFEval and 39.7 on IFBench, and on knowledge and QA it reaches 80.9 on MMLU and 48.6 on GPQA. It also posts 59.8 on AlpacaEval 2 LC for chat quality and 89.5 on the safety evaluation.

Use Cases

• Interactive chatbots and virtual assistants
• Math, coding, and reasoning assistance
• Content creation and writing support
• Research on open, reproducible language models
• Educational and knowledge-based applications

Deploy Olmo 3.1 32B Instruct on Vast.ai to run a fully open, high-performance instruct model with vLLM or quantized llama.cpp variants.