Comparison
mteb vs awesome-LLM-resources
Verdict
Pick mteb if mTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a.
Markdown twin · mteb alternatives · awesome-LLM-resources alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | mteb | awesome-LLM-resources |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1mo · github_public_v1 | Very active (2d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1mo · github_public_v1 | Not a fork · Personal account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- mteb
- State-of-the-art evaluation of embeddings across languages and modalities
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- mteb
- 3.4k
- awesome-LLM-resources
- 8.8k
Forks
- mteb
- 645
- awesome-LLM-resources
- 950
Open issues
- mteb
- 309
- awesome-LLM-resources
- 23
Language
- mteb
- Python
- awesome-LLM-resources
- -
Adopt for
- mteb
- MTEB is an evaluator for embedding models across languages and modalities under the Apache-2.0 license.
- awesome-LLM-resources
- awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a
Persona
- mteb
- -
- awesome-LLM-resources
- -
Runtime
- mteb
- -
- awesome-LLM-resources
- -
License
- mteb
- Apache-2.0
- awesome-LLM-resources
- Apache-2.0
Last pushed
- mteb
- Jul 22, 2026
- awesome-LLM-resources
- Aug 14, 2026
Categories
- mteb
- Evaluation & Observability
- awesome-LLM-resources
- AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- mteb
- 0d
- awesome-LLM-resources
- 2d
Open issues (now)
- mteb
- 309
- awesome-LLM-resources
- 23
Stars delta
- mteb
- Unknown
- awesome-LLM-resources
- +142 (30d)
Open issues delta
- mteb
- Unknown
- awesome-LLM-resources
- -13 (30d)
Owner type
- mteb
- Organization
- awesome-LLM-resources
- User
Full report
- mteb
- Trust report
- awesome-LLM-resources
- Trust report
Choose mteb if…
- Tags unique to mteb: benchmark, bitext-mining, clustering, embeddings.
- mteb ships Docker support for self-hosted deployment.
- You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts.
When NOT to use mteb
- Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope.
- You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system.
Choose awesome-LLM-resources if…
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models.
- Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks, Model Training.
- - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
When NOT to use awesome-LLM-resources
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
- - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (embeddings-benchmark/mteb) · observed Jul 22, 2026
- GitHub forks (embeddings-benchmark/mteb) · observed Jul 22, 2026
- Last push (embeddings-benchmark/mteb) · observed Jul 22, 2026
- License file (Apache-2.0) · observed Jul 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Aug 14, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 10, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mteb 3.4k · awesome-LLM-resources 8.8k (synced Jul 22, 2026).
Common questions
- What is the difference between mteb and awesome-LLM-resources?
- mteb: State-of-the-art evaluation of embeddings across languages and modalities. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose mteb over awesome-LLM-resources?
- Choose mteb over awesome-LLM-resources when Tags unique to mteb: benchmark, bitext-mining, clustering, embeddings; mteb ships Docker support for self-hosted deployment; You require benchmarking tools specifically designed for state-of-the-art embedding evaluations in low-resource NLP contexts.
- When should I choose awesome-LLM-resources over mteb?
- Choose awesome-LLM-resources over mteb when Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks, Model Training; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
- When should I avoid mteb?
- Your project exclusively focuses on a single language or modality not covered by MTEB’s broad scope. You need a tool that supports operations beyond evaluation, such as model training or fine-tuning directly within the same system.
- When should I avoid awesome-LLM-resources?
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
- Is mteb or awesome-LLM-resources more popular on GitHub?
- awesome-LLM-resources has more GitHub stars (8,845 vs 3,364). Stars measure visibility, not whether either tool fits your constraints.
- Are mteb and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (mteb: Apache-2.0, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to mteb or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at mteb alternatives and awesome-LLM-resources alternatives (mteb markdown twin, awesome-LLM-resources 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, mteb or awesome-LLM-resources?
- mteb: Very active. awesome-LLM-resources: 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 mteb and awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mteb trust report; awesome-LLM-resources trust report.