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ollama.com APIollama.com

Search Ollama's model library, retrieve model details, and list all tags/variants with context windows, file sizes, and pull commands via 3 REST endpoints.

Endpoints
3
Updated
10d ago
Try it
Sort order: 'popular' or 'newest'
Search keyword (e.g., 'deepseek', 'qwen3', 'gemma')
Filter by capability: 'cloud', 'embedding', 'vision', 'tools', 'thinking'. Empty string me
api.parse.bot/scraper/f1555b97-c36d-48d9-b657-60146faa21e9/<endpoint>
Ready to send
Fill in the parameters and hit sign in to send to see live response data here.
Use it in your codegrab a free API key at signup
curl -X GET 'https://api.parse.bot/scraper/f1555b97-c36d-48d9-b657-60146faa21e9/search_models?sort=popular&query=gemma&capability=vision' \
  -H 'X-API-Key: $PARSE_API_KEY'
All endpoints · 3 totalclick to expand

Search for models on Ollama by keyword, with optional capability filters and sorting. Returns a list of matching models with descriptions, capabilities, available sizes, download counts, and pull commands. Combining sort=newest with a capability filter may return fewer results than sort=popular.

Input
ParamTypeDescription
sortstringSort order: 'popular' or 'newest'
querystringSearch keyword (e.g., 'deepseek', 'qwen3', 'gemma')
capabilitystringFilter by capability: 'cloud', 'embedding', 'vision', 'tools', 'thinking'. Empty string means no filter.
Response
{
  "type": "object",
  "fields": {
    "sort": "string, the sort order used",
    "query": "string, the search keyword used",
    "models": "array of model objects with name, slug, description, capabilities, sizes, pull_count, tag_count, updated, pull_command",
    "total_results": "integer, number of models returned",
    "capability_filter": "string, the capability filter applied"
  },
  "sample": {
    "data": {
      "sort": "popular",
      "query": "deepseek",
      "models": [
        {
          "name": "deepseek-r1",
          "slug": "deepseek-r1",
          "sizes": [
            "1.5b",
            "7b",
            "8b",
            "14b",
            "32b",
            "70b",
            "671b"
          ],
          "updated": "10 months ago",
          "tag_count": "35",
          "pull_count": "84.2M",
          "description": "DeepSeek-R1 is a family of open reasoning models with performance approaching that of leading models.",
          "capabilities": [
            "tools",
            "thinking"
          ],
          "pull_command": "ollama pull deepseek-r1"
        }
      ],
      "total_results": 15,
      "capability_filter": ""
    },
    "status": "success"
  }
}

About the ollama.com API

The Ollama API exposes 3 endpoints for searching and inspecting AI models hosted on ollama.com. search_models returns a filterable list of models including capabilities, parameter sizes, and download counts. get_model_detail returns per-model metadata such as run and pull commands. get_model_tags returns every variant for a given model, including context window length, file size, and hash.

What the API covers

The Ollama API gives programmatic access to the model library at ollama.com. Three endpoints cover the full discovery workflow: searching the catalog, reading per-model metadata, and enumerating every tagged variant. Response fields include capabilities (values like thinking, tools, vision, embedding), formatted download_count, pull_command, run_command, and relative updated timestamps.

Searching and filtering models

search_models accepts a free-text query (e.g. deepseek, qwen3, gemma), an optional capability filter, and a sort parameter of either popular or newest. The response includes a models array where each object carries the model slug, description, sizes (parameter counts like 1.5b, 7b, 70b), tag_count, pull_count, and a ready-to-paste pull_command. Note that combining sort=newest with a capability filter can reduce result counts compared to using either alone.

Model detail and tags

get_model_detail takes a model_name slug and returns the full display title, description, capabilities array, all available sizes, and both pull_command and run_command strings — useful for automating local setup scripts. For finer-grained variant data, get_model_tags returns a tags array where each entry includes the exact tag, full_name, size (on-disk file size), context (context window length), input_type, hash, and pull_command. total_tags gives the count of all variants for that model.

Common use cases
  • Build a model selection UI that filters ollama.com models by capability (e.g. vision, tools) and sorts by popularity
  • Automate local LLM setup scripts by pulling pull_command and run_command from get_model_detail
  • Compare context window sizes across all variants of a model using context fields from get_model_tags
  • Track newly published models by polling search_models with sort=newest
  • Build a model size calculator by aggregating size fields from get_model_tags to estimate disk requirements
  • Generate a changelog or catalog of available embedding models by filtering search_models with capability=embedding
  • Identify the most-downloaded models in a category using pull_count from search_models results
Pricing & limitsSee full pricing →
TierPriceCredits/monthRate limit
Free$0/mo1005 req/min
Hobby$30/mo1,00020 req/min
Developer$100/mo5,000250 req/min

One credit = one API call regardless of which marketplace API you call. Exceeding the rate limit returns a 429 response. Authenticate with the X-API-Key header.

Frequently asked questions
Does Ollama have an official developer API?+
Ollama provides a local REST API for running models on your own machine, documented at https://github.com/ollama/ollama/blob/main/docs/api.md. That API operates against a locally running Ollama instance. This Parse API covers the public model library at ollama.com — searching models, reading metadata, and listing variants — without requiring a local installation.
What does `get_model_tags` return beyond what `get_model_detail` provides?+
get_model_detail returns aggregate metadata: display title, description, all available parameter sizes, total download count, and pull/run commands. get_model_tags goes per-variant: each tag object includes the exact tag string, on-disk size, context window length, input_type, and a hash for integrity checking. Use get_model_tags when you need to differentiate between, for example, a q4_0 and q8_0 quantization of the same model.
Are user reviews, community comments, or model benchmark scores available?+
Not currently. The API covers model metadata, capabilities, variant sizes, context windows, and download counts. User reviews, comments, and benchmark results are not part of the response shape. You can fork this API on Parse and revise it to add an endpoint targeting that data if it becomes available on the page.
Is there pagination support for `search_models` results?+
The endpoint returns a total_results count reflecting how many models matched, but there is no page or offset parameter exposed. All matching results for the query are returned in a single response. If you need to paginate across the full catalog, you can fork this API on Parse and revise it to add offset or cursor parameters.
Does the `capability` filter in `search_models` affect result counts?+
Yes. The capability_filter field in the response confirms what filter was applied. Combining sort=newest with a capability filter (e.g. capability=thinking) tends to return fewer results than using sort=popular with the same filter, because not all recently added models carry every capability tag. Omitting the capability field returns the full unfiltered set for the given query and sort.
Page content last updated . Spec covers 3 endpoints from ollama.com.
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