DeepSeekDeepSeek V3.2 Speciale
DeepSeek develops the DeepSeek V 3.2 Speciale, a chat model exceling at code generation, function calling, and reasoning, with notable strengths in coding and mathematical tasks. Its technical architecture features DeepSeek Sparse Attention, an efficient attention mechanism optimized for long-context scenarios, supporting a context window of 163,840 tokens.
Input
Output
Context
164K
Max Output
64K
Parameters
685.4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Resources & Links
Estimated GPU requirements for DeepSeek V3.2 Speciale
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.
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Frequently asked questions about DeepSeek V3.2 Speciale
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
DeepSeek V3.2 Speciale advertises a context window of 163,840 tokens, with a maximum output of 64,000 tokens in a single response. 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 Speciale 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 Speciale 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 Speciale 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 Speciale is shown as not available rather than estimated.
Not on the managed pool today; DeepSeek V3.2 Speciale is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 660.0 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: Oct 16, 2026
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