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LiteLLM pricing is incorrect/outdated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

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

Research: Release date, pricing, context window, capabilities
Add to capabilities.py models list and release_dates
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model-registry-maintainer

Como adicionar

Cole no README do seu repo

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