model-registry-maintainer

Model Registry Maintainer

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Model Registry Maintainer

This skill provides guidance for maintaining MassGen's model registry across two key files:

  • massgen/backend/capabilities.py

  • Models, capabilities, release dates

  • massgen/token_manager/token_manager.py

  • Pricing, context windows

When to Use This Skill

  • New model released by a provider

  • Model pricing changes

  • Context window limits updated

  • Model capabilities changed

  • New provider/backend added

Two Files to Maintain

File 1: capabilities.py (Models & Features)

What it contains:

  • List of available models per provider

  • Model capabilities (web search, code execution, vision, etc.)

  • Release dates

  • Default models

Used by:

  • Config builder (--quickstart , --generate-config )

  • Documentation generation

  • Backend validation

Always update this file for new models.

File 2: token_manager.py (Pricing & Limits)

What it contains:

  • Hardcoded pricing/context windows for models NOT in LiteLLM database

  • On-demand loading from LiteLLM database (500+ models)

Used by:

  • Cost estimation

  • Token counting

  • Context management

Pricing resolution order:

  • LiteLLM database (fetched on-demand, cached 1 hour)

  • Hardcoded PROVIDER_PRICING (fallback only)

  • Pattern matching heuristics

Only update PROVIDER_PRICING if:

  • Model is NOT in LiteLLM database

  • LiteLLM pricing is incorrect/outdated

  • Model is custom/internal to your organization

Information to Gather for New Models

  1. Release Date
  1. Context Window

  • Input context size (tokens)

  • Max output tokens

  • Look for: "context window", "max tokens", "input/output limits"

  1. Pricing
  1. Capabilities

  • Web search, code execution, vision, reasoning, etc.

  • Check official API documentation

  1. Model Name

  • Exact API identifier (case-sensitive)

  • Check provider's model documentation

Adding a New Model - Complete Workflow

Step 1: Add to capabilities.py

Add model to the models list and model_release_dates :

massgen/backend/capabilities.py

"openai": BackendCapabilities( # ... existing fields ... models=[ "new-model-name", # Add here (newest first) "gpt-5.1", # ... existing models ... ], model_release_dates={ "new-model-name": "2025-12", # Add here "gpt-5.1": "2025-11", # ... existing dates ... }, )

Step 2: Check if pricing is in LiteLLM (Usually Skip)

First, check if the model is already in LiteLLM database:

import requests

url = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" pricing_db = requests.get(url).json()

if "new-model-name" in pricing_db: print("✅ Model found in LiteLLM - no need to update token_manager.py") print(f"Pricing: ${pricing_db['new-model-name']['input_cost_per_token']*1000}/1K input") else: print("❌ Model NOT in LiteLLM - need to add to PROVIDER_PRICING")

Only if NOT in LiteLLM, add to PROVIDER_PRICING :

massgen/token_manager/token_manager.py

PROVIDER_PRICING: Dict[str, Dict[str, ModelPricing]] = { "OpenAI": { # Format: ModelPricing(input_per_1k, output_per_1k, context_window, max_output) "new-model-name": ModelPricing(0.00125, 0.01, 300000, 150000), # ... existing models ... }, }

Provider name mapping:

  • "OpenAI" (not "openai")

  • "Anthropic" (not "claude")

  • "Google" (not "gemini")

  • "xAI" (not "grok")

Step 3: Update Capabilities (if new features)

If the model introduces new capabilities:

supported_capabilities={ "web_search", "code_execution", "new_capability", # Add here }

Step 4: Update Default Model (if appropriate)

Only change if the new model should be the recommended default:

default_model="new-model-name"

Step 5: Validate and Test

Run capabilities tests

uv run pytest massgen/tests/test_backend_capabilities.py -v

Test config generation with new model

massgen --generate-config ./test.yaml --config-backend openai --config-model new-model-name

Verify the config was created successfully

cat ./test.yaml

Step 6: Regenerate Documentation

uv run python docs/scripts/generate_backend_tables.py cd docs && make html

Current Model Data

OpenAI Models (as of Nov 2025)

In capabilities.py:

models=[ "gpt-5.1", # 2025-11 "gpt-5-codex", # 2025-09 "gpt-5", # 2025-08 "gpt-5-mini", # 2025-08 "gpt-5-nano", # 2025-08 "gpt-4.1", # 2025-04 "gpt-4.1-mini", # 2025-04 "gpt-4.1-nano", # 2025-04 "gpt-4o", # 2024-05 "gpt-4o-mini", # 2024-07 "o4-mini", # 2025-04 ]

In token_manager.py (add missing models):

"OpenAI": { "gpt-5": ModelPricing(0.00125, 0.01, 400000, 128000), "gpt-5-mini": ModelPricing(0.00025, 0.002, 400000, 128000), "gpt-5-nano": ModelPricing(0.00005, 0.0004, 400000, 128000), "gpt-4o": ModelPricing(0.0025, 0.01, 128000, 16384), "gpt-4o-mini": ModelPricing(0.00015, 0.0006, 128000, 16384), # Missing: gpt-5.1, gpt-5-codex, gpt-4.1 family, o4-mini }

Claude Models (as of Nov 2025)

In capabilities.py:

models=[ "claude-haiku-4-5-20251001", # 2025-10 "claude-sonnet-4-5-20250929", # 2025-09 "claude-opus-4-1-20250805", # 2025-08 "claude-sonnet-4-20250514", # 2025-05 ]

In token_manager.py:

"Anthropic": { "claude-haiku-4-5": ModelPricing(0.001, 0.005, 200000, 65536), "claude-sonnet-4-5": ModelPricing(0.003, 0.015, 200000, 65536), "claude-opus-4.1": ModelPricing(0.015, 0.075, 200000, 32768), "claude-sonnet-4": ModelPricing(0.003, 0.015, 200000, 8192), }

Gemini Models (as of Nov 2025)

In capabilities.py:

models=[ "gemini-3-pro-preview", # 2025-11 "gemini-2.5-flash", # 2025-06 "gemini-2.5-pro", # 2025-06 ]

In token_manager.py (missing gemini-2.5 and gemini-3):

"Google": { "gemini-1.5-pro": ModelPricing(0.00125, 0.005, 2097152, 8192), "gemini-1.5-flash": ModelPricing(0.000075, 0.0003, 1048576, 8192), # Missing: gemini-2.5-pro, gemini-2.5-flash, gemini-3-pro-preview }

Grok Models (as of Nov 2025)

In capabilities.py:

models=[ "grok-4-1-fast-reasoning", # 2025-11 "grok-4-1-fast-non-reasoning", # 2025-11 "grok-code-fast-1", # 2025-08 "grok-4", # 2025-07 "grok-4-fast", # 2025-09 "grok-3", # 2025-02 "grok-3-mini", # 2025-05 ]

In token_manager.py (missing grok-3, grok-4 families):

"xAI": { "grok-2-latest": ModelPricing(0.005, 0.015, 131072, 131072), "grok-2": ModelPricing(0.005, 0.015, 131072, 131072), "grok-2-mini": ModelPricing(0.001, 0.003, 131072, 65536), # Missing: grok-3, grok-4, grok-4-1 families }

Model Name Matching

Important: The names in PROVIDER_PRICING use simplified patterns:

  • "gpt-5" matches gpt-5 , gpt-5-preview , gpt-5-*

  • "claude-sonnet-4-5" matches claude-sonnet-4-5-* (any date suffix)

  • "gemini-2.5-pro" is exact match

The token manager uses prefix matching for flexibility.

Common Tasks

Task: Add brand new GPT-5.2 model

  • Research: Release date, pricing, context window, capabilities

  • Add to capabilities.py models list and release_dates

  • Add to token_manager.py PROVIDER_PRICING["OpenAI"]

  • Run tests

  • Regenerate docs

Task: Update pricing for existing model

  • Verify new pricing from official source

  • Update only token_manager.py PROVIDER_PRICING

  • No need to touch capabilities.py

  • Document change in notes if significant

Task: Add new capability to model

  • Update supported_capabilities in capabilities.py

  • Add to notes explaining when/how capability works

  • Update backend implementation if needed

  • Run tests

Validation Commands

Test capabilities registry

uv run pytest massgen/tests/test_backend_capabilities.py -v

Test token manager

uv run pytest massgen/tests/test_token_manager.py -v

Generate config with new model

massgen --generate-config ./test.yaml --config-backend openai --config-model new-model

