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DeepSeek R1 Distill Qwen 14B vs DeepSeek-R1

Put two to four models side by side on the fields that decide a deployment: context, price, region, license, languages, hosting, and what is verified.

DeepSeek R1 Distill Qwen 14BDeepSeek-R1
Provider DeepSeekDeepSeek
Parameters 14.8B684.49B
Context window Not verified: the provider has not documented this claim.131k
License mitmit
Modalities texttext
Release date 20 Jan 202520 Jan 2025
Languages Not verified: the provider has not documented this claim.Strong: en, zh
Adequate: de, fr, es, it, pt, ru, ja, ko
Open weights yesyes
Tasks ReasoningReasoning, Coding, Summarisation

Where two models differ, this table shows the difference and stops. LLM EU publishes no winner badge without a documented score for that task, and a missing figure stays missing instead of becoming a zero.

When to use it

DeepSeek R1 Distill Qwen 14B

Strengths

  • open weights are published on Hugging Face and can be downloaded without a vendor agreement
  • mit licence, so commercial self-hosting is permitted without a separate agreement

Limits

  • catalogue only — LLM EU does not run this model, and nothing on this card implies an LLM EU endpoint
  • no evaluation has been run by LLM EU
  • the published configuration records no context window, so the card carries none
  • at 14.8B parameters, self-hosting needs a single accelerator with enough memory for the weights

Tasks

Reasoning

DeepSeek-R1

Strengths

  • MIT licence for a 671B-parameter reasoning model
  • published reasoning traces in the base training recipe
  • strong maths scores for a January 2025 release

Limits

  • 671B parameters, so self-hosting is expensive
  • reasoning output is verbose and raises token cost
  • superseded by DeepSeek-V3.2 and V4-Flash for most workloads

Tasks

Reasoning, Coding, Summarisation

Tasks: Reasoning

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