DeepSeek
DeepSeek R1
model
deepseek-r1MidStableCommunityStreamingReasoningBatchLong contextChina
Context
128K
Input / 1M tokens
$0.5500
Output / 1M tokens
$2.19
Cached input / 1M tokens
— (bills at input price)
Cache write / 1M tokens
— (bills at input price)
About this model
DeepSeek R1
DeepSeek R1 is the reasoning-specialist sibling of V3 — trained with reinforcement learning to think step-by-step before answering. It matches or beats OpenAI o1 on math and competitive programming while costing ~80% less.
Key facts
- Maker
- DeepSeek
- Context window
- 128K tokens
- Max output
- 4K tokens
- Lane
- Community: needs account opt-in; the provider may use data for training
- Modality
- Text
- Input price
- $0.5500 per 1M tokens
- Output price
- $2.19 per 1M tokens
- Processing region(s)
- cn
- Route
- Via SiliconFlow's API
- Cheapest plan
- Available from the Pro plan
- Price comparison
- Priced below 51 of 113 pro-band community models on Anoman (blended price: 1 part input to 3 parts output).
Released
2025-01
Training cutoff
2024-07
Parameters
MoE 671B (37B active)
Best for
Use cases
- Competition-grade mathematics (AIME, MATH)
- Algorithmic coding and competitive programming
- Multi-step logical inference
- Scientific reasoning + research summarization
- Any workload where you'd otherwise reach for o1
Strengths
What it does well
- AIME 2024: 79.8% (matches o1)
- MATH-500: 97.3% — near-saturating the benchmark
- Codeforces Elo: 2029 (96.3 percentile)
- Emits explicit chain-of-thought you can inspect or hide
- Open weights (MIT license) for self-host fallback
Limitations
Know the trade-offs
- Output token count is high — chain-of-thought adds 2–5× the tokens vs a normal chat model
- Slower TTFT — minimum ~3 s latency on first token while it thinks
- Less suited to conversational UX — pair with V3 for chat, R1 for hard problems
Benchmarks
Published scores
Scores from official model cards and public leaderboards. Higher is better unless noted.
| Benchmark | Score | Measures |
|---|---|---|
| AIME 2024 | 79.8 | Competition math, high-school olympiad |
| MATH-500 | 97.3 | Mathematics, competition + textbook |
| HumanEval | 96.3 | Python code generation, pass@1 |
| MMLU | 90.8 | General knowledge across 57 subjects |
| Codeforces | 96.3 | Percentile rank vs human competitive programmers |
Code example
Chat completion with DeepSeek R1
from openai import OpenAI
client = OpenAI(
base_url="https://api.anoman.io/v1",
api_key="anm-sk-..."
)
response = client.chat.completions.create(
model="deepseek-r1",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)Alternatives
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