dyadadd-models

name	dyad:add-models
description	Add one or more AI models to the language model constants file, researching specs from official docs.

Add Models

Add one or more AI models to src/ipc/shared/language_model_constants.ts, researching correct specifications from official documentation.

Arguments

• $ARGUMENTS: Comma-separated list of model names to add (e.g., "gemini 3.1 pro, glm 5, sonnet 4.6").

Instructions

1. Parse the model list:

   Split $ARGUMENTS by commas to get individual model names. Trim whitespace from each.

2. Read the current constants file:

   Read src/ipc/shared/language_model_constants.ts to understand:

   • Which providers exist and their current model entries
   • The naming conventions for each provider (e.g., claude-sonnet-4-20250514 for Anthropic, gemini-2.5-pro for Google)
   • The structure of ModelOption entries (name, displayName, description, maxOutputTokens, contextWindow, temperature, dollarSigns)

3. Identify the provider for each model:

   Map each model to its provider based on the model name:

   • Anthropic (anthropic): Claude models (Opus, Sonnet, Haiku)
   • OpenAI (openai): GPT models
   • Google (google): Gemini models
   • xAI (xai): Grok models
   • OpenRouter (openrouter): Models from other providers (DeepSeek, Qwen, Moonshot/Kimi, Z-AI/GLM, etc.)
   • Azure (azure): Azure-hosted OpenAI models
   • Bedrock (bedrock): AWS Bedrock-hosted Anthropic models
   • Vertex (vertex): Google Vertex AI-hosted models

   If a model could belong to multiple providers (e.g., a new Anthropic model should go in anthropic AND potentially bedrock), add it to the primary provider. Ask the user if they also want it added to secondary providers.

4. Research each model's specifications:

   For each model, use WebSearch and WebFetch to look up the official documentation:

   • Anthropic models: Search docs.anthropic.com for model specs
   • OpenAI models: Search platform.openai.com/docs/models for model specs
   • Google Gemini models: Search ai.google.dev/gemini-api/docs/models for model specs
   • xAI models: Search docs.x.ai/docs/models for model specs
   • OpenRouter models: Search openrouter.ai/<provider>/<model-name> for model specs and pricing

   For each model, determine:

   • API model name: The exact string used in API calls (e.g., claude-sonnet-4-5-20250929, gemini-2.5-pro)
   • Display name: Human-readable name (e.g., "Claude Sonnet 4.5", "Gemini 2.5 Pro")
   • Description: Short description following the style of existing entries
   • Max output tokens: The model's maximum output token limit
   • Context window: The model's total context window size
   • Temperature: Default temperature (0 for most models, 1 for OpenAI, 1.0 for Gemini 3.x models)
   • Dollar signs: Cost tier from 0-6 based on pricing relative to other models in the same provider

   Dollar signs guide (approximate, based on per-million-token input pricing):

   • 0: Free
   • 1: Very cheap (<$0.50/M input tokens)
   • 2: Cheap ($0.50-$2/M)
   • 3: Moderate ($2-$8/M)
   • 4: Expensive ($8-$15/M)
   • 5: Very expensive ($15-$30/M)
   • 6: Premium ($30+/M)

5. Follow provider-specific conventions:

   Match the patterns used by existing entries:

   • OpenAI: maxOutputTokens: undefined (OpenAI errors with max_tokens), temperature: 1
   • Anthropic: maxOutputTokens: 32_000, temperature: 0
   • Google: maxOutputTokens: 65_536 - 1 (exclusive upper bound for Vertex), temperature varies
   • OpenRouter: maxOutputTokens: 32_000, prefix model name with provider (e.g., deepseek/deepseek-chat-v3.1)
   • Azure: maxOutputTokens commented out, temperature: 1
   • Bedrock: Model names use ARN format (e.g., us.anthropic.claude-sonnet-4-5-20250929-v1:0)
   • xAI: maxOutputTokens: 32_000, temperature: 0

6. Add the models to the constants file:

   Insert each new model entry into the appropriate provider's array in MODEL_OPTIONS. Place new models:

   • At the top of the provider's array if it's the newest/most capable model
   • After existing models of the same family but before older generations
   • Add a comment with a link to the model's documentation page

   Also check if related arrays need updating:

   • TURBO_MODELS: If the model has a turbo variant
   • PROVIDERS_THAT_SUPPORT_THINKING: If adding a new provider that supports thinking

7. Check for named constant exports:

   If the new model is likely to be referenced elsewhere (like SONNET_4_5 or GPT_5_2_MODEL_NAME), create a named constant export for it. Search the codebase for references to similar constants to determine if one is needed:

   grep -r "SONNET_4_5\|GPT_5_2_MODEL_NAME\|GEMINI_3_FLASH" src/
   

8. Verify the changes compile:

   npm run ts
   

   Fix any type errors if they occur.

9. Summarize what was added:

   Report to the user:

   • Which models were added and to which providers
   • The key specs for each (context window, max output, pricing tier)
   • Any models that couldn't be found or had ambiguous specifications
   • Any decisions that were made (e.g., choosing between model versions)