# LLMRing Registry - LLM Documentation

URL: https://llmring.ai/docs/registry/
This is the LLM-readable version of the LLMRing Registry page.

---

# LLMRing Open Registry

Public, versioned, **human-validated** registry of model capabilities and pricing, hosted on GitHub Pages. Models are keyed as `provider:model`.

Base URL: [https://llmring.github.io/registry/](https://llmring.github.io/registry/)

**Curation Philosophy**: All published registry files are reviewed and validated by humans. Automation is used only to generate draft candidates; no auto-publish to ensure data accuracy and trustworthiness.

## Files

- Current per provider: `/[provider]/models.json`
- Archived versions: `/[provider]/v/[n]/models.json`
- Manifest: `/manifest.json`

## Schema (per provider)

```json
{
  "version": 2,
  "updated_at": "2025-08-20T00:00:00Z",
  "models": {
    "openai:gpt-5-2025-08-07": {
      "provider": "openai",
      "model_name": "gpt-5-2025-08-07",
      "display_name": "GPT-5",
      "max_input_tokens": 272000,
      "max_output_tokens": 128000,
      "dollars_per_million_tokens_input": 1.25,
      "dollars_per_million_tokens_output": 10.0,
      "supports_vision": true,
      "supports_function_calling": true,
      "supports_json_mode": true,
      "supports_parallel_tool_calls": false,
      "tool_call_format": null,
      "is_reasoning_model": true,
      "min_recommended_reasoning_tokens": 2000,
      "is_active": true
    }
  }
}
```

Reasoning metadata (`is_reasoning_model`, `min_recommended_reasoning_tokens`, `supports_thinking`, `dollars_per_million_tokens_output_thinking`) drives automatic token budgeting inside the client and CLI. Cache pricing (`dollars_per_million_tokens_cached_input`, `dollars_per_million_tokens_cache_write_*`, `cache_storage_cost_per_million_tokens_per_hour`) and long-context fields (`supports_long_context_pricing`, `long_context_threshold_tokens`, `dollars_per_million_tokens_input_long_context`) inform the new 1.2 cost breakdown.

## Curation Workflow (Human-Validated, Canonical)

LLMRing's registry prioritizes accuracy through manual review:

1. **Gather sources** (recommended): Collect pricing/docs HTML and PDFs from each provider for audit trail
2. **Generate draft**: Use automation to create best-effort draft from sources (automation allowed for drafts only)
3. **Review changes**: Compare draft vs current published file, field-by-field; manually adjust as needed
4. **Promote**: Bump per-provider `version`, set `updated_at`, archive previous under `v//models.json`, replace current `models.json`

**Critical**: Published `models.json` files are always human-reviewed. Automation generates candidates only; humans make final decisions to ensure accuracy.

### CLI (from `registry` package)

```bash
# Install browser for PDF fetching (first time only)
uv run playwright install chromium

# Fetch documentation from all providers
uv run llmring-registry fetch --provider all

# Extract model information to create drafts
uv run llmring-registry extract --provider all --timeout 120

# Review draft changes for each provider
uv run llmring-registry review-draft --provider openai
uv run llmring-registry review-draft --provider anthropic
uv run llmring-registry review-draft --provider google

# Accept all changes (after review)
uv run llmring-registry review-draft --provider openai --accept-all

# Promote reviewed file to production and archive
uv run llmring-registry promote --provider openai
```

**Single provider update example:**
```bash
uv run llmring-registry fetch --provider openai
uv run llmring-registry extract --provider openai
uv run llmring-registry review-draft --provider openai --accept-all
uv run llmring-registry promote --provider openai
```

## Clients

- The `llmring` library fetches current models and uses the registry for cost calculation and limit validation.
- The server proxies the registry and may cache responses.

### Client-side Lookup Rules

- Models map is a dictionary keyed by `provider:model`. Clients should prefer O(1) lookups by that key.
- When only `model` is available, clients may attempt fallback keys: `models[model]` or `models[f"{provider}/{model}"]` for legacy data.

## Links

- Live data: [https://llmring.github.io/registry/](https://llmring.github.io/registry/)
- Source: [https://github.com/llmring/registry](https://github.com/llmring/registry)