LLM
LLMsLarge Language ModelLarge Language ModelsLLM
Providers using this tag (405)
Ranked by API Evangelist rating — Exemplar and Strong are expanded by default.
Exemplar 10 Complete, well-documented, and agent-ready
Strong 60 Solid coverage with minor gaps
Developing 87 Usable, with meaningful gaps to close
Thin 125 Limited public surface area
Emerging 67 Early or largely undocumented
Minimal 55 Almost no public developer surface
Unrated 1 Not yet scored
APIs with this tag (186)
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 14 Complete, well-documented, and agent-ready
Strong 35 Solid coverage with minor gaps
Developing 44 Usable, with meaningful gaps to close
Thin 63 Limited public surface area
Emerging 25 Early or largely undocumented
Minimal 5 Almost no public developer surface
Companies reaching this through an API (98)
These companies publish an API, specification or operation carrying “LLM” 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.
Show all 98 companies ↓
Score breakdown
Where this tag sits
Related tags
Where this tag comes from
Markets provisional
Kin Score markets whose measured providers carry this tag. Share is the fraction of the market's measured providers that carry it — it describes the market, not this tag.
| Market | Share of the market's providers |
|---|---|
| AI Model Routing | 65% |
| Typed Decision Models | 45% |
| Hugging Face Hub Organisations | 32% |
| Retrieval, Vector and RAG | 29% |
| Speech and Voice AI | 29% |
Cohort brief
Auto-generatedThe 394 providers in the APIs.io catalog tagged LLM, 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 | 41.1 | 24.5 | +16.6 | 394 |
| Contract Quality | 31.8 | 18.6 | +13.2 | 394 |
| Operational Transparency | 21.9 | 13.2 | +8.7 | 394 |
| Access Clarity | 38 | 30 | +8 | 394 |
| Discoverability | 65.6 | 59.1 | +6.5 | 394 |
| Contract Governance | 13.8 | 9.7 | +4.1 | 394 |
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) | 31% | 18% | +13 |
| MCP server (first-party) | 32% | 16% | +16 |
| Agent Skills | 0% | 0% | 0 |
| OAuth scopes | 9% | 13% | -4 |
| Security | 99% | 99% | 0 |
| Arazzo workflows | 3% | 1% | +2 |
| Governance rules | 62% | 36% | +26 |
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 Anthropic 86.4
- 2 OpenAI 82.5
- 3 Kong 73.8
- 4 AlphaAI 72.2
- 5 SpicyAPI 72
- 6 Octen 70.6
- 7 Bem 69.1
- 8 Dust 69.1
- 9 Hugging Face 69.1
- 10 Azure OpenAI Service 68.3
- 1 Perplexity 67.7
- 2 AlphaAI 67.6
- 3 Patronus Protect 66.9
- 4 S&P Global 65.1
- 5 kymaapi-com 61.4
- 6 Speko 61
- 7 Coval 60.7
- 8 Exa 60
- 9 OpenAI 58.4
- 10 GMI Cloud 58
Coverage caveat. 292 of 394 scored members had under half their catalog-attributable points satisfied at all (catalog_earned / catalog_max < 0.5). They are INCLUDED in the mean above. A large share here means the cohort mean is partly a measurement of how far our enrichment reached, not only of how these providers publish.Generated from the catalog build of 14 November 2026, across 41591 providers, using the same computation served by the APIs.io cohort API. Briefs are published for rosters of 5 or more scored providers; below that a distribution is not meaningful.
Work with this as data
Every tag here is available over the APIs.io API and to AI agents over MCP.