DeepSeek-R1
DeepSeek-R1 is an AI model published by DeepSeek-AI, released on 2025-01-20, for Reasoning model, with 671B parameters, and 128K context length, requiring about 134GB storage, with a 97.30 score on MATH-500.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Model basics
Open source & experience
Official resources
API details
| Type | Condition | Input | Output |
|---|---|---|---|
| Text | - | $0.550/ 1M | $2.19/ 1M |
| Type | TTL | Write | Read |
|---|---|---|---|
| Text | - | $0.550/ 1M | $0.140/ 1M |
Benchmark Results
DeepSeek-R1 currently shows benchmark results led by MMLU (8 / 66, score 90.80), MMLU Pro (38 / 132, score 84), MATH-500 (13 / 44, score 97.30). This page also consolidates core specs, context limits, and API pricing so you can evaluate the model from benchmark results and deployment constraints together.
General Knowledge
4 evaluationsCoding and Software Engineer
2 evaluationsMath and Reasoning
3 evaluationsWriting and Creative Capabilities
1 evaluationsCompare with other models
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Publisher
Model Overview
DeepSeek-R1 is an AI model published by DeepSeek-AI, released on 2025-01-20, for Reasoning model, with 671B parameters, and 128K context length, requiring about 134GB storage, with a 97.30 score on MATH-500.
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