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Update default models with pricing documentation
- Add pricing comments (USD per 1M tokens input/output) - Update to cost-optimized models for weekly CI runs: - openai: gpt-4o-mini ($0.15 / $0.60) - gemini: gemini-2.5-flash-lite ($0.10 / $0.40) - anthropic: claude-haiku-4-5 ($1.00 / $5.00) - Use model aliases that auto-update to latest snapshots - Note: Anthropic is ~10x more expensive than Gemini - Update docs and Copilot instructions with new defaults
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.github/copilot-instructions.md

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@@ -109,7 +109,8 @@ cell_ann = CellAnnotator(
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### LLM Provider Selection
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- Providers: `"openai"` (default), `"gemini"`, `"anthropic"`
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- API keys via environment variables or `.env` file (loaded with python-dotenv)
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- Models: `gpt-4o-mini` (default), `gpt-4o`, `gemini-2.0-flash-exp`, `claude-3-5-sonnet-20241022`
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- Models: `gpt-4o-mini`, `gemini-2.5-flash-lite`, `claude-haiku-4-5` (defaults)
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- Anthropic is most expensive ($1/$5 per 1M tokens), minimize usage in tests
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### Structured Outputs with Pydantic
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- `CellTypeListOutput`: List of expected cell types

src/cell_annotator/_constants.py

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min_markers: int = 15
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use_raw: bool = False
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default_min_color_distance: float = 4.0 # Delta E threshold for color distinguishability
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# Default models optimized for cost-effectiveness in CI runs
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# Pricing in USD per 1M tokens (input / output):
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# - openai (gpt-4o-mini): $0.15 / $0.60
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# - gemini (gemini-2.5-flash-lite): $0.10 / $0.40
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# - anthropic (claude-haiku-4-5): $1.00 / $5.00
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default_models: dict[str, str] = {
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"openai": "gpt-4o-mini",
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"gemini": "gemini-2.5-flash-lite",
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"anthropic": "claude-3-5-haiku-20241022",
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"anthropic": "claude-haiku-4-5",
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}
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# Supported LLM providers
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supported_providers: list[str] = ["openai", "gemini", "anthropic"]

src/cell_annotator/_docs.py

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_model = """\
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model
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Model name. If None, uses the default model for the selected or auto-detected provider.
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Examples: 'gpt-4o-mini', 'gemini-2.5-flash', 'claude-3-haiku'."""
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Examples: 'gpt-4o-mini', 'gemini-2.5-flash-lite', 'claude-haiku-4-5'."""
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_max_completion_tokens = """\
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max_completion_tokens

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