DeepSeekDeepSeek Prover V1.5 SFT
DeepSeek develops Deepseek Prover V 1.5 SFT, a chat model exceling at reasoning and text generation, particularly in formal mathematical languages. It is notably trained using reinforcement learning from proof assistant feedback and employs a variant of Monte-Carlo tree search, achieving state-of-the-art results on the miniF2F and ProofNet benchmarks with scores of 63.5% and 25.3%, respectively.
Input
Output
Context
4K
Max Output
2K
Parameters
6.9B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for DeepSeek Prover V1.5 SFT
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 Prover V1.5 SFT
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
DeepSeek Prover V1.5 SFT advertises a context window of 4,096 tokens, with a maximum output of 2,048 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 Prover V1.5 SFT is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
DeepSeek Prover V1.5 SFT 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.
No published benchmark result for DeepSeek Prover V1.5 SFT is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; DeepSeek Prover V1.5 SFT 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 7.1 GB at INT8 for the weights and a default context, from the catalog's 6.9B 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 14, 2026
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