DeepSeek R1 Turbo
DeepSeek R1 Turbo is a reasoning-focused text generation model from DeepSeek with a 128,000 token context window.
DeepSeek
DeepInfraFast reasoning with extended chain-of-thought
DeepSeek R1 Turbo is a text generation model developed by DeepSeek and released in January 2025. It is a variant of the DeepSeek R1 family, optimized for faster inference while retaining the chain-of-thought reasoning approach that characterizes the R1 series. The model is served through Deep Infra and is available on MindStudio for use in AI-powered applications without requiring direct API configuration.
DeepSeek R1 Turbo is designed for tasks that benefit from explicit reasoning steps, such as mathematical problem solving, logical analysis, and structured question answering. It supports a context window of 128,000 tokens and a maximum response size of 8,000 tokens, making it suitable for processing long documents or multi-turn conversations. The Turbo designation indicates a focus on throughput and latency improvements relative to the base R1 model, making it a practical choice for applications where response speed matters alongside reasoning depth.
What DeepSeek R1 Turbo supports
Chain-of-Thought Reasoning
Generates explicit intermediate reasoning steps before producing a final answer, following the R1 series approach to structured problem solving.
Long Context Handling
Supports a 128,000 token context window, allowing it to process lengthy documents, codebases, or extended conversation histories in a single request.
Mathematical Problem Solving
Applies step-by-step reasoning to solve multi-step math problems, a documented strength of the DeepSeek R1 model family.
Code Generation
Produces and analyzes code across common programming languages, leveraging its reasoning capabilities to work through logic before outputting solutions.
Optimized Inference Speed
The Turbo variant is designed for reduced latency compared to the base R1 model, making it suitable for applications requiring faster response times.
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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, which allows it to handle long documents, extended conversations, or large codebases within a single request.
What is the maximum response size?
The model has a maximum response size of 8,000 tokens per output, which applies to each individual generation.
What is the difference between DeepSeek R1 and DeepSeek R1 Turbo?
DeepSeek R1 Turbo is a variant of the R1 model optimized for faster inference. Both use chain-of-thought reasoning, but the Turbo version is intended to reduce latency while maintaining reasoning capability.
What tasks is DeepSeek R1 Turbo best suited for?
The model is well suited for tasks requiring structured reasoning, such as mathematical problem solving, logical analysis, code generation, and detailed question answering over long documents.
Is pricing information available for DeepSeek R1 Turbo on MindStudio?
Specific pricing details are not published in the current model metadata. You can check MindStudio's platform for up-to-date usage and billing information.
What is the knowledge cutoff date for DeepSeek R1 Turbo?
The model was released in January 2025. DeepSeek has not publicly specified an exact training data cutoff date for the R1 Turbo variant in available documentation.
Parameters & options
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