DeepSeekDeepSeek V3.2
DeepSeek-V3.2, developed by DeepSeek, is a chat model that excels at reasoning, code generation, and text generation, thanks to its efficient attention mechanism, DeepSeek Sparse Attention (DSA), which reduces computational complexity while preserving performance. With a context window of 163,840 tokens, this model is well-suited for complex, long-context scenarios.
Input
Output
Context
164K
Max Output
-
Parameters
685.4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Served variants
The same model in other encodings or serving configurations. Requesting deepseek-v3-2 lets routing pick among them; the ids below pin one build.
deepseek-v3-2:fp4quantized
Benchmarks
Resources & Links
Estimated GPU requirements for DeepSeek V3.2
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
More models by DeepSeek
The creator's other models in the catalog, with their context, size and license where published.
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Frequently asked questions about DeepSeek V3.2
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
DeepSeek V3.2 advertises a context window of 163,840 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. DeepSeek V3.2 is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
DeepSeek V3.2 accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
DeepSeek V3.2 has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured DeepSeek V3.2 is shown as not available rather than estimated.
Yes. DeepSeek V3.2 is served on the managed pool through the OpenAI-compatible endpoint as deepseek-v3-2, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 672.9 GB at INT8 for the weights and a default context, from the catalog's 685.4B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 6, 2026
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