diff --git a/docs/config-index.md b/docs/config-index.md
index 724a50d3..f7cc0cad 100644
--- a/docs/config-index.md
+++ b/docs/config-index.md
@@ -37,13 +37,13 @@ environment: production
 embeddings:
   model:
     provider: ollama
-    model: qwen3-embedding:4b
+    name: qwen3-embedding:4b
   vector_dim: 2560
 
 qa:
   model:
     provider: ollama
-    model: gpt-oss
+    name: gpt-oss
     enable_thinking: false
 ```
 
@@ -72,18 +72,18 @@ lancedb:
 embeddings:
   model:
     provider: ollama
-    model: qwen3-embedding:4b
+    name: qwen3-embedding:4b
   vector_dim: 2560
 
 reranking:
   model:
     provider: ""  # Empty to disable, or mxbai, cohere, zeroentropy, vllm
-    model: ""
+    name: ""
 
 qa:
   model:
     provider: ollama
-    model: gpt-oss
+    name: gpt-oss
     enable_thinking: false
   max_sub_questions: 3
   max_iterations: 2
@@ -92,7 +92,7 @@ qa:
 research:
   model:
     provider: ""  # Empty to use qa settings
-    model: ""
+    name: ""
     enable_thinking: true
   max_iterations: 3
   confidence_threshold: 0.8
diff --git a/docs/providers.md b/docs/providers.md
index 946c96e5..06385a77 100644
--- a/docs/providers.md
+++ b/docs/providers.md
@@ -15,7 +15,7 @@ Configure model behavior for `qa` and `research` workflows. These settings apply
 qa:
   model:
     provider: ollama
-    model: gpt-oss
+    name: gpt-oss
     temperature: 0.7
     max_tokens: 500
 ```
@@ -74,7 +74,7 @@ If you use Ollama, you can use any pulled model that supports embeddings.
 embeddings:
   model:
     provider: ollama
-    model: mxbai-embed-large
+    name: mxbai-embed-large
   vector_dim: 1024
 ```
 
@@ -106,7 +106,7 @@ uv pip install haiku.rag-slim[voyageai]
 embeddings:
   model:
     provider: voyageai
-    model: voyage-3.5
+    name: voyage-3.5
   vector_dim: 1024
 ```
 
@@ -124,7 +124,7 @@ OpenAI embeddings are included in the default installation:
 embeddings:
   model:
     provider: openai
-    model: text-embedding-3-small  # or text-embedding-3-large
+    name: text-embedding-3-small  # or text-embedding-3-large
   vector_dim: 1536
 ```
 
@@ -142,7 +142,7 @@ For high-performance local inference, you can use vLLM to serve embedding models
 embeddings:
   model:
     provider: vllm
-    model: mixedbread-ai/mxbai-embed-large-v1
+    name: mixedbread-ai/mxbai-embed-large-v1
   vector_dim: 512
 
 providers:
@@ -162,7 +162,7 @@ Configure which LLM provider to use for question answering. Any provider and mod
 qa:
   model:
     provider: ollama
-    model: gpt-oss
+    name: gpt-oss
 ```
 
 The Ollama base URL can be configured via the `OLLAMA_BASE_URL` environment variable, config file, or defaults to `http://localhost:11434`:
@@ -187,7 +187,7 @@ OpenAI QA is included in the default installation:
 qa:
   model:
     provider: openai
-    model: gpt-4o-mini  # or gpt-4, gpt-3.5-turbo, etc.
+    name: gpt-4o-mini  # or gpt-4, gpt-3.5-turbo, etc.
 ```
 
 Set your API key via environment variable:
@@ -204,7 +204,7 @@ Anthropic QA is included in the default installation:
 qa:
   model:
     provider: anthropic
-    model: claude-3-5-haiku-20241022  # or claude-3-5-sonnet-20241022, etc.
+    name: claude-3-5-haiku-20241022  # or claude-3-5-sonnet-20241022, etc.
 ```
 
