--- title: "Model-Fingerprint vs deepeval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cnut1648-model-fingerprint-vs-confident-ai-deepeval" tools: ["cnut1648-model-fingerprint", "confident-ai-deepeval"] --- # Model-Fingerprint vs deepeval *GraphCanon updated Aug 5, 2026* ## Verdict Pick Model-Fingerprint if model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0; pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. [Model-Fingerprint](https://github.com/cnut1648/Model-Fingerprint) reports 52 GitHub stars, 8 forks, and 5 open issues, last pushed Jul 11, 2024. [deepeval](https://deepeval.com) has 17k stars, 1.7k forks, and 404 open issues, last pushed Jul 27, 2026. Figures are from public GitHub metadata via [Model-Fingerprint's repository](https://github.com/cnut1648/Model-Fingerprint) and [deepeval's repository](https://github.com/confident-ai/deepeval). | | [Model-Fingerprint](/tools/cnut1648-model-fingerprint.md) | [deepeval](/tools/confident-ai-deepeval.md) | | --- | --- | --- | | Tagline | Fingerprint large language models | LLM Evaluation Framework. | | Stars | 52 | 17,226 | | Forks | 8 | 1,736 | | Open issues | 5 | 404 | | Language | Python | Python | | Adopt for | Model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0. | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 License | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Model-Fingerprint](/tools/cnut1648-model-fingerprint.md) | [deepeval](/tools/confident-ai-deepeval.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 754d | 1d | | Open issues (now) | 5 | 404 | | Owner type | User | Organization | | Full report | [trust report](/tools/cnut1648-model-fingerprint/trust.md) | [trust report](/tools/confident-ai-deepeval/trust.md) | ## Shared compatibility - **Python**: [Model-Fingerprint](/tools/cnut1648-model-fingerprint.md) - Python runtime; [deepeval](/tools/confident-ai-deepeval.md) - Python runtime ## Decision facts: Model-Fingerprint - **Adopt for:** Model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0. ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Choose when ### Choose Model-Fingerprint if… - License: Model-Fingerprint is MIT, deepeval is Apache-2.0. - Tags unique to Model-Fingerprint: fingerprinting, large language models, pytorch. - Use Model-Fingerprint when you need to fingerprint large language models for evaluation or observability purposes, especially in research contexts involving CUDA 11.3 and PyTorch 2.0 environments. ### Choose deepeval if… - License: deepeval is Apache-2.0, Model-Fingerprint is MIT. - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: evaluation, llm-evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ## When NOT to use Model-Fingerprint - Do not use Model-Fingerprint if your development environment does not support CUDA 11.3 and PyTorch 2.0, as it may lead to incompatibility issues. - Avoid this toolset if you need a solution that supports multiple versions of CUDA or Pytorch for flexibility across different hardware configurations without modification. ## When NOT to use deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## Common questions ### What is the difference between Model-Fingerprint and deepeval? Model-Fingerprint: Fingerprint large language models. deepeval: LLM Evaluation Framework.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Model-Fingerprint over deepeval? Choose Model-Fingerprint over deepeval when License: Model-Fingerprint is MIT, deepeval is Apache-2.0; Tags unique to Model-Fingerprint: fingerprinting, large language models, pytorch; Use Model-Fingerprint when you need to fingerprint large language models for evaluation or observability purposes, especially in research contexts involving CUDA 11.3 and PyTorch 2.0 environments. ### When should I choose deepeval over Model-Fingerprint? Choose deepeval over Model-Fingerprint when License: deepeval is Apache-2.0, Model-Fingerprint is MIT; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I avoid Model-Fingerprint? Do not use Model-Fingerprint if your development environment does not support CUDA 11.3 and PyTorch 2.0, as it may lead to incompatibility issues. Avoid this toolset if you need a solution that supports multiple versions of CUDA or Pytorch for flexibility across different hardware configurations without modification. ### When should I avoid deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### Is Model-Fingerprint or deepeval more popular on GitHub? deepeval has more GitHub stars (17,226 vs 52). Stars measure visibility, not whether either tool fits your constraints. ### Are Model-Fingerprint and deepeval open source? Yes - both are open-source projects on GitHub (Model-Fingerprint: MIT, deepeval: Apache-2.0). ### Where can I find alternatives to Model-Fingerprint or deepeval? GraphCanon lists graph-backed alternatives at [Model-Fingerprint alternatives](/tools/cnut1648-model-fingerprint/alternatives) and [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) ([Model-Fingerprint markdown twin](/tools/cnut1648-model-fingerprint/alternatives.md), [deepeval markdown twin](/tools/confident-ai-deepeval/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/cnut1648-model-fingerprint-vs-confident-ai-deepeval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Model-Fingerprint or deepeval? Model-Fingerprint: Dormant. deepeval: Very 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 Model-Fingerprint and deepeval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Model-Fingerprint trust report](/tools/cnut1648-model-fingerprint/trust); [deepeval trust report](/tools/confident-ai-deepeval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=cnut1648-model-fingerprint`](/api/graphcanon/graph?tool=cnut1648-model-fingerprint) - 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/_