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.
DeepSeek
DeepSeek R1 Turbo
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.
Unknown
128,000 context
8,000 tokens output
Chain-of-Thought Reasoning
Math Problem Solving
Code Generation
Long Context Processing
Speed-Optimized Inference
Logical Deduction
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
DeepSeek
Input Context Window
The number of tokens supported by the input context window.
128,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
8,000 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Unknown
Knowledge Cut-off Date
When the model's knowledge was last updated.
Unknown
API Providers
The providers that offer this model. This is not an exhaustive list.
DeepSeek API
Modalities
Types of data this model can process.
Text
What is DeepSeek R1 Turbo
A fuller summary of positioning, capabilities, and source-specific details for DeepSeek R1 Turbo.
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.
Capabilities
What DeepSeek R1 Turbo supports
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.
Pricing for DeepSeek R1 Turbo
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$1.00
Per million tokens
Output tokens
N/A
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
2
maxResponseSize
8,000 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
DeepSeek API
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
|
|
|
GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
|
|
HLE
Questions that challenge frontier models across many domains
|
|
|
LiveCodeBench
Real-world coding tasks from recent competitions
|
|
|
MATH-500
Undergraduate and competition-level math problems
|
|
|
MMLU-Pro
Expert knowledge across 14 academic disciplines
|
|
|
SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
FAQ
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.
More models from DeepSeek
Continue browsing adjacent models from the same provider.