Add MiniMax M3 model costs

packages/graphrag-llm/graphrag_llm/model_cost_registry/model_cost_registry.py

@@ -9,11 +9,25 @@
 from typing_extensions import Self
 
 
-class ModelCosts(TypedDict):
+class ModelCosts(TypedDict, total=False):
     """Model costs."""
 
     input_cost_per_token: float
     output_cost_per_token: float
+    cache_read_input_token_cost: float
+    max_input_tokens: int
+    mode: str
+
+
+ADDITIONAL_MODEL_COSTS: dict[str, ModelCosts] = {
+    "minimax/MiniMax-M3": {
+        "input_cost_per_token": 0.6 / 1_000_000,
+        "output_cost_per_token": 2.4 / 1_000_000,
+        "cache_read_input_token_cost": 0.12 / 1_000_000,
+        "max_input_tokens": 1_000_000,
+        "mode": "chat",
+    },
+}
 
 
 class ModelCostRegistry:
@@ -30,7 +44,7 @@ def __new__(cls, *args: Any, **kwargs: Any) -> Self:
 
     def __init__(self):
         if not hasattr(self, "_initialized"):
-            self._model_costs = model_cost
+            self._model_costs = {**model_cost, **ADDITIONAL_MODEL_COSTS}
             self._initialized = True
 
     def register_model_costs(self, model: str, costs: ModelCosts) -> None:

tests/integration/language_model/test_factory.py

@@ -15,6 +15,7 @@
 )
 from graphrag_llm.config import ModelConfig
 from graphrag_llm.embedding import LLMEmbedding, create_embedding, register_embedding
+from graphrag_llm.model_cost_registry import model_cost_registry
 
 if TYPE_CHECKING:
     from collections.abc import AsyncIterator, Iterator
@@ -70,6 +71,17 @@ def tokenizer(self) -> "Tokenizer": ...
     assert isinstance(model, CustomChatModel)
 
 
+def test_minimax_m3_model_costs_registered():
+    costs = model_cost_registry.get_model_costs("minimax/MiniMax-M3")
+
+    assert costs is not None
+    assert costs["input_cost_per_token"] == 0.6 / 1_000_000
+    assert costs["output_cost_per_token"] == 2.4 / 1_000_000
+    assert costs["cache_read_input_token_cost"] == 0.12 / 1_000_000
+    assert costs["max_input_tokens"] == 1_000_000
+    assert costs["mode"] == "chat"
+
+
 def test_create_custom_embedding_llm():
     class CustomEmbeddingModel(LLMEmbedding):
         def __init__(self, **kwargs): ...