DeepSeek-R1 is a 671B parameter Mixture-of-Experts (MoE) model with 37B activated parameters per token, trained via large-scale reinforcement learning with a focus on reasoning capabilities. It incorporates two RL stages for discovering improved reasoning patterns and aligning with human preferences, along with two SFT stages for seeding reasoning and non-reasoning capabilities. The model achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
| GPT‑5.1 | DeepSeek-R1 | |
|---|---|---|
Web Site
| ||
Provider
| ||
Chat
| ||
Release Date
| ||
Modalities
| text images | text |
API Providers
| OpenAI API (Responses & Chat Completions),ChatGPT (Instant & Thinking),Codex (CLI & IDE) | DeepSeek, HuggingFace |
Knowledge Cut-off Date
| 06.2025 | Unknown |
Open Source
| No | Yes |
Pricing Input
| $1.25 per million tokens | $0.55 per million tokens |
Pricing Output
| $10.00 per million tokens | $2.19 per million tokens |
MMLU
| - | 90.8% Pass@1 Source |
MMLU-Pro
| 78.4% Source | 84% EM Source |
MMMU
| 85.4% Source | - |
HellaSwag
| - | - |
HumanEval
| - | - |
MATH
| - | - |
GPQA
| 88.1% Diamond Source | 71.5% Pass@1 Source |
IFEval
| - | 83.3% Prompt Strict Source |
SimpleQA
| - | - |
AIME 2024 | - | - |
AIME 2025 | 94% Source | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| - | - |
MathVista
| - | - |
Mobile Application | ||
MathArena | ||
| Avg. Score | - | 82% |
AIME 2025 A test based on problems from the American Invitational Mathematics Examination, designed to assess the mathematical skills of models. | - | 89% |
HMMT February 2025 A test based on problems from the Harvard-MIT Mathematics Tournament, February 2025, designed to assess the mathematical skills of models. | - | 77% |
BRUMO 2025 | - | 92% |
SMT 2025 A test based on problems from the Stanford Math Tournament, 2025, designed to assess the mathematical skills of models. | - | 83% |
CMIMC 2025 A test based on problems from the Canadian Mathematical Olympiad, 2025, designed to assess the mathematical skills of models. | - | 69% |
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