R1 Distill Qwen 32B
VerifiedDistilled 32B reasoning model with extended context for efficient inference.
About R1 Distill Qwen 32B
The model uses knowledge distillation techniques to transfer capabilities from DeepSeek's larger R1 system into the Qwen 32B base. This approach preserves core reasoning behaviors while reducing computational requirements for deployment. The resulting weights remain fully open for research and commercial use.
Its 128k token context enables handling of extended inputs such as lengthy codebases, technical documentation, or multi-turn conversations without truncation. The text-only modality focuses resources on language understanding and generation tasks. Users typically run the model locally or via APIs for applications requiring strong logical inference.
Common usage includes coding assistance, mathematical problem solving, and analysis of long-form content. The open-weight release allows fine-tuning on domain-specific data while maintaining the distilled reasoning strengths.
Capabilities
Benchmarks & performance
Independent evaluation scores and measured speed.
Source: Artificial Analysis
How R1 Distill Qwen 32B compares
R1 Distill Qwen 32B (striped bar) vs other language models on intelligence, speed and price.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · R1 Distill Qwen 32B ranks #47 of 67
Price
USD per 1M output tokens · Lower is better · R1 Distill Qwen 32B ranks #30 of 141
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Long document analysis
The model processes and reasons over texts up to 128000 tokens, enabling synthesis of insights from extensive reports or research collections.
Complex software development
It generates, debugs, and iterates on code through multi-step problem solving for large-scale programming projects.
Advanced quantitative tasks
Strong mathematical reasoning supports step-by-step solutions to intricate problems in science and engineering.
Strengths & limitations
Strengths
- +Strong chain-of-thought reasoning from R1 distillation
- +Efficient performance for 32B scale
- +Handles extended contexts effectively
- +Competent across STEM tasks
Limitations
- –Text-only modality
- –Distilled model may trail full-scale R1 on hardest problems
- –Standard LLM risks of hallucination on niche topics
Cost calculator
Estimate what R1 Distill Qwen 32B would cost for your usage.
Based on R1 Distill Qwen 32B's $0.29/1M input · $0.29/1M output. Estimate only — actual cost varies by provider and caching.
Quick start
OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. Only the model slug changes between models.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
});
const completion = await client.chat.completions.create({
model: "deepseek/deepseek-r1-distill-qwen-32b",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: deepseek/deepseek-r1-distill-qwen-32b
Editor's verdict
R1 Distill Qwen 32B is DeepSeek's open-weight language models with a 128K-token context window.
On independent testing it scores 17.2 on the Artificial Analysis Intelligence Index.
At $0.29 per 1M output tokens, it is very cost-efficient for its class.
As an open-weight model you can self-host it or call it through a hosted API.
Best suited to strong chain-of-thought reasoning from r1 distillation and efficient performance for 32b scale.
Frequently asked questions
It supports a context window of 128000 tokens.
User reviews
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