DeepSeek Available Models: Supported API Models, Version Differences, Capabilities Comparison, And Access Requirements

DeepSeek offers a growing set of large language models for both API integration and open-weight community use, each with distinct capabilities, context handling, and usage conventions.

The current experience is shaped by which API model IDs are callable, which underlying versions are active, and which open-weight variants are available for research and local deployment.

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The Main API Models Are “deepseek-chat” And “deepseek-reasoner” With Distinct Modes.

DeepSeek’s primary API offering centers on two model identifiers: deepseek-chat and deepseek-reasoner.

These represent different operational modes of DeepSeek’s flagship model line, corresponding to standard chat and advanced reasoning.

The deepseek-chat model operates in non-thinking mode, providing speed and general assistant behaviors optimized for fast conversation and practical tasks.

The deepseek-reasoner model enables thinking mode, supporting multi-step reasoning workflows, chain-of-thought tasks, and more in-depth analytical exchanges.

These two endpoints cover most production uses, with support for JSON output, tool calling, and beta features such as chat prefix completion.

Differences in output defaults, maximum output lengths, and context handling reflect the split between chat and reasoning workflows.

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The Latest Version Across Both Modes Is DeepSeek-V3.2, With Special Variants For Evaluation.

Both main API endpoints are currently backed by the DeepSeek-V3.2 model family.

Updates and improvements are rolled out under the V3 line, with earlier releases including DeepSeek-V3, V3.1, V3.1-Terminus, and V3.2-Exp.

A special variant, DeepSeek-V3.2-Speciale, has been offered for evaluation through a separate endpoint, focusing on maximal reasoning but with temporary restrictions such as limited tool calling.

The V3.2 series emphasizes long-context efficiency, speed, and improved performance across reasoning and general chat applications.

Version lineage is maintained in release notes and impacts which features and context lengths are available on each API model.

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Capabilities Differ Between Chat And Reasoning Modes, With Some Beta Features Unique To Chat.

Both deepseek-chat and deepseek-reasoner support modern API workflows, including JSON output formatting, tool calling for function-based tasks, and beta chat prefix completion.

FIM (Fill-in-the-Middle) completion is supported on deepseek-chat but not on deepseek-reasoner.

Default and maximum output limits may differ, especially when comparing non-thinking versus thinking workflows, reflecting their intended usage.

The reasoning endpoint is better suited for multi-step problems and explicit chain-of-thought tasks, while the chat endpoint is optimized for interactive general assistance.

Context window is documented at 128,000 tokens for the current V3.2 endpoints, but some legacy and documentation pages may reference 64,000 tokens or additional reasoning-specific token budgets.

Experimental and special endpoints may carry additional restrictions or feature limitations depending on evaluation status.

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DeepSeek API Models And Their Key Differences.

Model ID	Current Version	Mode	Notable Capabilities	Feature Differences
deepseek-chat	V3.2	Non-thinking	Fast conversation, general tasks, tool use, JSON, FIM (beta)	Supports FIM, higher speed
deepseek-reasoner	V3.2	Thinking	Multi-step reasoning, advanced chain-of-thought	No FIM, optimized for reasoning
Speciale/Exp variants	V3.2-Speciale, V3.2-Exp	Experimental	Long-context, special reasoning tasks	May restrict tools, API-only, evaluation focus

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Open-Weight Models Include Both Flagship Releases And Distilled Variants For Local Use.

DeepSeek’s open-weight family includes full-scale releases for research and community use, as well as smaller distilled models for specific hardware or use cases.

Notable open-weight releases cover the V3 line (V3, V3.1, V3.2, etc.), R1 line (R1, R1-0528), and distilled versions optimized for size and inference speed.

The six distilled models include options based on Qwen and Llama architectures, ranging from 1.5B to 70B parameters.

Open-weight models can be run locally and are best suited for researchers, developers, and organizations wanting to host their own DeepSeek instances.

Model weights, architectures, and documentation are maintained on major model hubs and in DeepSeek’s own release notes.

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DeepSeek Open-Weight Models And Their Main Variants.

Model Name	Release Family	Parameter Scale	Main Use Case	Special Notes
DeepSeek-V3, V3.1, V3.2	V3 flagship	Large-scale	Local deployment, research	Full weights, context up to 128K tokens
DeepSeek-R1, R1-0528	R1 reasoning	Large-scale	Reasoning, chain-of-thought, open licensing	Open source, MIT license
Distilled models (Qwen, Llama)	R1 distilled	1.5B to 70B	Fast inference, edge, mobile, specific HW	Six models, various architectures

Legacy and preview models such as DeepSeek-V2.5, DeepSeek-V3.1-Terminus, and DeepSeek-R1-Lite-Preview are still referenced in documentation but may not be available for new API use.

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Access Requirements Are API-Key Driven For Hosted Models And Open-Weight Downloads For Local Use.

Accessing DeepSeek API models requires registration on the DeepSeek platform and retrieval of an API key.

Requests use OpenAI-compatible conventions, with model selection determined by passing the appropriate model ID, such as deepseek-chat or deepseek-reasoner.

API base URLs are specified in documentation, and the /v1 route is used for compatibility rather than model versioning.

For open-weight models, downloads are provided through supported model hubs, with licensing and local deployment handled per release notes.

Special endpoints or evaluation models may require additional instructions, temporary credentials, or are limited to API-only use for a defined period.

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Summary Comparison: Model Choice Reflects Task, Version, And Operational Requirements.

DeepSeek’s API currently centers on two main model IDs, each mapped to a specific operational mode and the latest model family.

Capabilities and limits differ between chat and reasoning endpoints, with unique features and beta tools staged to each.

Open-weight models extend the ecosystem for local use and research, while access requirements balance security for hosted models with flexibility for open deployments.

Choosing the right DeepSeek model means aligning operational goals, API needs, and hardware constraints with the available version and endpoint.

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