 Set your API key via environment variable:
@@ -221,7 +221,7 @@ For high-performance local inference:
 qa:
   model:
     provider: vllm
-    model: Qwen/Qwen3-4B  # Any model with tool support in vLLM
+    name: Qwen/Qwen3-4B  # Any model with tool support in vLLM
 
 providers:
   vllm:
@@ -239,19 +239,19 @@ Any provider supported by Pydantic AI can be used. Examples:
 qa:
   model:
     provider: gemini
-    model: gemini-1.5-flash
+    name: gemini-1.5-flash
 
 # Groq
 qa:
   model:
     provider: groq
-    model: llama-3.3-70b-versatile
+    name: llama-3.3-70b-versatile
 
 # Mistral
 qa:
   model:
     provider: mistral
-    model: mistral-small-latest
+    name: mistral-small-latest
 ```
 
 See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the complete list of supported providers and models.
@@ -276,7 +276,7 @@ Then configure:
 reranking:
   model:
     provider: mxbai
-    model: mixedbread-ai/mxbai-rerank-base-v2
+    name: mixedbread-ai/mxbai-rerank-base-v2
 ```
 
 ### Cohere
@@ -293,7 +293,7 @@ Then configure:
 reranking:
   model:
     provider: cohere
-    model: rerank-v3.5
+    name: rerank-v3.5
 ```
 
 Set your API key via environment variable:
@@ -316,7 +316,7 @@ Then configure:
 reranking:
   model:
     provider: zeroentropy
-    model: zerank-1  # Currently the only available model
+    name: zerank-1  # Currently the only available model
 ```
 
 Set your API key via environment variable:
@@ -333,7 +333,7 @@ For high-performance local reranking using dedicated reranking models:
 reranking:
   model:
     provider: vllm
-    model: mixedbread-ai/mxbai-rerank-base-v2
+    name: mixedbread-ai/mxbai-rerank-base-v2
 
 providers:
   vllm:
diff --git a/docs/qa-research.md b/docs/qa-research.md
index d6b1826a..5f2f7074 100644
--- a/docs/qa-research.md
+++ b/docs/qa-research.md
@@ -8,7 +8,7 @@ Configure the QA workflow:
 qa:
   model:
     provider: ollama
-    model: gpt-oss
+    name: gpt-oss
     enable_thinking: false
   max_sub_questions: 3  # Maximum sub-questions for deep QA
   max_iterations: 2     # Maximum search iterations per sub-question
@@ -30,7 +30,7 @@ Configure the multi-agent research workflow:
 research:
   model:
     provider: ""            # Empty to use qa settings
-    model: ""               # Empty to use qa model
+    name: ""               # Empty to use qa model
     enable_thinking: true
   max_iterations: 3
   confidence_threshold: 0.8
diff --git a/evaluations/evaluations/benchmark.py b/evaluations/evaluations/benchmark.py
index bba02402..d721a717 100644
--- a/evaluations/evaluations/benchmark.py
+++ b/evaluations/evaluations/benchmark.py
@@ -44,18 +44,16 @@ def build_experiment_metadata(
         "dataset": dataset_key,
         "test_cases": test_cases,
         "embedder_provider": config.embeddings.model.provider,
-        "embedder_model": config.embeddings.model.model,
+        "embedder_model": config.embeddings.model.name,
         "embedder_dim": config.embeddings.vector_dim,
         "chunk_size": config.processing.chunk_size,
         "context_chunk_radius": config.processing.context_chunk_radius,
         "rerank_provider": config.reranking.model.provider
         if config.reranking.model
         else None,
-        "rerank_model": config.reranking.model.model
-        if config.reranking.model
-        else None,
+        "rerank_model": config.reranking.model.name if config.reranking.model else None,
         "qa_provider": config.qa.model.provider,
-        "qa_model": config.qa.model.model,
+        "qa_model": config.qa.model.name,
         "judge_provider": "ollama",
         "judge_model": judge_model,
     }
diff --git a/examples/ag-ui-research/backend/main.py b/examples/ag-ui-research/backend/main.py
index 9d65c932..ccf62f15 100644
--- a/examples/ag-ui-research/backend/main.py
+++ b/examples/ag-ui-research/backend/main.py
@@ -47,7 +47,7 @@ if not db_path.exists():
 
 logger.info(f"Initializing research assistant with database: {db_path}")
 logger.info(
-    f"Research Provider: {Config.research.model.provider}, Model: {Config.research.model.model}"
+    f"Research Provider: {Config.research.model.provider}, Model: {Config.research.model.name}"
 )
 
