R1 Distill Qwen 14B

DeepSeek chatreasoningcode

API ID: deepseek/deepseek-r1-distill-qwen-14b

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Input Price
$0.15
/1M tokens
Output Price
$0.15
/1M tokens

About R1 Distill Qwen 14B

DeepSeek R1 Distill Qwen 14B brings reasoning capability to an efficient 14 billion parameter model. Through distillation from DeepSeek R1, it captures advanced problem-solving abilities while enabling deployment on consumer hardware. The model demonstrates strong performance on mathematics and logical reasoning relative to its size, showing transparent thinking processes. It features a practical context window and runs efficiently on high-end consumer GPUs. The open weights support fine-tuning and customization for specific reasoning domains. For developers building reasoning-enhanced applications with limited resources, this distilled model provides accessible entry to advanced AI reasoning. It's particularly valuable for local deployment, educational tools, and cost-sensitive applications requiring genuine problem-solving capability.

💰
Price Ranking
#628 lowest price among 950 Chat models

Model Specifications

Context Length
131k
Max Output
—
Release Date
2025-01-29
Capabilities
chat reasoning code
Input Modalities
text
Output Modalities
text

DeepSeek R1 Distill Qwen 14B: hosted API vs local GGUF

The model name appears in both API pricing searches and local-download searches, but those workflows have different costs and requirements.

OptionWhat you pay forBest fitMain limitation
Hosted API$0.15 input and $0.15 output per 1M tokensFast integration, elastic traffic, no GPU operationsOngoing token charges and provider-specific limits
Local GGUFHardware, electricity, storage, and engineering timeOffline testing, privacy-sensitive experiments, steady utilizationQuantization quality, memory capacity, and slower setup

Context size and settings

This listing records a 131k context window. Actual usable context can vary by host, quantization, runtime, memory, and prompt template. Treat community KoboldCpp or SillyTavern settings as runtime guidance rather than API pricing data.

How to compare total cost

Use the hosted token calculator for irregular workloads. For local deployment, compare GPU memory, expected tokens per second, utilization, power cost, and maintenance. A zero token price does not mean zero operating cost.

Common questions

Is GGUF included in this API price? No. GGUF is a local model file format; the prices above describe hosted inference rows available on this site.

Should I use 14B locally or through an API? Choose local inference when you can support the hardware and need control. Choose a hosted API when setup speed, scaling, and operational simplicity matter more.

Best For

  • Complex reasoning, math problems, multi-step logic
  • Code generation, debugging, code review, refactoring
  • Conversations, content writing, general assistance

Consider Alternatives For

  • Image understanding (needs vision capability)
  • Simple Q&A (cheaper models available)

💰 Real-World Cost Examples

Estimated monthly costs for common use cases

Personal AI Assistant
$0.11
/month
50 conversations/day, ~500 tokens each
Code Copilot
$1.08
/month
200 completions/day, ~1k tokens each
Customer Service Bot
$3.60
/month
1000 tickets/day, ~800 tokens each
Data Analysis Pipeline
$5.17
/month
500 analyses/day, ~2k tokens each
Calculate Your Custom Usage

DeepSeek Model Lineup

Compare all models from DeepSeek to find the best fit

Model Input Output Context Capabilities
R1 Distill Qwen 14B Current Free Free 131k chat reasoning code
DeepSeek V3.1 Base Free Free 164k chat
DeepSeek V3.1 Base Free Free 164k chat
DeepSeek Prover V2 Free Free 164k chat
DeepSeek V3 Base Free Free 131k chat
DeepSeek V3 Base Free Free 131k chat
View All DeepSeek Models →

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🚀 Quick Start

Get started with R1 Distill Qwen 14B API

OpenAI-compatible SDK 
from openai import OpenAI

client = OpenAI(
    base_url="https://api.provider.com/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="deepseek/deepseek-r1-distill-qwen-14b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)

Resources

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