We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.
With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors.
However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance,
we introduce DeepSeek-R1, which incorporates cold-start data before RL.
DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.
NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.
2. Model Summary
Post-Training: Large-Scale Reinforcement Learning on the Base Model
We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area.
We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities.
We believe the pipeline will benefit the industry by creating better models.
Distillation: Smaller Models Can Be Powerful Too
We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future.
Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community.
DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base.
For more details regarding the model architecture, please refer to DeepSeek-V3 repository.
DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1.
We slightly change their configs and tokenizers. Please use our setting to run these models.
4. Evaluation Results
DeepSeek-R1-Evaluation
For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 64 responses per query to estimate pass@1.
Category
Benchmark (Metric)
Claude-3.5-Sonnet-1022
GPT-4o 0513
DeepSeek V3
OpenAI o1-mini
OpenAI o1-1217
DeepSeek R1
Architecture
-
-
MoE
-
-
MoE
# Activated Params
-
-
37B
-
-
37B
# Total Params
-
-
671B
-
-
671B
English
MMLU (Pass@1)
88.3
87.2
88.5
85.2
91.8
90.8
MMLU-Redux (EM)
88.9
88.0
89.1
86.7
-
92.9
MMLU-Pro (EM)
78.0
72.6
75.9
80.3
-
84.0
DROP (3-shot F1)
88.3
83.7
91.6
83.9
90.2
92.2
IF-Eval (Prompt Strict)
86.5
84.3
86.1
84.8
-
83.3
GPQA-Diamond (Pass@1)
65.0
49.9
59.1
60.0
75.7
71.5
SimpleQA (Correct)
28.4
38.2
24.9
7.0
47.0
30.1
FRAMES (Acc.)
72.5
80.5
73.3
76.9
-
82.5
AlpacaEval2.0 (LC-winrate)
52.0
51.1
70.0
57.8
-
87.6
ArenaHard (GPT-4-1106)
85.2
80.4
85.5
92.0
-
92.3
Code
LiveCodeBench (Pass@1-COT)
33.8
34.2
-
53.8
63.4
65.9
Codeforces (Percentile)
20.3
23.6
58.7
93.4
96.6
96.3
Codeforces (Rating)
717
759
1134
1820
2061
2029
SWE Verified (Resolved)
50.8
38.8
42.0
41.6
48.9
49.2
Aider-Polyglot (Acc.)
45.3
16.0
49.6
32.9
61.7
53.3
Math
AIME 2024 (Pass@1)
16.0
9.3
39.2
63.6
79.2
79.8
MATH-500 (Pass@1)
78.3
74.6
90.2
90.0
96.4
97.3
CNMO 2024 (Pass@1)
13.1
10.8
43.2
67.6
-
78.8
Chinese
CLUEWSC (EM)
85.4
87.9
90.9
89.9
-
92.8
C-Eval (EM)
76.7
76.0
86.5
68.9
-
91.8
C-SimpleQA (Correct)
55.4
58.7
68.0
40.3
-
63.7
Distilled Model Evaluation
Model
AIME 2024 pass@1
AIME 2024 cons@64
MATH-500 pass@1
GPQA Diamond pass@1
LiveCodeBench pass@1
CodeForces rating
GPT-4o-0513
9.3
13.4
74.6
49.9
32.9
759
Claude-3.5-Sonnet-1022
16.0
26.7
78.3
65.0
38.9
717
o1-mini
63.6
80.0
90.0
60.0
53.8
1820
QwQ-32B-Preview
44.0
60.0
90.6
54.5
41.9
1316
DeepSeek-R1-Distill-Qwen-1.5B
28.9
52.7
83.9
33.8
16.9
954
DeepSeek-R1-Distill-Qwen-7B
55.5
83.3
92.8
49.1
37.6
1189
DeepSeek-R1-Distill-Qwen-14B
69.7
80.0
93.9
59.1
53.1
1481
DeepSeek-R1-Distill-Qwen-32B
72.6
83.3
94.3
62.1
57.2
1691
DeepSeek-R1-Distill-Llama-8B
50.4
80.0
89.1
49.0
39.6
1205
DeepSeek-R1-Distill-Llama-70B
70.0
86.7
94.5
65.2
57.5
1633
5. Chat Website & API Platform
You can chat with DeepSeek-R1 on DeepSeek's official website: chat.deepseek.com, and switch on the button "DeepThink"
We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:
