What is DeepSeek-Prover-V1.5-SFT?
DeepSeek-Prover-V1.5-SFT is a 7-billion-parameter causal language model trained specifically for automated theorem proving. Its purpose is to generate or complete formal mathematical proofs written in Lean 4, a programming language and proof assistant used to express mathematical statements and verify that proposed proofs are logically valid.
Unlike a general conversational model, it is not primarily designed to answer questions in natural language. Its main output is text containing Lean 4 code. That code is only useful when it passes Lean's checker, so the model's apparent fluency is less important than whether its generated proof compiles against the intended theorem, definitions, and imported Mathlib libraries.
The SFT name refers to supervised fine-tuning. Within the DeepSeek-Prover-V1.5 training pipeline, this checkpoint represents the supervised-learning stage and is positioned alongside the Base and reinforcement-learning variants. The supplied research describes it as an older, legacy or superseded checkpoint rather than a current general-purpose DeepSeek offering.
Purpose and positioning
DeepSeek-Prover-V1.5-SFT was initialized from a 7B base model derived from DeepSeekMath-Base and then specialized for formal mathematical language. Its supervised fine-tuning used an enhanced Lean 4 theorem-proving dataset containing proof sequences and verifier-related information. The training process supported both chain-of-thought and non-chain-of-thought proof-generation modes.
In practical terms, the model is a candidate generator for formal proof systems. A typical workflow asks it to complete a theorem or produce a proof attempt, then sends the result to Lean for verification. If the attempt fails, an application can request additional samples, repair the code, or explore several alternatives through a separate search procedure.
This positioning matters when comparing it with ordinary coding or chat models. DeepSeek-Prover-V1.5-SFT is narrower, but its output is aligned with a formal verifier. It is therefore most relevant to researchers and developers building theorem-proving systems, not to users looking for a conversational assistant or a broad software-development tool.
Verified specifications
| Specification | Details |
|---|---|
| Provider | DeepSeek |
| Model family | DeepSeek-Prover |
| Release date | August 15, 2024 |
| Model size | Approximately 7 billion parameters |
| Context length | 4,096 tokens |
| Primary input | Text and Lean 4 source code |
| Primary output | Text and Lean 4 proof code |
| Availability | Downloadable open weights through Hugging Face |
| First-party hosted API | No documented first-party hosted API is supplied in the research |
| Maximum output length | Not identified in the supplied research |
| Pricing | Not applicable as a self-hosted checkpoint; infrastructure costs depend on the user's deployment |
| License | DeepSeek model license; repository code is identified as MIT-licensed |
The 4,096-token context window limits how much theorem context, imported material, previous proof state, and requested completion can be provided in one generation. The supplied research does not identify a separate maximum-output-token setting, so no fixed output limit should be assumed beyond the model configuration and available context.
Benchmark performance
In the reported DeepSeek-Prover-V1.5 evaluation, the SFT checkpoint achieved a 57.4% pass rate on the miniF2F test set under the stated large-sample evaluation and 22.9% on ProofNet in the comparison table. The same research reports 17.9% overall on ProofNet with a 128-sample budget in the stated single-pass setting.
These figures are benchmark results from a particular evaluation setup, not guarantees for every theorem or deployment. Formal-proof performance can change with prompting, the number of sampled candidates, theorem preprocessing, available imports, verifier configuration, and search strategy. The results should also not be confused with a general reasoning score.
The reported SFT results are below those of the later DeepSeek-Prover-V1.5-RL model in several settings. Nevertheless, the SFT checkpoint remains useful when researchers want the supervised stage as a standalone model, need open weights for reproducible experiments, or want to add their own verifier-guided search and refinement methods.
How the proof workflow works
A user normally supplies a theorem statement, its surrounding Lean context, and possibly a partial proof. The model then generates a candidate completion in Lean 4 syntax. That candidate is passed to Lean and Mathlib4, where the proof assistant checks types, tactics, referenced definitions, and logical validity.