 # Store client reference for proper lifecycle management
@@ -154,7 +154,7 @@ async def health_check(_: Request) -> JSONResponse:
             "status": "healthy",
             "agent_model": str(agent.model),
             "research_provider": Config.research.model.provider,
-            "research_model": Config.research.model.model,
+            "research_model": Config.research.model.name,
             "db_path": str(db_path),
             "db_exists": db_path.exists(),
         }
diff --git a/examples/ag-ui-research/haiku.rag.yaml.example b/examples/ag-ui-research/haiku.rag.yaml.example
index dfd7ce7d..461b2e7c 100644
--- a/examples/ag-ui-research/haiku.rag.yaml.example
+++ b/examples/ag-ui-research/haiku.rag.yaml.example
@@ -17,18 +17,21 @@ providers:
     base_url: http://host.docker.internal:11434
 
 research:
-  provider: ollama
-  model: gpt-oss:latest
+  model:
+    provider: ollama
+    name: gpt-oss:latest
   max_iterations: 3
   confidence_threshold: 0.8
   max_concurrency: 1
 
 # For OpenAI:
 # research:
-#   provider: openai
-#   model: gpt-4o-mini
+#   model:
+#     provider: openai
+#     name: gpt-4o-mini
 
 # For Anthropic:
 # research:
-#   provider: anthropic
-#   model: claude-3-5-haiku-20241022
+#   model:
+#     provider: anthropic
+#     name: claude-3-5-haiku-20241022
diff --git a/haiku_rag_slim/haiku/rag/config/models.py b/haiku_rag_slim/haiku/rag/config/models.py
index 1c1007cf..aab3750b 100644
--- a/haiku_rag_slim/haiku/rag/config/models.py
+++ b/haiku_rag_slim/haiku/rag/config/models.py
@@ -11,14 +11,14 @@ class ModelConfig(BaseModel):
 
     Attributes:
         provider: Model provider (ollama, openai, anthropic, etc.)
-        model: Model name/identifier
+        name: Model name/identifier
         enable_thinking: Control reasoning behavior (true/false/None for default)
         temperature: Sampling temperature (0.0 to 1.0+)
         max_tokens: Maximum tokens to generate
     """
 
     provider: str = "ollama"
-    model: str = "gpt-oss"
+    name: str = "gpt-oss"
 
     enable_thinking: bool | None = None
     temperature: float | None = None
@@ -47,7 +47,7 @@ class EmbeddingsConfig(BaseModel):
     model: ModelConfig = Field(
         default_factory=lambda: ModelConfig(
             provider="ollama",
-            model="qwen3-embedding:4b",
+            name="qwen3-embedding:4b",
         )
     )
     vector_dim: int = 2560
@@ -61,7 +61,7 @@ class QAConfig(BaseModel):
     model: ModelConfig = Field(
         default_factory=lambda: ModelConfig(
             provider="ollama",
-            model="gpt-oss",
+            name="gpt-oss",
             enable_thinking=False,
         )
     )
@@ -74,7 +74,7 @@ class ResearchConfig(BaseModel):
     model: ModelConfig = Field(
         default_factory=lambda: ModelConfig(
             provider="ollama",
-            model="gpt-oss",
+            name="gpt-oss",
             enable_thinking=True,
         )
     )
diff --git a/haiku_rag_slim/haiku/rag/embeddings/__init__.py b/haiku_rag_slim/haiku/rag/embeddings/__init__.py
index 44464d9b..03f0be1d 100644
--- a/haiku_rag_slim/haiku/rag/embeddings/__init__.py
+++ b/haiku_rag_slim/haiku/rag/embeddings/__init__.py
@@ -16,7 +16,7 @@ def get_embedder(config: AppConfig = Config) -> EmbedderBase:
 
