R1 0528
DeepSeek R1 0528 excels at advanced reasoning and coding tasks
A tradução deste card ainda não está disponível; parte do conteúdo aparece em inglês.
- Janela de contexto (tokens)
- 64.000
- Inteligência
- 13.1
- Velocidade
- —–
- Custo/tarefa
- $0.022
DeepSeek R1 0528 is built for users who need deep analytical thinking and precise code generation. It handles multi-step reasoning, mathematical problem solving, and software development tasks with remarkable accuracy. The model maintains context well across long conversations and adapts to diverse writing styles and technical requirements.
Use this model when you need rigorous logic, debugging assistance, algorithm design, or structured content creation. It performs especially well in scenarios requiring step-by-step explanations, technical documentation, or creative writing with complex constraints.
What sets it apart is its balance of reasoning depth and practical usability — it doesn't just generate text, it works through problems methodically, making it reliable for both learning and production-grade tasks.
- Complex code generation and debugging
- Multi-step mathematical reasoning
- Technical documentation and tutorials
- Algorithm design and optimization
- Structured analytical writing
Excels at breaking down complex problems into logical steps with clear explanations.
Generates accurate, well-structured code across multiple programming languages and frameworks.
Maintains coherence and detail across long, multi-turn conversations and large inputs.
- Provide clear constraints and expected output format for best results
- Break complex tasks into sequential prompts for improved accuracy
- Include example inputs or test cases when requesting code solutions
- Use system-style instructions to define role, tone, and reasoning depth
- May over-explain simple queries with unnecessary detail
- Occasionally struggles with highly ambiguous or subjective creative prompts
- Performance can vary on niche or low-resource programming languages
Design a high-availability distributed caching layer with consistent hashing, automatic failover, and TTL-based eviction. Provide architecture diagram in Mermaid, core components, and Python pseudocode for the client interface.
Find all integer solutions to x^3 + y^3 + z^3 = 33. Show your reasoning, modular arithmetic steps, and final answer with verification.
Refactor this 200-line monolithic Python script into a modular package with clean architecture: separate concerns into models, services, repositories, and CLI. Add type hints, docstrings, and unit test structure using pytest.
Fixes randomness for reproducible results — use the same seed to get identical outputs across runs.
Limits token selection to the most probable cumulative percentage — lower values make responses more predictable, higher allows more diversity.
Sets the maximum length of the generated response — increase for longer outputs like code or essays.
Controls randomness — lower values make output more focused and deterministic, higher values increase creativity and variation.
Defines text strings that halt generation — useful for preventing unwanted continuation or formatting issues.
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Can it write production-ready code?
Yes, it generates syntactically correct and logically sound code suitable for real-world use with minor review.
Does it support multiple programming languages?
It handles major languages like Python, JavaScript, Rust, and Go with strong proficiency.
Is it good for creative writing?
It performs well on structured creative tasks but may lack spontaneity in open-ended storytelling.