--- title: "whichllm vs anubis-oss" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/andyyyy64-whichllm-vs-uncsoft-anubis-oss" tools: ["andyyyy64-whichllm", "uncsoft-anubis-oss"] --- # whichllm vs anubis-oss *GraphCanon updated Sep 20, 2026* ## Verdict Pick whichllm if whichllm is designed to help users identify and benchmark local large language models that perform well on their specific hardware configuration via real-time benchmarks; pick anubis-oss if anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. [whichllm](https://github.com/Andyyyy64/whichllm) reports 6.7k GitHub stars, 368 forks, and 13 open issues, last pushed Sep 19, 2026. [anubis-oss](https://devpadapp.com/leaderboard.html) has 207 stars, 15 forks, and 1 open issues, last pushed Sep 5, 2026. Figures are from public GitHub metadata via [whichllm's repository](https://github.com/Andyyyy64/whichllm) and [anubis-oss's repository](https://github.com/uncSoft/anubis-oss). | | [whichllm](/tools/andyyyy64-whichllm.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Tagline | Command-line tool to find and benchmark local LLM performance | Local LLM Testing & Benchmarking for Apple Silicon | | Stars | 6,666 | 207 | | Forks | 368 | 15 | | Open issues | 13 | 1 | | Language | Python | Swift | | Adopt for | whichllm is designed to help users identify and benchmark local large language models that perform well on their specific hardware configuration via real-time benchmarks. | Anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. | | Categories | Evaluation & Observability, Inference & Serving | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [whichllm](/tools/andyyyy64-whichllm.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 15d | | Open issues (now) | 13 | 1 | | Stars delta | +441 (30d) | +9 (30d) | | Open issues delta | -9 (30d) | -3 (30d) | | Full report | [trust report](/tools/andyyyy64-whichllm/trust.md) | [trust report](/tools/uncsoft-anubis-oss/trust.md) | ## Decision facts: whichllm - **Adopt for:** whichllm is designed to help users identify and benchmark local large language models that perform well on their specific hardware configuration via real-time benchmarks. ## Decision facts: anubis-oss - **Pricing:** freemium - The tool is free and open-source with no monetary costs for usage or distribution. - **Requirements:** Min 8 GB RAM - **Adopt for:** Anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. - **License detail:** GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. ## Choose when ### Choose whichllm if… - whichllm is primarily Python; anubis-oss is Swift. - License: whichllm is MIT, anubis-oss is GPL-3.0. - Tags unique to whichllm: ai, benchmarks, cli, huggingface. - When you need to quickly discover which locally available LLM runs most efficiently on your Apple Silicon or GPU infrastructure using Python scripts ### Choose anubis-oss if… - anubis-oss is primarily Swift; whichllm is Python. - License: anubis-oss is GPL-3.0, whichllm is MIT. - Pricing: The tool is free and open-source with no monetary costs for usage or distribution.. - Requirements: Min 8 GB RAM. - Tags unique to anubis-oss: benchmarking, gpu, macos, mlx. - When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ## When NOT to use whichllm - In scenarios where extensive customization of benchmarking criteria beyond what this tool offers is required - When you are working in a non-Python environment and prefer not to introduce Python scripts into your workflow ## When NOT to use anubis-oss - If your development does not involve Apple Silicon or macOS environments as Anubis-oss is tightly integrated with these platforms. - When preferring a language other than Swift, since Anubis-oss depends on this for its operations. ## Common questions ### What is the difference between whichllm and anubis-oss? whichllm: Command-line tool to find and benchmark local LLM performance. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose whichllm over anubis-oss? Choose whichllm over anubis-oss when whichllm is primarily Python; anubis-oss is Swift; License: whichllm is MIT, anubis-oss is GPL-3.0; Tags unique to whichllm: ai, benchmarks, cli, huggingface; When you need to quickly discover which locally available LLM runs most efficiently on your Apple Silicon or GPU infrastructure using Python scripts. ### When should I choose anubis-oss over whichllm? Choose anubis-oss over whichllm when anubis-oss is primarily Swift; whichllm is Python; License: anubis-oss is GPL-3.0, whichllm is MIT; Pricing: The tool is free and open-source with no monetary costs for usage or distribution.; Requirements: Min 8 GB RAM; Tags unique to anubis-oss: benchmarking, gpu, macos, mlx; When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ### When should I avoid whichllm? In scenarios where extensive customization of benchmarking criteria beyond what this tool offers is required When you are working in a non-Python environment and prefer not to introduce Python scripts into your workflow ### When should I avoid anubis-oss? If your development does not involve Apple Silicon or macOS environments as Anubis-oss is tightly integrated with these platforms. When preferring a language other than Swift, since Anubis-oss depends on this for its operations. ### Is whichllm or anubis-oss more popular on GitHub? whichllm has more GitHub stars (6,666 vs 207). Stars measure visibility, not whether either tool fits your constraints. ### Are whichllm and anubis-oss open source? Yes - both are open-source projects on GitHub (whichllm: MIT, anubis-oss: GPL-3.0). ### Where can I find alternatives to whichllm or anubis-oss? GraphCanon lists graph-backed alternatives at [whichllm alternatives](/tools/andyyyy64-whichllm/alternatives) and [anubis-oss alternatives](/tools/uncsoft-anubis-oss/alternatives) ([whichllm markdown twin](/tools/andyyyy64-whichllm/alternatives.md), [anubis-oss markdown twin](/tools/uncsoft-anubis-oss/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/andyyyy64-whichllm-vs-uncsoft-anubis-oss.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, whichllm or anubis-oss? whichllm: Very active. anubis-oss: Active. 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 whichllm and anubis-oss? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [whichllm trust report](/tools/andyyyy64-whichllm/trust); [anubis-oss trust report](/tools/uncsoft-anubis-oss/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=andyyyy64-whichllm`](/api/graphcanon/graph?tool=andyyyy64-whichllm) - 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/_