DeepSeek Available Models: Supported Model Families, Version Differences, Capability Comparison, And Access Conditions
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DeepSeek provides an extensive range of large language models and code models designed to serve a wide spectrum of use cases from open-ended conversation and reasoning to code completion and mathematical problem solving. The platform’s offerings include both hosted API endpoints and open-weight models for self-hosted or research deployments. With rapid updates and a transparent changelog, DeepSeek continues to refine its models, extending both accessibility and performance to individual users, enterprises, and the broader AI research community.
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DeepSeek’s model catalog features general, reasoning, and code-specialized models for different user needs.
The DeepSeek ecosystem is anchored by several major model families. The DeepSeek-V3.x generation is the current flagship, powering the platform’s most capable general-purpose models for both chat and structured reasoning tasks. DeepSeek-V3.x is implemented as the backbone for deepseek-chat (optimized for conversational throughput and general writing in non-thinking mode) and deepseek-reasoner (dedicated to multi-step logic and analysis in thinking mode).
Another notable line is DeepSeek-R1, which is focused on high-performance reasoning, math, and logical tasks. DeepSeek-R1 and its successors, such as DeepSeek-R1-0528, introduce iterative improvements specifically aimed at deepening logical inference and structured chain-of-thought.
For organizations or individuals requiring models that excel at code understanding and generation, DeepSeek-Coder-V2 provides a code-specialized architecture. This family leverages a mixture-of-experts approach to rival leading code models, supporting developer workflows across code completion, debugging, and translation.
Earlier generalist checkpoints like DeepSeek-V2.5-1210 remain relevant for users with moderate compute resources, striking a balance between performance, licensing flexibility, and deployment footprint.
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Version differences reflect significant leaps in architecture, training methods, and deployment flexibility.
With each major release, DeepSeek has introduced distinct advances in model architecture and post-training. DeepSeek-V3.1 is recognized for supporting both hybrid inference (thinking and non-thinking) in a single model, with improvements in agent skills, tool use, and long-context tasks. This release reflects DeepSeek’s movement toward multi-modal agent workflows, where models can reason, plan, and generate content within the same context window.
DeepSeek’s public changelogs show a pattern of rolling upgrades, with API endpoints such as deepseek-chat and deepseek-reasoner periodically mapped to the latest V3.x backbone. This means that a developer using the stable API names automatically benefits from ongoing improvements but may see changes in model behavior and capability with each backend refresh.
For users needing explicit version control, DeepSeek provides open-weight checkpoints like DeepSeek-V3-0324 and DeepSeek-V3.1 on platforms such as Hugging Face. These are released under permissive licenses, supporting fine-tuning, private deployment, or integration into proprietary workflows.
DeepSeek-R1 and R1-0528 further highlight DeepSeek’s focus on advanced reasoning, with targeted post-training on mathematical, logical, and structured reasoning benchmarks. Meanwhile, DeepSeek-V2.5-1210’s release marks an earlier stage with documented gains in math and code but a lighter computational footprint.
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DeepSeek models offer distinct capabilities across conversation, reasoning, and code generation.
The core DeepSeek model endpoints provide a range of specialized strengths. The deepseek-chat endpoint, continuously upgraded to the latest V3.x backend, is suited for drafting, summarization, general Q&A, and routine communication. Its non-thinking mode prioritizes fluency and speed for everyday tasks.
The deepseek-reasoner endpoint leverages the same V3.x architecture but operates in thinking mode, supporting extended chain-of-thought, multi-step logic, and analytical work. DeepSeek’s published API documentation details model-specific context limits, such as a 64K context window and an 8K output cap, with the reasoner endpoint benefiting from a larger maximum chain-of-thought token budget for deeper reasoning.
DeepSeek-R1 and its variants are tuned for tasks where reasoning depth, math, or structured analysis are central. These models often require more tokens for long-form explanations but provide stronger results for logic-heavy workflows.
DeepSeek-Coder-V2 Instruct and related code-focused variants excel at developer-centric use cases—code completion, language-to-code translation, code review, and debugging—delivering competitive results to leading code LLMs.
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DeepSeek Model Family Capabilities and Best-Fit Workloads
Model Family / Endpoint | Strengths | Best-Fit Workloads | Access Path |
deepseek-chat (API) | Conversational ability, speed | Drafts, summaries, general chat, customer support | Hosted API |
deepseek-reasoner(API) | Deep reasoning, structured logic | Multi-step analysis, math, planning, hard Q&A | Hosted API |
DeepSeek-V3.1 / V3-0324 | General language, hybrid inference | Self-hosted assistants, flexible workflows | Open checkpoint |
DeepSeek-R1 / R1-0528 | Advanced math and logical reasoning | Math pipelines, structured data, analytical reports | Open checkpoint |
DeepSeek-V2.5-1210 | Generalist with moderate compute needs | General deployment, resource-constrained inference | Open checkpoint |
DeepSeek-Coder-V2 | Code generation, debugging, refactoring | Code completion, language-to-code, developer tasks | Open checkpoint |
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Access conditions include hosted API usage, open-weight licensing, and resource requirements.
DeepSeek’s hosted APIs (deepseek-chat and deepseek-reasoner) operate under a usage-based billing model, charging per input and output token. These endpoints are subject to DeepSeek’s published limits, such as context length and output token caps, and may enforce dynamic rate limits during periods of peak demand.
Open-weight checkpoints, including V3.x, R1, V2.5, and Coder models, are published under permissive licenses (such as MIT for V3-0324) on Hugging Face. These models allow self-hosted deployment, research use, and integration into private stacks, but require significant compute for inference, especially for the largest variants. Users must manage memory, hardware, and scaling orchestration for optimal performance.
Operational access is also influenced by DeepSeek’s infrastructure management. There have been instances of temporary API service suspensions or credit top-up limits during demand surges, as well as ongoing upgrades to backend model mappings. Users seeking full consistency or enterprise stability may choose to pin to specific open checkpoints.
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DeepSeek Model Access and Licensing Table
Model / Endpoint | Licensing / Access | Context & Output Limits | Deployment Notes |
deepseek-chat (API) | Usage-based API | 64K context, 8K output | Upgrades map to new V3.x backend |
deepseek-reasoner (API) | Usage-based API | 64K context, 8K output, | Enhanced CoT for reasoning |
DeepSeek-V3.1, V3-0324 | MIT License, open weights | Dependent on hardware | Self-host, fine-tune, integrate |
DeepSeek-R1 / R1-0528 | Open weights, permissive | Dependent on hardware | Reasoning pipelines, math |
DeepSeek-V2.5-1210 | Open weights | Dependent on hardware | Efficient, solid generalist |
DeepSeek-Coder-V2 | Open weights | Dependent on hardware | Optimized for code |
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DeepSeek model development and selection are guided by performance, deployment needs, and version transparency.
The choice of DeepSeek model depends on use case complexity, resource availability, and operational priorities. For dynamic, hosted conversational or reasoning workflows, the deepseek-chat and deepseek-reasoner endpoints offer evolving best-in-class capabilities. For enterprise, research, or private deployments, open-weight checkpoints provide explicit version control, integration flexibility, and the freedom to self-host under permissive licensing.
Performance advances with each version, especially in multi-step reasoning, code synthesis, and long-context support, mean organizations can match their AI strategy to DeepSeek’s public roadmap and changelog cadence. Whether accessed through hosted APIs or open weights, DeepSeek’s ecosystem is engineered to enable robust, adaptable, and high-performance AI for diverse professional and research environments.
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