--- title: "DeepSeek-V3 vs search_with_lepton" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-v3-vs-leptonai-search-with-lepton" tools: ["deepseek-ai-deepseek-v3", "leptonai-search-with-lepton"] --- # DeepSeek-V3 vs search_with_lepton *GraphCanon updated Jul 12, 2026* ## Verdict Pick DeepSeek-V3 when deepSeek-V3 is primarily Python; search_with_lepton is TypeScript; pick search_with_lepton when search_with_lepton is primarily TypeScript; DeepSeek-V3 is Python. [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) reports 104k GitHub stars, 17k forks, and 248 open issues, last pushed Aug 28, 2025. [search_with_lepton](https://search.lepton.run) has 8.1k stars, 1.0k forks, and 47 open issues, last pushed Dec 2, 2025. Figures are from public GitHub metadata via [DeepSeek-V3's repository](https://github.com/deepseek-ai/DeepSeek-V3) and [search_with_lepton's repository](https://github.com/leptonai/search_with_lepton). | | [DeepSeek-V3](/tools/deepseek-ai-deepseek-v3.md) | [search_with_lepton](/tools/leptonai-search-with-lepton.md) | | --- | --- | --- | | Tagline | Repository lacking description with unspecified content related to AI development. | Building a quick conversation-based search demo with Lepton AI. | | Stars | 103,904 | 8,089 | | Forks | 16,730 | 1,003 | | Open issues | 248 | 47 | | Language | Python | TypeScript | | Adopt for | DeepSeek-V3 is a Python-based AI development tool, with documentation focused solely on licensing terms for both its codebase and models. It's unclear from the available information what specific features or capabilities | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Developer Tools, Inference & Serving | Developer Tools, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-V3](/tools/deepseek-ai-deepseek-v3.md) | [search_with_lepton](/tools/leptonai-search-with-lepton.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Archived (8%) | | Days since push | 318d | 221d | | Archived on GitHub | No | Yes | | Open issues (now) | 248 | 47 | | Full report | [trust report](/tools/deepseek-ai-deepseek-v3/trust.md) | [trust report](/tools/leptonai-search-with-lepton/trust.md) | ## Decision facts: DeepSeek-V3 - **Adopt for:** DeepSeek-V3 is a Python-based AI development tool, with documentation focused solely on licensing terms for both its codebase and models. It's unclear from the available information what specific features or capabilities ## Choose when ### Choose DeepSeek-V3 if… - DeepSeek-V3 is primarily Python; search_with_lepton is TypeScript. - License: DeepSeek-V3 is MIT, search_with_lepton is Apache-2.0. - Tags unique to DeepSeek-V3: commercial use, mit license, python. - - When you need an AI model that allows for commercial usage as DeepSeek-V3 explicitly supports this based on licensing provided. ### Choose search_with_lepton if… - search_with_lepton is primarily TypeScript; DeepSeek-V3 is Python. - License: search_with_lepton is Apache-2.0, DeepSeek-V3 is MIT. - Tags unique to search_with_lepton: ai, ai-applications, leptonai, llm. - Also covers LLM Frameworks. ## When NOT to use DeepSeek-V3 - - If detailed documentation and clear feature descriptions are crucial as the repository lacks descriptive content. - - When you require open-source model details or functionalities other than those related solely to licensing terms. ## When NOT to use search_with_lepton - search_with_lepton is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between DeepSeek-V3 and search_with_lepton? DeepSeek-V3: Repository lacking description with unspecified content related to AI development.. search_with_lepton: Building a quick conversation-based search demo with Lepton AI.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-V3 over search_with_lepton? Choose DeepSeek-V3 over search_with_lepton when DeepSeek-V3 is primarily Python; search_with_lepton is TypeScript; License: DeepSeek-V3 is MIT, search_with_lepton is Apache-2.0; Tags unique to DeepSeek-V3: commercial use, mit license, python; - When you need an AI model that allows for commercial usage as DeepSeek-V3 explicitly supports this based on licensing provided. ### When should I choose search_with_lepton over DeepSeek-V3? Choose search_with_lepton over DeepSeek-V3 when search_with_lepton is primarily TypeScript; DeepSeek-V3 is Python; License: search_with_lepton is Apache-2.0, DeepSeek-V3 is MIT; Tags unique to search_with_lepton: ai, ai-applications, leptonai, llm; Also covers LLM Frameworks. ### When should I avoid DeepSeek-V3? - If detailed documentation and clear feature descriptions are crucial as the repository lacks descriptive content. - When you require open-source model details or functionalities other than those related solely to licensing terms. ### When should I avoid search_with_lepton? search_with_lepton is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is DeepSeek-V3 or search_with_lepton more popular on GitHub? DeepSeek-V3 has more GitHub stars (103,904 vs 8,089). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-V3 and search_with_lepton open source? Yes - both are open-source projects on GitHub (DeepSeek-V3: MIT, search_with_lepton: Apache-2.0). ### Where can I find alternatives to DeepSeek-V3 or search_with_lepton? GraphCanon lists graph-backed alternatives at [DeepSeek-V3 alternatives](/tools/deepseek-ai-deepseek-v3/alternatives) and [search_with_lepton alternatives](/tools/leptonai-search-with-lepton/alternatives) ([DeepSeek-V3 markdown twin](/tools/deepseek-ai-deepseek-v3/alternatives.md), [search_with_lepton markdown twin](/tools/leptonai-search-with-lepton/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/deepseek-ai-deepseek-v3-vs-leptonai-search-with-lepton.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-V3 or search_with_lepton? DeepSeek-V3: Slowing. search_with_lepton: Archived. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for DeepSeek-V3 and search_with_lepton? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-V3 trust report](/tools/deepseek-ai-deepseek-v3/trust); [search_with_lepton trust report](/tools/leptonai-search-with-lepton/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepseek-ai-deepseek-v3`](/api/graphcanon/graph?tool=deepseek-ai-deepseek-v3) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_