DeepSeekDeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 is a chat model developed by DeepSeek, superseding a preview version. It is designed with enhanced agentic capabilities, particularly for production environments, and incorporates a DSpark speculative decoding module.
Input
Output
Context
1M
Max Output
384K
Parameters
1650.5B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Resources & Links
Estimated GPU requirements for DeepSeek V4 Pro 0813
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 V4 Pro 0813
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
DeepSeek V4 Pro 0813 advertises a context window of 1,048,576 tokens, with a maximum output of 384,000 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 V4 Pro 0813 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 V4 Pro 0813 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 V4 Pro 0813 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 V4 Pro 0813 is shown as not available rather than estimated.
Not on the managed pool today; DeepSeek V4 Pro 0813 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 1.59 TB at INT8 for the weights and a default context, from the catalog's 1650.5B 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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