DeepSeekDeepSeek V3.1
DeepSeek-V3.1, developed by DeepSeek, is a hybrid chat model that excels at tasks such as code generation, function calling, and math, thanks to its improved thinking mode and smarter tool calling capabilities. Notably, it features a long context window of 131,072 tokens, allowing it to process extensive inputs.
Input
Output
Context
131K
Max Output
-
Parameters
684.5B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Served variants
The same model in other encodings or serving configurations. Requesting deepseek-v3-1 lets routing pick among them; the ids below pin one build.
deepseek-v3-1:fp4quantized
Benchmarks
Resources & Links
Estimated GPU requirements for DeepSeek V3.1
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 V3.1
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
DeepSeek V3.1 advertises a context window of 131,072 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 V3.1 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 V3.1 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 V3.1 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 V3.1 is shown as not available rather than estimated.
Yes. DeepSeek V3.1 is served on the managed pool through the OpenAI-compatible endpoint as deepseek-v3-1, 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 672.0 GB at INT8 for the weights and a default context, from the catalog's 684.5B 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: Nov 6, 2026
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