Large Language Models
Providers using this tag (64)
Ranked by API Evangelist rating — Exemplar and Strong are expanded by default.
Strong 1 Solid coverage with minor gaps
Developing 24 Usable, with meaningful gaps to close
Thin 10 Limited public surface area
Emerging 16 Early or largely undocumented
Minimal 11 Almost no public developer surface
APIs with this tag (23)
Ranked by the provider's API Evangelist rating — the Kin Score is scored per provider, not per API, so every API of a provider shares its band. How the rating works →
Exemplar 1 Complete, well-documented, and agent-ready
Strong 4 Solid coverage with minor gaps
Developing 8 Usable, with meaningful gaps to close
Thin 7 Limited public surface area
Emerging 3 Early or largely undocumented
Companies reaching this through an API (8)
These companies publish an API, specification or operation carrying “Large Language Models” but do not classify their business under it. Listed unranked and kept out of the count above, because one tagged operation is not a statement about what a company does.
Score breakdown
Related tags
Where this tag comes from
Cohort brief
Auto-generatedThe 64 providers in the APIs.io catalog tagged Large Language Models, scored on the Kin Score. Every figure below is computed from the catalog — nothing here is written.
| Facet | This cohort | Catalog | Difference | Scored |
|---|---|---|---|---|
| Developer Ergonomics | 38.9 | 23.4 | +15.5 | 64 |
| Contract Quality | 30.5 | 19.8 | +10.7 | 64 |
| Operational Transparency | 22.2 | 14 | +8.2 | 64 |
| Access Clarity | 31.3 | 26.5 | +4.8 | 64 |
| Discoverability | 65.6 | 61.2 | +4.4 | 64 |
| Contract Governance | 8.5 | 6.4 | +2.1 | 64 |
A facet is averaged over the members that carry it, not over the whole cohort — the “Scored” column is that count. Averaging an absent facet as zero would score our own coverage gaps as the providers’ posture.
| Artifact | This cohort | Catalog | Difference |
|---|---|---|---|
| MCP server (any) | 17% | 10% | +7 |
| MCP server (first-party) | 17% | 13% | +4 |
| Agent Skills | 0% | 0% | 0 |
| OAuth scopes | 6% | 11% | -5 |
| Security | 100% | 97% | +3 |
| Arazzo workflows | 6% | 2% | +4 |
| Governance rules | 19% | 14% | +5 |
mcp_pct counts any mcp/ artifact including ones API Evangelist derived from the provider OpenAPI; mcp_first_party_pct counts only servers the provider publishes. Prefer the latter.
- 1 OpenAI 82.9
- 2 Anthropic 77.8
- 3 Cerebras Systems 60.4
- 4 Claude 53.8
- 5 Unify 52
- 6 H2O.ai 50.9
- 7 Parasail 50.9
- 8 Anthropic Claude 48.8
- 9 Flowise 48.5
- 10 OpenRouter 48.4
- 1 OpenAI 48.1
- 2 H2O.ai 44.4
- 3 Anthropic 37.7
- 4 Textcortex 37.2
- 5 Smithery 36.7
- 6 Uniphore 34.9
- 7 LangChain 33.4
- 8 OpenRouter 31.3
- 9 LocalAI 30.9
- 10 Hamad Bin Khalifa University 30.5
Work with this as data
Every tag here is available over the APIs.io API and to AI agents over MCP.