     if config.embeddings.model.provider == "ollama":
         return OllamaEmbedder(
-            config.embeddings.model.model, config.embeddings.vector_dim, config
+            config.embeddings.model.name, config.embeddings.vector_dim, config
         )
 
     if config.embeddings.model.provider == "voyageai":
@@ -29,21 +29,21 @@ def get_embedder(config: AppConfig = Config) -> EmbedderBase:
                 "uv pip install haiku.rag[voyageai]"
             )
         return VoyageAIEmbedder(
-            config.embeddings.model.model, config.embeddings.vector_dim, config
+            config.embeddings.model.name, config.embeddings.vector_dim, config
         )
 
     if config.embeddings.model.provider == "openai":
         from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
 
         return OpenAIEmbedder(
-            config.embeddings.model.model, config.embeddings.vector_dim, config
+            config.embeddings.model.name, config.embeddings.vector_dim, config
         )
 
     if config.embeddings.model.provider == "vllm":
         from haiku.rag.embeddings.vllm import Embedder as VllmEmbedder
 
         return VllmEmbedder(
-            config.embeddings.model.model, config.embeddings.vector_dim, config
+            config.embeddings.model.name, config.embeddings.vector_dim, config
         )
 
     raise ValueError(
diff --git a/haiku_rag_slim/haiku/rag/embeddings/base.py b/haiku_rag_slim/haiku/rag/embeddings/base.py
index 61452992..640bb152 100644
--- a/haiku_rag_slim/haiku/rag/embeddings/base.py
+++ b/haiku_rag_slim/haiku/rag/embeddings/base.py
@@ -4,7 +4,7 @@ from haiku.rag.config import AppConfig, Config
 
 
 class EmbedderBase:
-    _model: str = Config.embeddings.model.model
+    _model: str = Config.embeddings.model.name
     _vector_dim: int = Config.embeddings.vector_dim
     _config: AppConfig = Config
 
diff --git a/haiku_rag_slim/haiku/rag/reranking/__init__.py b/haiku_rag_slim/haiku/rag/reranking/__init__.py
index b9f32694..0fc83e3f 100644
--- a/haiku_rag_slim/haiku/rag/reranking/__init__.py
+++ b/haiku_rag_slim/haiku/rag/reranking/__init__.py
@@ -45,7 +45,7 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
         try:
             from haiku.rag.reranking.vllm import VLLMReranker
 
-            reranker = VLLMReranker(config.reranking.model.model)
+            reranker = VLLMReranker(config.reranking.model.name)
         except ImportError:
             reranker = None
 
@@ -54,7 +54,7 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
             from haiku.rag.reranking.zeroentropy import ZeroEntropyReranker
 
             # Use configured model or default to zerank-1
-            model = config.reranking.model.model or "zerank-1"
+            model = config.reranking.model.name or "zerank-1"
             reranker = ZeroEntropyReranker(model)
         except ImportError:
             reranker = None
diff --git a/haiku_rag_slim/haiku/rag/reranking/base.py b/haiku_rag_slim/haiku/rag/reranking/base.py
index 808371fd..bdbe76f6 100644
--- a/haiku_rag_slim/haiku/rag/reranking/base.py
+++ b/haiku_rag_slim/haiku/rag/reranking/base.py
@@ -3,9 +3,7 @@ from haiku.rag.store.models.chunk import Chunk
 
 
 class RerankerBase:
-    _model: str | None = (
-        Config.reranking.model.model if Config.reranking.model else None
-    )
+    _model: str | None = Config.reranking.model.name if Config.reranking.model else None
 
