DeepSeek open-weight models: V4.1 Flash, V4, V3.2, R1, and local deployment

DeepSeek's open-weight catalog should be read separately from its hosted API. A downloadable model such as DeepSeek-V4.1-Flash is a fixed checkpoint that can be self-hosted, while an API name such as deepseek-flash is a service route that DeepSeek can update or redirect over time.

This page maps the major open-weight families that remain useful in 2026 and explains the deployment implications of choosing a very large frontier checkpoint rather than the hosted API.

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V4.1 FLASH IS THE CURRENT OPEN-WEIGHT FRONTIER RELEASE

DeepSeek-V4.1-Flash is published as an official downloadable model and carries an MIT license. The official model card identifies it as an image-text-to-text model and reports a model size of roughly 763 billion parameters. It can be served through modern inference stacks such as Transformers and vLLM when the required hardware and software support are available.

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V4, V3.2, AND R1 REMAIN DISTINCT DOWNLOADABLE FAMILIES

The V4 family remains relevant for studying the architecture immediately preceding V4.1. DeepSeek-V3.2 remains an official MIT-licensed text-generation model, while DeepSeek-R1 remains an MIT-licensed reasoning family with both the full model and distilled variants derived from Qwen and Llama bases.

These releases do not disappear merely because the hosted DeepSeek API moves to a newer route. Their value shifts toward reproducible research, self-hosting, fine-tuning, benchmarking and third-party serving.

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Family	Primary role today	Modality	Licensing / deployment note
V4.1 Flash	Current open-weight frontier	Text + image	Official MIT-licensed checkpoint
V4	Previous frontier generation	Primarily text in V4 Flash release	Still downloadable for local serving
V3.2	Older general / reasoning-capable generation	Text	MIT-licensed official weights
R1	Reasoning-focused family	Text	MIT for main repository/weights; distills inherit base-model considerations

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SELF-HOSTING CHANGES THE ECONOMICS FROM TOKEN PRICING TO INFRASTRUCTURE

Downloading weights removes per-token billing from DeepSeek itself, but it does not make inference free. Hardware acquisition or rental, memory capacity, storage, power, orchestration, quantization, monitoring and engineering become the cost base.

For V4.1 Flash, a simple Data Studios lower-bound memory calculation illustrates the scale: 763 billion parameters at one byte per parameter is about 763 GB of raw weight storage, while two bytes per parameter is about 1.526 TB. Runtime overhead, KV cache, activations and parallelism requirements sit on top of that figure, so actual serving hardware needs are higher.

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DISTILLED R1 MODELS SERVE A DIFFERENT HARDWARE SEGMENT

DeepSeek-R1 also produced distilled Qwen- and Llama-based variants. Those models are materially smaller than the full frontier checkpoints and are therefore the more realistic local route for teams that want DeepSeek-style reasoning behavior without a multi-GPU frontier-scale cluster.

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OPEN WEIGHTS DO NOT GUARANTEE API FEATURE PARITY

A local checkpoint does not automatically reproduce every hosted product feature. Tool calling conventions, Responses API behavior, server-side caching, managed multimodal preprocessing, rate isolation and compatibility layers can depend on DeepSeek's hosted service or on the third-party inference stack used locally.

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DATA STUDIOS DEPLOYMENT MAP: HOSTED, FRONTIER SELF-HOSTED, OR DISTILLED

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Deployment path	Best fit	Main trade-off
DeepSeek hosted API	Teams prioritizing speed of deployment	Vendor routing and token billing
V4.1/V4 self-hosted	Large infrastructure teams needing model control	Very high memory and operational requirements
V3.2/R1 full models	Research and specialized self-hosting	Still frontier-scale infrastructure
R1 distilled models	Smaller local environments	Lower scale but not identical to full R1/V4 behavior

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THE RIGHT DEEPSEEK MODEL DEPENDS FIRST ON WHERE IT WILL RUN

For hosted use, the API identifier and live routing policy are the primary constraints. For local use, model size, license, inference framework support and memory footprint dominate. Treating those two choices as one model-selection problem creates avoidable confusion.

For the overall DeepSeek model map, see https://www.datastudios.org/post/deepseek-ai-models-available-full-lineup-capabilities-and-positioning-for-late-2025-2026. For current hosted API identifiers, see https://www.datastudios.org/post/deepseek-available-models-supported-api-models-version-differences-capabilities-comparison-and-a.

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