From 43298d4b539849c6aaad89f902e6cdec996e1047 Mon Sep 17 00:00:00 2001
From: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Date: Mon, 1 Jun 2026 22:30:15 +0000
Subject: [PATCH] feat: add Claude Code skill for adding models to
 model_prices_and_context_window.json

This skill provides step-by-step guidance for correctly adding new model
entries to the model JSON, including field requirements, pricing conversion,
provider-specific patterns, and validation checks. This should help maintain
consistency and reduce review feedback when adding new models like the
recently announced OpenAI GPT-5.5/5.4 and Codex on Bedrock.

https://claude.ai/code/session_01J75nfVUa4uStAWJr5s168o
---
 .claude/commands/add-model.md | 156 ++++++++++++++++++++++++++++++++++
 .gitignore                    |   3 +-
 2 files changed, 158 insertions(+), 1 deletion(-)
 create mode 100644 .claude/commands/add-model.md

diff --git a/.claude/commands/add-model.md b/.claude/commands/add-model.md
new file mode 100644
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+++ b/.claude/commands/add-model.md
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+# Add Model to LiteLLM
+
+Add a new model to `model_prices_and_context_window.json` with correct configuration.
+
+## Workflow
+
+1. **Get the model information** from the user or from a provided URL (AWS blog post, provider documentation, etc.). Extract:
+   - Model name/ID
+   - Provider (bedrock, openai, anthropic, etc.)
+   - Pricing (input/output per token)
+   - Context window (max input tokens, max output tokens)
+   - Supported features (vision, function calling, etc.)
+
+2. **Determine the correct model key format** based on provider:
+   - **Bedrock**: Use the model ID directly, e.g., `anthropic.claude-opus-4-5-20251101-v1:0`, `openai.gpt-5-5-v1:0`
+   - **OpenAI**: Use model name, e.g., `gpt-4o`, `o1-preview`
+   - **Anthropic**: Use model name, e.g., `claude-sonnet-4-20250514`
+   - **Azure**: Prefix with `azure/`, e.g., `azure/gpt-4o`
+   - **Other providers**: Check existing entries for that provider's pattern
+
+3. **Set required fields**:
+   - `litellm_provider`: Provider identifier. For Bedrock models using the Converse API (most modern models), use `bedrock_converse`. For older Bedrock models, use `bedrock`.
+   - `mode`: Usually `chat` for LLMs. Other options: `embedding`, `completion`, `image_generation`, `audio_transcription`, `audio_speech`, `moderation`, `rerank`, `search`
+   - `input_cost_per_token`: Cost per input token (convert from "per 1M tokens" by dividing by 1,000,000)
+   - `output_cost_per_token`: Cost per output token
+
+4. **Set token limits**:
+   - `max_input_tokens`: Maximum input context window
+   - `max_output_tokens`: Maximum output tokens the model can generate
+   - `max_tokens`: Legacy field; set to `max_output_tokens` value
+
+5. **Set feature flags** (only include if `true`):
+   - `supports_function_calling`: Tool/function calling
+   - `supports_vision`: Image input support
+   - `supports_prompt_caching`: Prompt caching support
+   - `supports_response_schema`: Structured output/JSON schema support
+   - `supports_tool_choice`: Ability to force tool selection
+   - `supports_system_messages`: System message support
+   - `supports_audio_input`: Audio input
+   - `supports_audio_output`: Audio output
+   - `supports_pdf_input`: PDF document input
+   - `supports_reasoning`: Extended thinking/reasoning
+   - `supports_computer_use`: Computer use capability
+   - `supports_assistant_prefill`: Assistant prefill
+   - `supports_parallel_function_calling`: Parallel tool calls
+   - `supports_web_search`: Web search integration
+   - `supports_native_structured_output`: Native structured outputs
+
+6. **Set caching costs** (if model supports prompt caching):
+   - `cache_creation_input_token_cost`: Cost to create cache
