Models
Modelsmodels
Providers using this tag (203)
Ranked by API Evangelist rating — Exemplar and Strong are expanded by default.
Exemplar 7 Complete, well-documented, and agent-ready
Strong 33 Solid coverage with minor gaps
Developing 77 Usable, with meaningful gaps to close
Thin 69 Limited public surface area
APIs with this tag (206)
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 7 Complete, well-documented, and agent-ready
Strong 34 Solid coverage with minor gaps
Developing 75 Usable, with meaningful gaps to close
Thin 73 Limited public surface area
Emerging 17 Early or largely undocumented
Score breakdown
Related tags
Where this tag comes from
Cohort brief
Auto-generatedThe 203 providers in the APIs.io catalog tagged Models, scored on the Kin Score. Every figure below is computed from the catalog — nothing here is written.
| Facet | This cohort | Catalog | Difference | Scored |
|---|---|---|---|---|
| Contract Quality | 52.3 | 17.5 | +34.8 | 203 |
| Developer Ergonomics | 44 | 17.9 | +26.1 | 203 |
| Access Clarity | 39.8 | 22.3 | +17.5 | 203 |
| Operational Transparency | 27 | 11.3 | +15.7 | 203 |
| Discoverability | 73 | 60.4 | +12.6 | 203 |
| Contract Governance | 14.9 | 6.4 | +8.5 | 203 |
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) | 40% | 16% | +24 |
| MCP server (first-party) | 14% | 7% | +7 |
| Agent Skills | 0% | 0% | 0 |
| OAuth scopes | 14% | 9% | +5 |
| Security | 99% | 88% | +11 |
| Arazzo workflows | 14% | 2% | +12 |
| Governance rules | 47% | 13% | +34 |
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 Salesforce 83.3
- 2 RudderStack 80.2
- 3 NVIDIA NIM 71.2
- 4 Hightouch 70.6
- 5 BlueConic 68.8
- 6 Anthropic 68.6
- 7 Inworld AI 66.7
- 8 FLORA 65.9
- 9 Amazon SageMaker 65
- 10 Factset 64.8
- 1 FLORA 62.4
- 2 Hightouch 61.2
- 3 Sarj AI Developer API 60.4
- 4 Hugging Face Transformers 53.9
- 5 Factset 52.8
- 6 Salesforce 52.2
- 7 RudderStack 52
- 8 Microsoft Azure 49.6
- 9 Gumloop 47.3
- 10 BlueConic 47
Work with this as data
Every tag here is available over the APIs.io API and to AI agents over MCP.
MCP server
One button, every client — Claude, Cursor, VS Code and the rest.
https://apis.io/mcp
Tools for tags
7 MCP tools reach this
find_tagsBrowse and filter every tag in the catalog.get_cohortThis tag as a scored cohort — every provider carrying it, with scores.cohort_statsPRO — the distribution across this tag: mean, median, band split, adoption rates.cohort_rankingsPRO — the leaderboard, on composite AND agent-readiness axes.apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.resolveTurn a domain, URL or GitHub org into the provider it belongs to.find_cohortsEvery scored population of providers in the catalog.
Call it yourself
curl for this page
curl "https://apis.io/api/v1/tags/models"
curl "https://apis.io/api/v1/tags?limit=25"
curl "https://apis.io/api/v1/cohorts/tag/models"
curl "https://apis.io/api/v1/cohorts/tag/models/stats" \
-H "X-API-Key: $APIS_IO_KEY"
Discovery needs no key. Ratings and market analysis are Pro.