Build docs to verify tables

cd docs && make html

Programmatic Model Updates

LiteLLM Pricing Database (RECOMMENDED)

The easiest way to get comprehensive model pricing and context window data:

URL: https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json

Coverage: 500+ models across 30+ providers including:

  • OpenAI, Anthropic, Google, xAI

  • Together AI, Groq, Cerebras, Fireworks

  • AWS Bedrock, Azure, Cohere, and more

Data Available:

{ "gpt-4o": { "input_cost_per_token": 0.0000025, "output_cost_per_token": 0.00001, "max_input_tokens": 128000, "max_output_tokens": 16384, "supports_vision": true, "supports_function_calling": true, "supports_prompt_caching": true } }

Usage:

import requests

Fetch latest pricing

url = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" pricing_db = requests.get(url).json()

Get info for a model

model_info = pricing_db.get("gpt-4o") input_per_1k = model_info["input_cost_per_token"] * 1000 output_per_1k = model_info["output_cost_per_token"] * 1000

Update token_manager.py from LiteLLM:

  • Convert per-token costs to per-1K costs

  • Extract context window and max output tokens

  • Keep models in reverse chronological order

OpenRouter API (Real-Time)

For the most up-to-date model list with live pricing:

Endpoint: https://openrouter.ai/api/v1/models

Data Available:

  • Real-time pricing (prompt, completion, reasoning, caching)

  • Context windows and max completion tokens

  • Model capabilities and modalities

  • 200+ models from multiple providers

Usage:

import requests import os

headers = {"Authorization": f"Bearer {os.environ['OPENROUTER_API_KEY']}"} response = requests.get("https://openrouter.ai/api/v1/models", headers=headers) models = response.json()["data"]

for model in models: print(f"{model['id']}: ${model['pricing']['prompt']} input, ${model['pricing']['completion']} output")

Provider-Specific APIs

Provider Models API Pricing in API? Recommendation

OpenAI https://api.openai.com/v1/models

❌ No Use LiteLLM

Claude No public API ❌ No Use LiteLLM

Gemini https://generativelanguage.googleapis.com/v1beta/models

❌ No API + LiteLLM

Grok (xAI) https://api.x.ai/v1/models

❌ No Use LiteLLM

Together AI https://api.together.xyz/v1/models

✅ Yes API directly

Groq https://api.groq.com/openai/v1/models

❌ No Use LiteLLM

Cerebras https://api.cerebras.ai/v1/models

❌ No Use LiteLLM

Fireworks https://api.fireworks.ai/v1/accounts/{id}/models

❌ No Use LiteLLM

Azure OpenAI Azure Management API ❌ Complex Manual

Claude Code No API ❌ No Manual

Automation Script

Create scripts/update_model_pricing.py to automate updates:

#!/usr/bin/env python3 """Update token_manager.py pricing from LiteLLM database."""

import requests

Fetch LiteLLM database

url = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" pricing_db = requests.get(url).json()

Filter by provider

openai_models = {k: v for k, v in pricing_db.items() if v.get("litellm_provider") == "openai"} anthropic_models = {k: v for k, v in pricing_db.items() if v.get("litellm_provider") == "anthropic"}

Generate ModelPricing entries

for model_name, info in openai_models.items(): input_per_1k = info["input_cost_per_token"] * 1000 output_per_1k = info["output_cost_per_token"] * 1000 context = info.get("max_input_tokens", 0) max_output = info.get("max_output_tokens", 0)

print(f'    "{model_name}": ModelPricing({input_per_1k}, {output_per_1k}, {context}, {max_output}),')

Run weekly to keep pricing current:

uv run python scripts/update_model_pricing.py

Reference Files

Important Maintenance Notes

  • Keep models in reverse chronological order - Newest first

  • Use exact API names - Match provider documentation exactly

  • Verify pricing units - Always per 1K tokens in token_manager.py

  • Document uncertainties - If info is estimated/unofficial, note it

  • Update both files - Don't forget token_manager.py when adding models

  • Use LiteLLM for pricing - Comprehensive and frequently updated

  • Test after updates - Run pytest to verify no breaking changes

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