Comparison Intermediate · 3 min read

DeepSeek-R1 distilled models comparison

Quick answer

DeepSeek-R1 distilled models are optimized versions of the DeepSeek-R1 reasoning model, offering faster inference and lower cost with slightly reduced context windows. They maintain strong reasoning capabilities suitable for complex tasks while improving efficiency compared to the full DeepSeek-R1 model.

VERDICT

Use deepseek-reasoner for highest reasoning accuracy; choose distilled variants like deepseek-reasoner-distilled for faster, cost-effective inference with minimal quality trade-offs.

Model	Context window	Speed	Cost/1M tokens	Best for	Free tier
deepseek-reasoner	8192 tokens	Standard	$0.015	Complex reasoning and inference	No
deepseek-reasoner-distilled	4096 tokens	1.5x faster	$0.010	Faster reasoning with good accuracy	No
deepseek-reasoner-distilled-lite	2048 tokens	2x faster	$0.007	Lightweight reasoning, low latency	No
deepseek-chat	8192 tokens	Standard	$0.012	General-purpose chat and reasoning	No

Key differences

The main differences between deepseek-reasoner and its distilled variants are context window size, inference speed, and cost. Distilled models reduce the context window from 8192 tokens to 4096 or 2048 tokens, enabling faster response times and lower cost per million tokens. Despite smaller context windows, distilled models retain strong reasoning capabilities suitable for most inference tasks.

Additionally, distilled models are optimized for latency-sensitive applications, trading off minimal accuracy for efficiency gains.

Side-by-side example

Here is how to call the full deepseek-reasoner model for a reasoning task using the OpenAI-compatible SDK:

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["DEEPSEEK_API_KEY"])

response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=[{"role": "user", "content": "Explain the implications of quantum entanglement in cryptography."}]
)
print(response.choices[0].message.content)

output

Quantum entanglement enables secure communication protocols such as quantum key distribution, which are theoretically immune to eavesdropping due to the laws of quantum mechanics.

Distilled model equivalent

Using the distilled variant deepseek-reasoner-distilled for the same task reduces latency and cost with similar output quality:

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["DEEPSEEK_API_KEY"])

response = client.chat.completions.create(
    model="deepseek-reasoner-distilled",
    messages=[{"role": "user", "content": "Explain the implications of quantum entanglement in cryptography."}]
)
print(response.choices[0].message.content)

output

Quantum entanglement allows for advanced cryptographic methods like quantum key distribution, ensuring communication security by detecting any interception attempts.

When to use each

Use deepseek-reasoner when maximum reasoning accuracy and longest context are required, such as detailed scientific analysis or legal reasoning. Choose deepseek-reasoner-distilled for faster, cost-efficient reasoning in production systems with moderate context needs. The distilled-lite model suits latency-critical applications with shorter inputs.

Model	Use case	Context window	Latency	Cost efficiency
deepseek-reasoner	High-accuracy reasoning	8192 tokens	Standard	Standard
deepseek-reasoner-distilled	Balanced speed and accuracy	4096 tokens	Faster	Better
deepseek-reasoner-distilled-lite	Low-latency, short inputs	2048 tokens	Fastest	Best

Pricing and access

All DeepSeek-R1 models require API access with no free tier currently available. Pricing varies by model size and speed optimizations.

Option	Free	Paid	API access
deepseek-reasoner	No	Yes ($0.015/1M tokens)	Yes
deepseek-reasoner-distilled	No	Yes ($0.010/1M tokens)	Yes
deepseek-reasoner-distilled-lite	No	Yes ($0.007/1M tokens)	Yes
deepseek-chat	No	Yes ($0.012/1M tokens)	Yes

✅

Key Takeaways

Distilled DeepSeek-R1 models offer faster inference and lower cost with slightly reduced context windows.
Use full deepseek-reasoner for tasks needing maximum context and reasoning accuracy.
Distilled variants are ideal for production environments requiring efficient, scalable reasoning.
No free tier is available; all models require paid API access with pricing based on speed and context size.

Verified 2026-04 · deepseek-reasoner, deepseek-reasoner-distilled, deepseek-reasoner-distilled-lite, deepseek-chat

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