# DeepSeek R1 ![Organization](https://img.shields.io/badge/Org-DeepSeek-2ea44f) ![Context](https://img.shields.io/badge/Context-128K%20tokens-1f6feb) ![Pricing](https://img.shields.io/badge/Price-%240.55%20%2F%20%242.19%20per%201M-2da44e) ![License](https://img.shields.io/badge/License-MIT-d29922) ![Modality](https://img.shields.io/badge/Modality-Text-8957e5) ![Released](https://img.shields.io/badge/Released-Jan%202025-555) > **Quick answer:** DeepSeek R1 is an open-weight chain-of-thought reasoning model released January 20, 2025 under the MIT licence. It is a 671B sparse MoE model (37B parameters active per forward pass) that achieves 96.4% on the AI2 Reasoning Challenge, rivalling closed frontier models at a fraction of the cost. Available via the DeepSeek API at ~$0.55 input / $2.19 output per 1M tokens, or free to self-host. ## At a Glance **Where DeepSeek R1 leads** - 96.4% AI2 Reasoning Challenge — top-tier multi-discipline science reasoning, on par with the best closed models - Open weights under MIT licence — free to download, audit, and self-host - ~$0.55/$2.19 per 1M tokens via DeepSeek API — among the most cost-efficient frontier reasoning models - 671B sparse MoE: high capability with only 37B active parameters per token - Strong math and coding benchmark scores at launch (AIME 2024, MATH-500) **Where it lags** - Text only — no vision or multimodal capability - 128K context window — smaller than Gemini 2.5 Pro (1M) or GPT-5.6 Sol (1.1M) - Self-hosting a 671B MoE model requires significant infrastructure investment - Performance on agentic/tool-use tasks not as thoroughly evaluated as closed models **Best for:** Research, cost-sensitive reasoning pipelines, open-weight deployments where licence and auditability matter, and high-throughput scientific analysis tasks. ## What DeepSeek R1 Is DeepSeek R1 is DeepSeek's flagship reasoning model, trained using reinforcement learning from scratch to develop chain-of-thought reasoning capabilities without supervised fine-tuning as the primary training signal. This approach — described in the R1 technical report — produced a model that reasons through problems step by step before generating a final answer, similar in principle to OpenAI's o-series. The model's architecture is a 671B sparse Mixture-of-Experts (MoE) transformer. MoE means only a fraction of the parameters (37B) are active for any given token, enabling high-quality outputs at lower inference cost than a dense 671B model would require. This efficiency makes R1 economically viable as an API offering and practical for teams with sufficient GPU infrastructure to self-host. R1's release in January 2025 was significant not just for its performance — which matched or exceeded then-frontier closed models on math and science benchmarks — but for its licence: MIT, meaning unrestricted commercial use and the ability to fine-tune and deploy the weights without any API dependency. ## Specifications | Field | Value | |---|---| | Organization | DeepSeek | | Parameters | 671B total (37B active per token) | | Context window | 128,000 tokens | | Architecture | Sparse MoE transformer | | License | MIT (open weights) | | HuggingFace | [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) | | Release date | January 20, 2025 | | Knowledge cutoff | July 2024 (estimated) | | Modality | Text | ## Pricing | | Input (per 1M tokens) | Output (per 1M tokens) | |---|---|---| | DeepSeek API | ~$0.55 | ~$2.19 | | Self-hosted | Free (infrastructure cost only) | Free (infrastructure cost only) | Pricing per the [DeepSeek API pricing page](https://api-docs.deepseek.com/quick_start/pricing) — rates may vary; verify before production use. ## Context Window DeepSeek R1 has a **128,000-token context window** — roughly **90 pages** of text in a single request. This is sufficient for most document analysis and coding tasks but is a constraint compared to long-context models like Gemini 2.5 Pro (1M) or GPT-5.6 Sol (1.1M). For long-context retrieval workloads, consider newer DeepSeek models or alternatives with larger windows. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [AI2 Reasoning Challenge](/benchmarks/ai2-reasoning-challenge) | 96.4% | Benchgen evaluation | 2025-07 | | [SHADE-Arena](/benchmarks/shade-arena) | 0.0 overall success | [Anthropic research post](https://www.anthropic.com/research/shade-arena-sabotage-monitoring) | 2025-06 | *Scores from Benchgen evaluations or attributed third-party sources. The SHADE-Arena score of 0.0 indicates R1 did not engage in sabotage behaviour in that safety evaluation — this is expected for a well-aligned model.* ## DeepSeek R1 vs Alternatives | Model | Context | AI2 RC | Licence | Price (in/out per 1M) | |---|---|---|---|---| | **DeepSeek R1** | 128K | 96.4% | MIT (open) | ~$0.55 / $2.19 | | [o4-mini](/models/openai/o4-mini) | 200K | 95.9% | Proprietary | $1.10 / $4.40 | | [o3](/models/openai/o3) | 200K | — | Proprietary | $10 / $40 | | [Claude 3.7 Sonnet](/models/anthropic/claude-3-7-sonnet) | 200K | — | Proprietary | $3 / $15 | | [Gemini 2.5 Pro](/models/google/gemini-2-5-pro) | 1M | — | Proprietary | $1.25 / $10 | R1's strongest case vs o4-mini: marginally higher AI2 Reasoning Challenge score (96.4% vs 95.9%), MIT licence for unrestricted use, and lower API cost. The key trade-offs: R1 has a smaller context window (128K vs 200K) and is text-only (no vision), and o4-mini has more extensive agentic and tool-use evaluation coverage. ## How DeepSeek R1 Performs on Real Agent Tasks R1's SHADE-Arena score of 0.0 overall success reflects the benchmark's safety-evaluation design — not engaging in sabotage is the correct behaviour. Its AI2 Reasoning Challenge score of 96.4% — essentially matching o4-mini (95.9%) — confirms its strong multi-discipline reasoning capability. For agent builders, R1's open weights are the primary differentiator. Teams that need to run inference on-premise (for data privacy, regulatory, or latency reasons), fine-tune the model for a specific domain, or avoid API dependency will find R1 uniquely positioned among frontier-class reasoning models. For cloud API use without those constraints, o4-mini and Gemini 2.5 Pro offer broader tool-use and multimodal evaluation coverage. ## Use DeepSeek R1 via API ```python from openai import OpenAI # DeepSeek API is OpenAI-compatible client = OpenAI( api_key="YOUR_DEEPSEEK_API_KEY", base_url="https://api.deepseek.com" ) response = client.chat.completions.create( model="deepseek-reasoner", messages=[{"role": "user", "content": "Solve this differential equation step by step..."}], ) print(response.choices[0].message.content) ``` ## Related Models & Benchmarks - Compare: [o4-mini](/models/openai/o4-mini) · [o3](/models/openai/o3) · [Claude 3.7 Sonnet](/models/anthropic/claude-3-7-sonnet) · [DeepSeek R1 0528](/models/deepseek/deepseek-r1-0528) - Benchmarks: [AI2 Reasoning Challenge](/benchmarks/ai2-reasoning-challenge) · [SHADE-Arena](/benchmarks/shade-arena) ## Frequently Asked Questions

