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DeepSeek
DeepSeek R1 Distill Qwen 14B
DeepSeek R1 Distill Qwen 14B is a 14-billion-parameter reasoning-oriented chat model from DeepSeek, created by distilling DeepSeek-R1 reasoning traces into a Qwen-based dense model.
Input from
$1.60 / 1M tokens
across 1 provider
API Pricing
| Provider | Input / 1M | Output / 1M |
|---|---|---|
| $1.60 | $1.60 |
Prices updated daily. Last check: Sep 5, 2026
DeepSeek R1 Distill Qwen 14B pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Performance & Benchmarks
Source: Artificial Analysis →Intelligence
4.1 / 100
Math
55.7 / 100
Reasoning & Knowledge
- MMLU-Pro74.0%
- GPQA Diamond48.4%
- Humanity's Last Exam4.1%
Coding
- LiveCodeBench37.6%
Math
- AIME 202555.7%
- AIME66.7%
- MATH-50094.9%
Instruction & Long Context
- IFBench22.1%
- Long-Context Reasoning10.3%
Benchmarks measured Sep 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- DeepSeek
- Modalities
- Text
Capabilities
- Tool Calling
- No
- Open Source
- No
Strengths & Limitations
Strengths
- Distilled from DeepSeek-R1 reasoning traces, so it produces explicit chain-of-thought before final answers without special prompting
- Dense 14B parameter count fits on a single high-memory consumer or workstation GPU, making self-hosting practical
- Model weights are published, allowing local deployment, fine-tuning, and air-gapped use
- Mid-tier position in the R1 distill family offers a middle ground between the 7B/8B distills and the 32B/70B variants
- Strong focus on math, logic, and code reasoning tasks rather than general chat alone
- Available from multiple inference providers as well as for self-hosting, giving deployment flexibility
- Standard dense transformer architecture is supported by common serving stacks such as vLLM, SGLang, and llama.cpp derivatives
Limitations
- Verbose reasoning output inflates output token counts and therefore per-request cost and latency
- Smaller than the 32B and 70B R1 distills, which generally score higher on difficult reasoning benchmarks
- Text-only chat model — no image or audio input
- Reasoning-style output can be poorly suited to short-form tasks like classification or extraction, where the thinking preamble is wasted work
- We do not currently track verified throughput or time-to-first-token figures for this model, so performance varies by provider and hardware
Key Features
•Chain-of-thought reasoning output distilled from DeepSeek-R1
•14B dense parameter Qwen-based backbone
•Open published weights for self-hosting and fine-tuning
•Text-in, text-out chat completion interface
•Math and code problem-solving focus
•Part of the DeepSeek R1 distill family (1.5B / 7B / 8B / 14B / 32B / 70B)
•Compatible with mainstream open-source inference servers
•Single-GPU deployment footprint at common quantization levels
About DeepSeek R1 Distill Qwen 14B
DeepSeek R1 Distill Qwen 14B is part of DeepSeek's R1 distillation series, a set of smaller dense models trained on reasoning data generated by the larger DeepSeek-R1 model. Rather than being a scaled-down architecture of R1 itself, it uses a Qwen 14B dense backbone that has been fine-tuned so that it reproduces the step-by-step, chain-of-thought answering style of R1. Within the R1 distill lineup it sits in the mid-size range, above the 1.5B, 7B, and 8B variants and below the 32B and 70B distills, which makes it a common choice when a full R1 deployment is impractical but a very small distill is not accurate enough.
The model responds in R1's characteristic format: it emits an extended internal reasoning segment before producing a final answer, which means output token counts per request are typically much higher than for a conventional instruction-tuned chat model of similar size. It is a text-in, text-out chat model aimed at math, logic puzzles, code generation and debugging, and multi-step question answering. Because it is a dense 14B model with published weights, it can be self-hosted on a single mid-range or high-end GPU, and it is also offered through several inference API providers — see the pricing table on this page for who serves it and at what rates.
In practice this model is used where reasoning quality matters more than latency and where the cost or hardware footprint of a large mixture-of-experts reasoning model is prohibitive. Compared with the 32B and 70B distills it trades some accuracy on hard reasoning benchmarks for lower memory requirements and faster generation; compared with the 7B and 8B distills it generally holds up better on multi-step math and code tasks. Our database does not currently record throughput or time-to-first-token measurements for this model, so compare provider-reported performance before committing to a deployment.
Common Use Cases
This model suits workloads where multi-step reasoning is required but a large frontier reasoning model is too expensive or too heavy to host: competition-style math problems, algorithmic coding and debugging assistance, technical question answering that benefits from visible intermediate steps, and generating synthetic reasoning data for further fine-tuning. Its 14B dense size makes it a reasonable pick for on-premise or edge-adjacent deployments where data cannot leave a controlled environment, and for research teams who want to inspect or modify the weights. It is a weaker fit for high-volume, latency-sensitive tasks such as routing, moderation, or short classification calls, where the extended reasoning preamble adds cost without improving the result — a small non-reasoning instruct model is usually more appropriate there.
Frequently Asked Questions
How much does DeepSeek R1 Distill Qwen 14B cost?
Pricing depends on which provider serves the model and on the pricing type — per-token API billing, hourly dedicated endpoints, or your own GPU costs if you self-host. Because it emits long chain-of-thought sequences, output tokens tend to dominate the bill. Check the pricing table on this page for current rates across providers.
What is DeepSeek R1 Distill Qwen 14B best used for?
It is best used for math, logic, and coding tasks that benefit from explicit step-by-step reasoning, and for teams that want R1-style reasoning behavior in a model small enough to run on a single GPU.
How does it differ from the full DeepSeek-R1 model?
DeepSeek-R1 is a much larger mixture-of-experts reasoning model. This distill is a dense 14B Qwen-based model fine-tuned on reasoning traces generated by R1, so it imitates R1's answering style at a fraction of the serving cost, with lower accuracy on the hardest reasoning problems.
How does the 14B distill compare with the other R1 distills?
It sits in the middle of the family. The 1.5B, 7B, and 8B distills are cheaper and faster but less accurate on multi-step problems, while the 32B and 70B distills generally reason better at the cost of substantially more GPU memory.
Can I run this model on my own hardware?
Yes — the weights are published, and at 14B dense parameters the model fits on a single high-memory GPU, particularly with quantization. It works with common open-source inference servers.
Does it support image input?
No. It is a text-in, text-out chat model; multimodal input is not part of this release.