LLMSpecs is a lightweight Ruby interface for fetching large language model (LLM) specifications from the Parsera API. It provides a simple, efficient way to access model metadata with built-in caching and query support.
- Fetches LLM specs from: https://api.parsera.org/v1/llm-specs
- Caches results locally to llm-specs.json
- Provides a queryable catalog of models via .models
- Supports filtering and lookup by model attributes
require 'llm_specs'
models = LLMSpecs.models # all models
models = LLMSpecs.gemini # provider specific models
models = LLMSpecs.anthropic
models = LLMSpecs.openai
models = LLMSpecs.deepseek
providers = LLMSpecs.providers # a list of all providers covered by Parsera APImodel = LLMSpecs.models.find("claude-3-sonnet")# List all Anthropic models
anthropic_models = LLMSpecs.models.where(provider: "anthropic")
# or
anthropic_models = LLMSpecs.anthropic
# List all Anthropic models by family "claude-3-7-sonnet"
claude_models = LLMSpecs.models.where(provider: "anthropic", family: "claude-3-7-sonnet")
#or
claude_models = LLMSpecs.anthropic.where(family: "claude-3-7-sonnet")
# List all models that support streaming
streaming_models = LLMSpecs.models.select(&:streaming?)
# List all models that support audio output
audio_models = LLMSpecs.models.select(&:audio_output?)Each model is represented by an instance of LLMSpecs::Model, a value object that encapsulates:
-
id,name,provider,family -
Token limits (
context_window,max_output_tokens) -
Supported modalities (e.g.
text,image) -
Capabilities such as:
function_calling?structured_output?streaming?reasoning?citations?batch?
-
Pricing breakdowns via
pricing,input_pricingandoutput_pricing
You can easily check capabilities:
model.supports?(:function_calling) # => true/false
model.function_calling? # => shortcut for above
# with multiple arguments
model.supports?(:function_calling, :batch) # => true/falseor input or output modalities:
model.supports_input?(:audio) # => true/false
model.supports_input?(:audio, :text) # multiple argument
model.audio_input? # shortcut
model.image_input?
model.text_input?
model.supports_output?(:audio) # => true/false
model.supports_output?(:audio, :embeddings) # multiple argument
model.audio_output? # shortcut
model.image_output?
model.text_output?
model.embeddings_output?Pricing methods:
model.pricing # => returns a hash:
# {
# text_tokens: {
# standard: {
# input_per_million: 15.0,
# cached_input_per_million: 18.75,
# output_per_million: 75.0
# },
# batch: {
# input_per_million: nil,
# output_per_million: nil
# }
# },
# embeddings: {
# standard: {
# input_per_million: nil
# },
# batch: {
# input_per_million: nil
# }
# }
# }
model.pricing.dig(:text_tokens, :standard, :input_per_million)
# input/output pricing
model.input_pricing # 15.0 => $ per 1M input tokens (default to pricing[:text_tokens][:standard][:input_per_million])
model.output_pricing # 75.0 => $ per 1M output tokens
model.input_pricing(:text_tokens, :batch)
model.input_pricing(:embeddings)LLMSpecs.cache_path = Rails.root.join("tmp", "cache", "llm-specs-cache.json")The gem is available as open source under the terms of the MIT License.