Allen AI: Olmo 3.1 32B Think

This model is no longer available.

Add AI to your application with Puter.js.

Explore Other Models

Model Card

OLMo 3.1 32B Think is the updated flagship reasoning model from the Allen Institute for AI (AI2), an improved successor to OLMo 3 32B Think with an additional 21 days of extended reinforcement learning training.

The extended training yielded gains of 5+ points on AIME, 4+ points on ZebraLogic and IFEval, and 20+ points on IFBench over its predecessor. It supports a 64K context window and is licensed under Apache 2.0 with full training transparency.

For API developers needing a high-performance open reasoning model for math, code, and complex instruction-following, OLMo 3.1 32B Think is AI2's most capable reasoning offering, competitive with Qwen 3 32B at the same scale.

Context Window 66K

tokens

Max Output 66K

tokens

Input Cost $0.15

per million tokens

Output Cost $0.5

per million tokens

Release Date Dec 10, 2025

 

Code Example

Add AI to your app with the Puter.js AI API — no API keys or setup required.

// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';

puter.ai.chat("Explain quantum computing in simple terms").then(response => {
    document.body.innerHTML = response.message.content;
});

<html>
<body>
    <script src="https://js.puter.com/v2/"></script>
    <script>
        puter.ai.chat("Explain quantum computing in simple terms").then(response => {
            document.body.innerHTML = response.message.content;
        });
    </script>
</body>
</html>

More AI Models From Allen AI

Find other Allen AI models →

Chat

Olmo 3 32B Think

OLMo 3 32B Think is a fully open reasoning model from the Allen Institute for AI (AI2), and the first fully open 32B thinking model to expose intermediate chain-of-thought reasoning traces. Trained on multi-step math, code, and general problem-solving tasks using a thinking SFT, DPO, and RLVR training flow, it is the strongest fully open reasoning model at the 32B scale — narrowing the gap to open-weight models like Qwen 3-32B-Think while trained on 6x fewer tokens. All training data, code, weights, and checkpoints are publicly available under Apache 2.0. Best suited for complex multi-step reasoning and mathematical problem solving via API.

Frequently Asked Questions

How do I use Olmo 3.1 32B Think?

You can access Olmo 3.1 32B Think by Allen AI through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.

Is Olmo 3.1 32B Think free?

Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Olmo 3.1 32B Think to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.

What is the pricing for Olmo 3.1 32B Think?
Olmo 3.1 32B Think costs $0.15 per 1M input tokens and $0.5 per 1M output tokens.
Price per 1M tokens
Input$0.15
Output$0.5
Who created Olmo 3.1 32B Think?

Olmo 3.1 32B Think was created by Allen AI and released on Dec 10, 2025.

What is the context window of Olmo 3.1 32B Think?

Olmo 3.1 32B Think supports a context window of 66K tokens. For reference, that is roughly equivalent to 131 pages of text.

What is the max output length of Olmo 3.1 32B Think?

Olmo 3.1 32B Think can generate up to 66K tokens in a single response.

How does Olmo 3.1 32B Think perform on benchmarks?

Olmo 3.1 32B Think scores 13.9 on the Artificial Analysis Intelligence Index, outperforming 29% of tracked models. On coding, it scores 9.8 (outperforms 21% of models). On math, it scores 77.3 (outperforms 72% of models).

Does it work with React / Vue / Vanilla JS / Node / etc.?

Yes — the Olmo 3.1 32B Think API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.

Get started with Puter.js

Add AI to your application without worrying about API keys or setup.

Explore Models View Tutorials