What is DeepSeek-Prover-V1.5-Base?
DeepSeek-Prover-V1.5-Base is a 7-billion-parameter open-weight language model developed by DeepSeek for formal theorem proving. Its main output is Lean 4 proof code: text written in the Lean proof-assistant language that can be compiled and checked against a formal theorem statement.
This makes the model different from a general-purpose chat model that may explain a proof in natural language. A natural-language explanation can sound convincing while containing a subtle mistake. A Lean proof must satisfy the rules of the Lean system and the imported mathematical libraries, including Mathlib, before it can be accepted as a valid formal proof.
The model is the base checkpoint in the DeepSeek-Prover-V1.5 release. The same project also produced supervised fine-tuned and reinforcement-learning variants. DeepSeek-Prover-V1.5-Base precedes those additional training stages, so it should be understood as a research foundation rather than the highest-performing version in that release.
Purpose and position in the DeepSeek-Prover lineup
DeepSeek-Prover-V1.5-Base was pretrained from DeepSeekMath-Base 7B and then specialized in formal mathematical languages. Its role is to provide a model that can generate complete or partial Lean 4 proofs from theorem statements and surrounding source code.
The broader DeepSeek-Prover-V1.5 system added techniques such as proof-assistant feedback and RMaxTS, a Monte Carlo tree-search approach for exploring alternative proof paths. Those techniques belong to the wider theorem-proving system and later post-training stages; they should not be interpreted as features of the Base checkpoint by itself.
In the current DeepSeek-Prover family, this checkpoint is an earlier-generation model. DeepSeek describes DeepSeek-Prover-V2-7B as being built upon DeepSeek-Prover-V1.5-Base and extending the context length to 32K tokens. That positioning makes V1.5-Base particularly relevant when reproducibility, access to the original research checkpoint, or custom experimentation matters more than using the newest available prover.
How it generates verifiable proofs
In a typical workflow, a user supplies a formal Lean theorem, its local definitions, and relevant surrounding code. The model then predicts Lean tokens that may complete the proof. The generated result is passed to Lean, which checks whether the proof type-checks. If it fails, a proof-search system can provide the error or try another candidate.
This separation between generation and verification is central to the model's usefulness. DeepSeek-Prover-V1.5-Base proposes proof code, but Lean and Mathlib determine whether that code is valid. The model therefore works best as one component in a loop that combines language-model generation, compilation, error handling, and possibly search over multiple candidates.
For beginners, the important distinction is that the model does not merely describe mathematics. It targets a formal language with strict syntax and semantics. For intermediate users, the practical implication is that model quality depends not only on the generated text but also on the exact Lean version, imported libraries, theorem environment, and proof-verification setup.
Reported benchmark results
In the DeepSeek-Prover-V1.5 project evaluation, the Base model achieved 42.2% on the miniF2F test benchmark and 13.2% on ProofNet under the reported evaluation setup. The miniF2F result for the Base checkpoint used three-shot prompting.
These are project-reported benchmark results, not guarantees for every theorem or deployment. Performance can vary with prompting, theorem formatting, available context, Lean and Mathlib versions, candidate sampling, and whether an external search or verification scheduler is used.
The Base checkpoint scored below the supervised fine-tuned and reinforcement-learning variants from the same release in the reported evaluations. That difference is important when choosing the model: V1.5-Base offers a useful open starting point, but it is not the strongest direct theorem-proving option in its own family according to the supplied project results.
Technical specifications and availability
| Specification | Details |
|---|---|
| Provider | DeepSeek |
| Model type | Open-weight language model specialized for formal theorem proving |
| Parameter count | 7 billion |
| Primary language and environment | Lean 4 with Mathlib |
| Context length | 4,096 tokens |
| Output type | Text, primarily Lean 4 proof code |
| Weights and format | Downloadable model weights in Safetensors format |
| Hosted API pricing | No official first-party token pricing identified |
| Managed batch service | No official managed batch service identified |
The 4,096-token context length is the context specified for this model in the supplied research. It limits how much theorem code, definitions, imports, previous proof state, and prompting information can be presented in one request. The research does not specify a separate maximum output-token limit, so no independent output ceiling should be assumed beyond the practical limits of the serving configuration and context window.
The model can be downloaded from its official Hugging Face repository and run with Transformers-based tooling. The project documentation targets Linux and Python 3.10 and describes Lean 4, a built Mathlib environment, vLLM examples, and a Lean verification scheduler. Exact hardware requirements are deployment-dependent and are not specified in the supplied material.
