Comparison
Model-Fingerprint vs deepeval
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.
Markdown twin · Model-Fingerprint alternatives · deepeval alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Model-Fingerprint | deepeval |
|---|---|---|
| Maintenance | Dormant (754d since push) As of 2w · github_public_v1 | Very active (1d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Model-Fingerprint
- Fingerprint large language models
- deepeval
- LLM Evaluation Framework.
Stars
- Model-Fingerprint
- 52
- deepeval
- 17k
Forks
- Model-Fingerprint
- 8
- deepeval
- 1.7k
Open issues
- Model-Fingerprint
- 5
- deepeval
- 404
Language
- Model-Fingerprint
- Python
- deepeval
- Python
Adopt for
- Model-Fingerprint
- Model-Fingerprint is a toolset for creating instructional fingerprints of large language models using CUDA 11.3 and PyTorch 2.0.
- deepeval
- Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
Persona
- Model-Fingerprint
- -
- deepeval
- -
Runtime
- Model-Fingerprint
- -
- deepeval
- -
License
- Model-Fingerprint
- MIT
- deepeval
- Apache-2.0 License
Last pushed
- Model-Fingerprint
- Jul 11, 2024
- deepeval
- Jul 27, 2026
Categories
- Model-Fingerprint
- Evaluation & Observability
- deepeval
- Evaluation & Observability
Trust and health
Maintenance
- Model-Fingerprint
- Dormant (18%)
- deepeval
- Very active (96%)
Days since push
- Model-Fingerprint
- 754d
- deepeval
- 1d
Open issues (now)
- Model-Fingerprint
- 5
- deepeval
- 404
Owner type
- Model-Fingerprint
- User
- deepeval
- Organization
OSV dependency advisories
- Model-Fingerprint
- No published findings from this source as of 2026-07-11
- deepeval
- No lockfile (source not queried)
Full report
- Model-Fingerprint
- Trust report
- deepeval
- Trust report
Shared compatibility
- Python · Model-Fingerprint: Python runtime · deepeval: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (cnut1648/Model-Fingerprint) · observed Aug 5, 2026
- GitHub forks (cnut1648/Model-Fingerprint) · observed Aug 5, 2026
- Last push (cnut1648/Model-Fingerprint) · observed Jul 11, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (confident-ai/deepeval) · observed Jul 28, 2026
- GitHub forks (confident-ai/deepeval) · observed Jul 28, 2026
- Last push (confident-ai/deepeval) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Model-Fingerprint 52 · deepeval 17k (synced Aug 5, 2026).
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 and deepeval alternatives (Model-Fingerprint markdown twin, deepeval markdown twin), 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 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; deepeval trust report.