DeepSeek R1 Turbo
DeepSeek-R1-Turbo is a fast, efficient variant of DeepSeek's powerful R1 reasoning model, optimized for speed without sacrificing strong analytical performance.
DeepSeek
DeepInfraFast chain-of-thought reasoning for complex tasks
DeepSeek R1 Turbo is a text generation model developed by DeepSeek, designed as an accelerated variant of the R1 reasoning model family. It retains the chain-of-thought reasoning capabilities of the base R1 model while incorporating architectural and inference optimizations aimed at reducing latency. The model supports a 128,000-token context window and was trained on data through late 2024. It accepts text input and produces text output across a wide range of analytical and generative tasks.
DeepSeek R1 Turbo is particularly well-suited for applications where multi-step reasoning is required but response time is a practical constraint. Common use cases include coding assistance, mathematical problem-solving, logical deduction, and structured analytical workflows. Developers building interactive tools or real-time applications that depend on reasoning-intensive outputs are the primary intended audience for this model.
What DeepSeek R1 Turbo supports
Chain-of-Thought Reasoning
Applies multi-step reasoning to break down complex problems before producing an answer, inheriting the R1 family's approach to logical and analytical tasks.
Math Problem Solving
Handles multi-step mathematical problems by working through intermediate reasoning steps, making it suitable for quantitative analysis and scientific computation.
Code Generation
Generates and analyzes code across common programming languages, leveraging structured reasoning to handle algorithmic and debugging tasks.
Long Context Processing
Supports a 128,000-token context window, enabling analysis of lengthy documents, codebases, or multi-turn conversations within a single request.
Speed-Optimized Inference
The Turbo variant includes inference optimizations that reduce latency compared to the base R1 model, making it practical for interactive and real-time applications.
Logical Deduction
Performs structured logical deduction and problem decomposition, useful for tasks like scientific analysis, reasoning chains, and decision support.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 84.9% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 81.3% |
| MATH-500 | Undergraduate and competition-level math problems | 98.3% |
| AIME 2024 | American math olympiad problems | 89.3% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 77.0% |
| HLE | Questions that challenge frontier models across many domains | 14.9% |
| SciCode | Scientific research coding and numerical methods | 40.3% |
Common questions about DeepSeek R1 Turbo
What is the context window for DeepSeek R1 Turbo?
DeepSeek R1 Turbo supports a context window of 128,000 tokens, allowing it to process long documents, extended conversations, or large codebases in a single request.
What is the knowledge cutoff date for this model?
Based on the available metadata, DeepSeek R1 Turbo was trained on data through late 2024.
How does DeepSeek R1 Turbo differ from the base DeepSeek R1 model?
The Turbo variant is optimized for faster inference speeds through architectural and inference-level changes, while retaining the chain-of-thought reasoning capabilities of the base R1 model.
What types of tasks is DeepSeek R1 Turbo best suited for?
It is designed for tasks requiring multi-step reasoning, including mathematics, coding, logical deduction, and structured analytical workflows, particularly in contexts where response latency matters.
What input and output types does DeepSeek R1 Turbo support?
DeepSeek R1 Turbo accepts text input and produces text output. It is classified as a text generation model.
Documentation & links
Parameters & options
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