A failed candidate does not necessarily mean that the theorem is beyond the model. It may contain a syntax error, use an unavailable lemma, misunderstand the local proof state, or select a mathematically plausible but formally incompatible tactic. For that reason, practical systems often sample multiple candidates and feed verifier errors back into a repair or search loop.
The official repository demonstrates inference with Hugging Face Transformers and provides a separate proof-verification workflow involving Lean 4 and Mathlib. The checkpoint is approximately 13.8 GB in the Hugging Face repository before quantization or other deployment optimizations. The documented setup targets Linux and Python 3.10.
Capabilities and limitations
Reasoning and coding capabilities
The model's reasoning capability is specialized rather than broad. It is trained to map formal mathematical goals to Lean proof terms or tactic sequences, which makes it relevant to theorem proving and formal mathematics research. Its coding output is also specialized: it produces Lean 4 source code rather than general application code.
The strongest practical property is verifiability. A generated proof can be checked mechanically, allowing an application to reject invalid output instead of treating plausible-looking text as correct. This makes the model suitable for systems where proof correctness is more important than conversational breadth.
Important limitations
- The 4,096-token context window is relatively short for large formal developments or theorem statements with extensive local context.
- The supplied research does not document a fixed maximum-output-token value.
- Successful generation generally requires Lean verification and may benefit from repeated sampling, repair, or tree search.
- The model is not a general-purpose conversational assistant and is not presented as a multimodal system.
- It has no documented official web-search, tool-calling, or function-calling capability. Lean and Mathlib are external verification infrastructure, not native model tools.
- It does not provide native image, audio, video, or other non-text output.
- There is no documented first-party hosted API or usage-based price for the checkpoint.
- Its age and superseded status may make a later theorem-proving model more suitable for new projects if higher benchmark performance or newer tooling is the priority.
Cost, speed, and deployment trade-offs
Because DeepSeek-Prover-V1.5-SFT is distributed as downloadable weights, there is no provider API price to compare with hosted token rates. The financial trade-off is instead between local infrastructure and control. Self-hosting can support private data, repeatable experiments, and unrestricted integration with a Lean verifier, but the checkpoint's approximately 13.8 GB repository size and the requirements of model inference and proof checking create operational costs.
The research gives an editorial speed score of 4 and cost score of 9, but these are comparative editorial assessments rather than DeepSeek-published specifications. They should not be interpreted as measured latency or a guaranteed cost per proof. Actual speed depends on hardware, quantization, batch size, sampling count, and the time spent compiling and checking proofs. A workflow that generates many candidates may improve pass rates while increasing total computation.
Compared with a hosted general-purpose model, this checkpoint offers more direct control over deployment and a specialized proof-generation objective, but less convenience. Users must install and maintain the model, Lean 4, Mathlib4, and the surrounding inference or search code themselves.
When to choose DeepSeek-Prover-V1.5-SFT
Choose DeepSeek-Prover-V1.5-SFT when the central task is Lean 4 formal proof generation and you need downloadable weights for local experimentation. It is a reasonable fit for:
- Research on automated theorem proving and formal mathematics.
- Lean 4 proof completion and candidate generation.
- Verifier-guided generation, proof repair, and tree-search experiments.
- Reproducible studies of the supervised-learning stage of the DeepSeek-Prover-V1.5 pipeline.
- Private or offline workflows where a hosted API is unsuitable.
A later or reinforcement-learning-refined theorem-proving model may be more appropriate when benchmark performance is the main objective. A general coding or conversational model may be preferable for software development, natural-language explanation, broad mathematical discussion, or long-context work. A hosted service may also be a better option when minimizing infrastructure maintenance matters more than controlling the full local pipeline.
Bottom line
DeepSeek-Prover-V1.5-SFT is best understood as a specialized, open-weight Lean 4 proof generator rather than a general AI assistant. Its value comes from combining supervised proof-generation training with mechanical verification: the model proposes formal code, and Lean determines whether that code is valid. The 7B checkpoint, 4,096-token context, open-weight distribution, and reported miniF2F and ProofNet results make it a useful research artifact, while its lack of a hosted API, limited context, external tooling requirements, and superseded position limit its appeal for turnkey production use.