DeepSeek: R1 Distill Qwen 32B
deepseek/deepseek-r1-distill-qwen-32b
Access R1 Distill Qwen 32B from DeepSeek using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "deepseek/deepseek-r1-distill-qwen-32b"
}).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", {
model: "deepseek/deepseek-r1-distill-qwen-32b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="deepseek/deepseek-r1-distill-qwen-32b",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "deepseek/deepseek-r1-distill-qwen-32b",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
DeepSeek R1 Distill Qwen 32B is a 32 billion parameter dense model fine-tuned from Qwen 2.5 using R1-generated reasoning data, achieving state-of-the-art results for dense models. It outperforms OpenAI o1-mini on various benchmarks while being efficient enough for local deployment.
Context Window 128K
tokens
Max Output 33K
tokens
Input Cost $0.29
per million tokens
Output Cost $0.29
per million tokens
Release Date Jan 29, 2025
Model Playground
Try R1 Distill Qwen 32B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How R1 Distill Qwen 32B performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 61.5% |
| Humanity's Last Exam Cross-domain reasoning | 5.5% |
| LiveCodeBench Recent coding problems | 27.0% |
| SciCode Scientific programming | 37.6% |
| MATH-500 Competition math | 94.1% |
| AIME 2024 Advanced math exam | 68.7% |
| AIME 2025 Advanced math exam | 63.0% |
| IFBench Instruction following | 22.9% |
| LCR Long-context reasoning | 9.7% |
Scores sourced from Artificial Analysis.
Find other DeepSeek models →
DeepSeek V4 Flash
DeepSeek V4 Flash is a lightweight, efficiency-focused Mixture-of-Experts model from DeepSeek, with 284B total parameters and 13B activated per token. It supports a 1M-token context window and configurable reasoning modes (standard, high, and max thinking effort). Designed as the fast and economical option in the V4 family, Flash delivers reasoning capabilities that closely approach the larger V4 Pro, and performs on par with it on simpler agentic tasks. In its max reasoning mode, it achieves comparable reasoning scores to Pro when given a larger thinking budget. At $0.14/M input and $0.28/M output tokens, it's one of the cheapest frontier-tier models available — well suited for high-throughput workloads like coding assistants, chat systems, and agent pipelines where latency and cost matter most.
ChatDeepSeek V4 Pro
DeepSeek V4 Pro is a 1.6T-parameter Mixture-of-Experts model from DeepSeek with 49B parameters activated per token, supporting a 1M-token context window. It is positioned as the strongest open-weight model currently available. V4 Pro leads all open-source models in math, coding, and STEM reasoning. On LiveCodeBench it scores 93.5, ahead of Gemini 3.1 Pro (91.7) and Claude Opus 4.6 (88.8). Its Codeforces rating of 3206 also tops GPT-5.4 (3168). On agentic tool-use benchmarks like MCPAtlas, it reaches near-parity with Opus 4.6. DeepSeek acknowledges it trails GPT-5.4 and Gemini 3.1 Pro overall by roughly 3–6 months of frontier development. Priced at $1.74/M input and $3.48/M output — a fraction of comparable closed-source models — it's a strong pick for complex reasoning, agentic coding, and knowledge-intensive tasks.
ChatDeepSeek V3.2
DeepSeek V3.2 is the December 2025 flagship model featuring DeepSeek Sparse Attention for efficiency and massive reinforcement learning post-training, achieving GPT-5-level performance. It's the first DeepSeek model to integrate thinking directly into tool-use and excels at agentic AI tasks.
Frequently Asked Questions
You can access R1 Distill Qwen 32B by DeepSeek 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.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add R1 Distill Qwen 32B to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.29 |
| Output | $0.29 |
R1 Distill Qwen 32B was created by DeepSeek and released on Jan 29, 2025.
R1 Distill Qwen 32B supports a context window of 128K tokens. For reference, that is roughly equivalent to 256 pages of text.
R1 Distill Qwen 32B can generate up to 33K tokens in a single response.
R1 Distill Qwen 32B scores 17.2 on the Artificial Analysis Intelligence Index, outperforming 42% of tracked models. On math, it scores 63.0 (outperforms 58% of models).
Yes — the R1 Distill Qwen 32B 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 R1 Distill Qwen 32B to your app without worrying about API keys or setup.
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