model-registry-maintainer

name	model-registry-maintainer
description	Guide for maintaining the MassGen model and backend registry. This skill should be used when adding new models, updating model information (release dates, pricing, context windows), or ensuring the registry stays current with provider releases. Covers both the capabilities registry and the pricing/token manager.

Model Registry Maintainer

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

1. massgen/backend/capabilities.py - Models, capabilities, release dates
2. 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:

1. LiteLLM database (fetched on-demand, cached 1 hour)
2. Hardcoded PROVIDER_PRICING (fallback only)
3. 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• LiteLLM pricing is incorrect/outdated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

• Format: "YYYY-MM"
• Sources:
  • OpenAI: https://openai.com/index
  • Anthropic: https://www.anthropic.com/news
  • Google DeepMind: https://blog.google/technology/google-deepmind/
  • xAI: https://x.ai/news

2. Context Window

• Input context size (tokens)
• Max output tokens
• Look for: "context window", "max tokens", "input/output limits"

3. Pricing

• Input cost per 1K tokens (USD)
• Output cost per 1K tokens (USD)
• Cached input cost (if applicable)
• Sources:
  • OpenAI: https://openai.com/api/pricing/
  • Anthropic: https://www.anthropic.com/pricing
  • Google: https://ai.google.dev/pricing
  • xAI: https://x.ai/api/pricing

4. Capabilities

• Web search, code execution, vision, reasoning, etc.
• Check official API documentation

5. 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

1. Research: Release date, pricing, context window, capabilities
2. Add to capabilities.py models list and release_dates
3. Add to token_manager.py PROVIDER_PRICING["OpenAI"]
4. Run tests
5. Regenerate docs

Task: Update pricing for existing model

1. Verify new pricing from official source
2. Update only token_manager.py PROVIDER_PRICING
3. No need to touch capabilities.py
4. Document change in notes if significant

Task: Add new capability to model

1. Update supported_capabilities in capabilities.py
2. Add to notes explaining when/how capability works
3. Update backend implementation if needed
4. 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

• Capabilities registry: massgen/backend/capabilities.py
• Token/pricing manager: massgen/token_manager/token_manager.py
• Capabilities tests: massgen/tests/test_backend_capabilities.py
• Config builder: massgen/config_builder.py
• Doc generator: docs/scripts/generate_backend_tables.py
• LiteLLM database: https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json
• OpenRouter API: https://openrouter.ai/docs/overview/models

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