Modalities, tools, and API support
DeepSeek-Prover-V1.5-Base is a text-in, text-out model. Its output is text representing Lean code; it does not provide native image, audio, video, music, speech, or embedding output according to the supplied model data. It is not documented as a multimodal model.
The model itself is not documented as having built-in web search, function calling, or managed tool-use support. A local application can connect its output to Lean, Mathlib, a compiler, or a proof-search scheduler, but those are external components in the surrounding workflow rather than native hosted-model tools.
Streaming, first-party caching, and fine-tuning support are not verified in the supplied research. Researchers can work with the open weights and build custom inference or training pipelines, but that is different from receiving a provider-operated fine-tuning product or API feature.
Main strengths
- Focused specialization: The model is trained for formal mathematical language and Lean 4 proof generation rather than broad conversational tasks.
- Mechanical verification: Candidate proofs can be checked by Lean and Mathlib, giving users an objective validity test instead of relying only on natural-language plausibility.
- Open-weight access: Downloadable weights support local experimentation, reproducible research, custom serving, and further model development.
- Useful research baseline: As a base checkpoint, it can be used to study prompting, proof search, fine-tuning, and verification-aware generation.
- Clear integration target: Its intended environment is concrete: Lean 4, Mathlib, and a surrounding proof-verification workflow.
Limitations and trade-offs
- Not a general assistant: It is not intended for ordinary chat, broad knowledge work, web research, or general coding assistance.
- Earlier-generation checkpoint: It has been superseded in the DeepSeek-Prover line by newer releases, including DeepSeek-Prover-V2.
- Lower reported performance than later variants: The Base model performed below the SFT and RL versions of DeepSeek-Prover-V1.5 in the project's reported evaluations.
- Shorter context than newer related research: Its documented context length is 4,096 tokens, while the supplied DeepSeek-Prover-V2 information describes a 32K-token context.
- Operational complexity: Useful deployment requires model-serving infrastructure, Lean 4, Mathlib, and a mechanism for checking and possibly retrying generated proofs.
- No identified hosted pricing: Users must account for their own compute or hosting costs because no official first-party token pricing was identified.
These limitations create a direct capability-versus-convenience trade-off. A locally deployed open checkpoint offers control and customization, but it requires more engineering than a managed API. The model's narrow specialization can be an advantage for Lean research and a poor fit for applications that need broad language, tool, or multimodal capabilities.
Speed and cost considerations
No provider-published speed or per-token cost is available for this checkpoint in the supplied research. Its practical speed depends on the hardware, quantization or serving configuration, batch size, candidate-generation strategy, and the time required for Lean verification.
The 7B size is materially smaller than many large general-purpose models, which can make local experimentation more approachable, but the research does not establish a guaranteed latency or hardware requirement. Proof search can also multiply inference work because a system may generate and verify several alternative candidates before finding a valid proof.
Cost therefore needs to be evaluated at the workflow level rather than by model size alone. Compute is used for inference, while additional resources may be needed for Lean compilation, Mathlib, search, logging, and repeated failed attempts. An open-weight deployment may still be economical for sustained research, but it is not automatically cheaper for small or occasional workloads.
Best use cases
DeepSeek-Prover-V1.5-Base is a strong fit for:
- Research on automated theorem proving and formal mathematics.
- Lean 4 proof completion and candidate generation.
- Experiments with proof search, verification feedback, and tree-search methods.
- Building local theorem-proving agents around Lean and Mathlib.
- Fine-tuning or adapting an open model for a specialized formal-mathematics dataset.
- Studying how language models interact with compilers and formal proof assistants.
It is less appropriate for general-purpose chat, production applications that need a managed commercial API, multimodal workloads, web-enabled assistants, or software development tasks unrelated to formal proof generation.
When to choose DeepSeek-Prover-V1.5-Base
Choose this model when you specifically need an open-weight Lean 4 prover and want direct control over inference, verification, or further training. It is especially suitable when the research goal involves reproducing or extending the DeepSeek-Prover-V1.5 work, testing custom proof-search methods, or integrating generation into a local Lean pipeline.
Choose a later DeepSeek-Prover release instead when a newer checkpoint, a longer context, or stronger reported theorem-proving performance is more important than using the original Base model. The supplied research specifically identifies DeepSeek-Prover-V2-7B as a successor built upon this checkpoint with a 32K context, although detailed comparative benchmark data for that model is not provided here.
Choose a general-purpose language model instead when the main requirement is conversation, broad coding, multimodal input or output, web search, or managed API operations. DeepSeek-Prover-V1.5-Base's value comes from its formal-proof specialization, not from breadth of application coverage.