What is DeepSeek R1?

DeepSeek R1 is an open-weight chain-of-thought reasoning model released January 20, 2025 by DeepSeek under the MIT licence. It is a 671B sparse MoE model (37B active parameters) that matches or exceeds frontier closed models on math and science reasoning benchmarks.

What is DeepSeek R1's context window?

DeepSeek R1 supports a 128,000-token context window — roughly 90 pages of text in a single request.

How much does DeepSeek R1 cost?

Via the DeepSeek API, R1 costs approximately $0.55 per 1M input tokens and $2.19 per 1M output tokens. As open-weight model under the MIT licence, it is also free to self-host (infrastructure costs apply).

Is DeepSeek R1 open source?

Yes. DeepSeek R1 is released under the MIT licence, which permits unrestricted commercial use, modification, and redistribution. The weights are available on HuggingFace at deepseek-ai/DeepSeek-R1.

What is the difference between DeepSeek R1 and o4-mini?

Both are frontier reasoning models with similar AI2 Reasoning Challenge scores (R1: 96.4%, o4-mini: 95.9%). R1 is open-weight (MIT) and cheaper via API (~$0.55/$2.19 per 1M). o4-mini is closed, costs $1.10/$4.40, has a larger context window (200K vs 128K), and supports vision. o4-mini has more extensive agentic benchmark coverage.

What is DeepSeek R1's knowledge cutoff?

DeepSeek R1's training knowledge cutoff is estimated at July 2024.

--- *Specs and scores sourced from DeepSeek's official R1 technical report (January 2025) and Benchgen evaluations; third-party benchmark scores attributed inline. API pricing cited to the DeepSeek pricing page. Last updated 2026-07-23.*

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