+   - `cache_read_input_token_cost`: Cost to read from cache
+   - `cache_creation_input_token_cost_above_1hr`: Higher tier for >1 hour cache (if applicable)
+
+7. **Add source URL**:
+   - `source`: URL to the pricing page or announcement
+
+8. **Insert the model entry** into `model_prices_and_context_window.json`:
+   - The file is sorted alphabetically by key
+   - Find the correct position and insert
+   - Ensure proper JSON formatting (no trailing commas, correct indentation)
+
+## Example: Adding a Bedrock model
+
+For a new Bedrock Claude model with:
+- Model ID: `anthropic.claude-new-model-v1:0`
+- Input: $3/1M tokens, Output: $15/1M tokens
+- Context: 200K input, 64K output
+- Supports: vision, function calling, caching, reasoning
+
+```json
+"anthropic.claude-new-model-v1:0": {
+    "cache_creation_input_token_cost": 3.75e-06,
+    "cache_read_input_token_cost": 3e-07,
+    "input_cost_per_token": 3e-06,
+    "litellm_provider": "bedrock_converse",
+    "max_input_tokens": 200000,
+    "max_output_tokens": 64000,
+    "max_tokens": 64000,
+    "mode": "chat",
+    "output_cost_per_token": 1.5e-05,
+    "source": "https://aws.amazon.com/bedrock/pricing/",
+    "supports_assistant_prefill": true,
+    "supports_function_calling": true,
+    "supports_prompt_caching": true,
+    "supports_reasoning": true,
+    "supports_response_schema": true,
+    "supports_tool_choice": true,
+    "supports_vision": true
+}
+```
+
+## Example: Adding OpenAI models on Bedrock
+
+For OpenAI GPT models available on Bedrock:
+- Use the Bedrock model ID format: `openai.gpt-5-5-v1:0`
+- Set `litellm_provider` to `bedrock_converse`
+- Include the Bedrock-specific pricing (may differ from OpenAI direct)
+
+```json
+"openai.gpt-5-5-v1:0": {
+    "input_cost_per_token": 2.5e-06,
+    "litellm_provider": "bedrock_converse",
+    "max_input_tokens": 128000,
+    "max_output_tokens": 32768,
+    "max_tokens": 32768,
+    "mode": "chat",
+    "output_cost_per_token": 1e-05,
+    "source": "https://aws.amazon.com/bedrock/pricing/",
+    "supports_function_calling": true,
+    "supports_response_schema": true,
+    "supports_tool_choice": true,
+    "supports_vision": true
+}
+```
+
+## Price conversion reference
+
+AWS and many providers list prices per 1 million tokens. Convert to per-token:
+- $3.00 per 1M tokens = 3 / 1,000,000 = 0.000003 = 3e-06
+- $0.25 per 1M tokens = 0.25 / 1,000,000 = 0.00000025 = 2.5e-07
+- $15.00 per 1M tokens = 15 / 1,000,000 = 0.000015 = 1.5e-05
+
+Cache pricing typically follows these patterns:
+- Cache creation cost: Usually 1.25x the base input cost
+- Cache read cost: Usually 0.1x the base input cost (10% of base)
+
+## Validation checklist
+
+Before committing:
+- [ ] Model key is in correct format for the provider
+- [ ] `litellm_provider` matches provider conventions
+- [ ] Prices are per-token (not per-1M-tokens)
+- [ ] `max_tokens` equals `max_output_tokens`
+- [ ] Feature flags match actual model capabilities (don't guess)
+- [ ] Entry is inserted in alphabetical order
+- [ ] JSON is valid (no trailing commas, proper formatting)
+- [ ] Source URL is included
+
+## Testing
+
+After adding the model, test with:
+
+```bash
+python litellm/proxy/proxy_cli.py --config litellm/proxy/dev_config.yaml --detailed_debug --reload
+```
+
+Then curl the proxy to verify the model works:
+
+```bash
+curl -X POST http://localhost:4000/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "bedrock/anthropic.claude-new-model-v1:0",
+    "messages": [{"role": "user", "content": "Hello"}]
+  }'
+```
diff --git a/.gitignore b/.gitignore
index 572830d35f6..eb8fbf0a475 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,7 +2,8 @@
 .venv
 .venv_policy_test
 .env
-.claude
+.claude/settings*.json
+.claude/*.local.json
 .newenv
 newenv/*
 litellm/proxy/myenv/*