     async def rerank(
         self, query: str, chunks: list[Chunk], top_n: int = 10
diff --git a/haiku_rag_slim/haiku/rag/reranking/mxbai.py b/haiku_rag_slim/haiku/rag/reranking/mxbai.py
index 517c0dc7..e36f4f1a 100644
--- a/haiku_rag_slim/haiku/rag/reranking/mxbai.py
+++ b/haiku_rag_slim/haiku/rag/reranking/mxbai.py
@@ -8,7 +8,7 @@ from haiku.rag.store.models.chunk import Chunk
 class MxBAIReranker(RerankerBase):
     def __init__(self):
         model_name = (
-            Config.reranking.model.model
+            Config.reranking.model.name
             if Config.reranking.model
             else "mxbai-rerank-base-v2"
         )
diff --git a/haiku_rag_slim/haiku/rag/utils.py b/haiku_rag_slim/haiku/rag/utils.py
index 074e01f0..fea916a2 100644
--- a/haiku_rag_slim/haiku/rag/utils.py
+++ b/haiku_rag_slim/haiku/rag/utils.py
@@ -65,7 +65,7 @@ def get_model(
         app_config = Config
 
     provider = model_config.provider
-    model = model_config.model
+    model = model_config.name
 
     if provider == "ollama":
         model_settings = None
@@ -366,13 +366,13 @@ def prefetch_models():
     # Collect Ollama models from config
     required_models: set[str] = set()
     if Config.embeddings.model.provider == "ollama":
-        required_models.add(Config.embeddings.model.model)
+        required_models.add(Config.embeddings.model.name)
     if Config.qa.model.provider == "ollama":
-        required_models.add(Config.qa.model.model)
+        required_models.add(Config.qa.model.name)
     if Config.research.model.provider == "ollama":
-        required_models.add(Config.research.model.model)
+        required_models.add(Config.research.model.name)
     if Config.reranking.model and Config.reranking.model.provider == "ollama":
-        required_models.add(Config.reranking.model.model)
+        required_models.add(Config.reranking.model.name)
 
     if not required_models:
         return
diff --git a/tests/test_embedder_config.py b/tests/test_embedder_config.py
index bfce4190..c8492360 100644
--- a/tests/test_embedder_config.py
+++ b/tests/test_embedder_config.py
@@ -17,7 +17,7 @@ def test_embedder_uses_config_from_get_embedder():
         embeddings=EmbeddingsConfig(
             model=ModelConfig(
                 provider="ollama",
-                model="custom-model",
+                name="custom-model",
             ),
             vector_dim=512,
         ),
@@ -42,7 +42,7 @@ def test_vllm_embedder_uses_config():
         embeddings=EmbeddingsConfig(
             model=ModelConfig(
                 provider="vllm",
-                model="custom-vllm-model",
+                name="custom-vllm-model",
             ),
             vector_dim=768,
         ),
@@ -68,7 +68,7 @@ def test_openai_embedder_uses_config():
         embeddings=EmbeddingsConfig(
             model=ModelConfig(
                 provider="openai",
-                model="text-embedding-3-large",
+                name="text-embedding-3-large",
             ),
             vector_dim=3072,
         ),
@@ -90,7 +90,7 @@ def test_voyageai_embedder_uses_config():
         embeddings=EmbeddingsConfig(
             model=ModelConfig(
                 provider="voyageai",
-                model="voyage-large-2",
+                name="voyage-large-2",
             ),
             vector_dim=1536,
         ),
diff --git a/tests/test_qa.py b/tests/test_qa.py
index f4fcfee3..08f41521 100644
--- a/tests/test_qa.py
+++ b/tests/test_qa.py
@@ -19,7 +19,7 @@ async def test_qa_ollama(qa_corpus: Dataset, temp_db_path):
     """Test Ollama QA with LLM judge."""
     client = HaikuRAG(temp_db_path)
     qa = QuestionAnswerAgent(
-        client, ModelConfig(provider="ollama", model="gpt-oss", enable_thinking=False)
+        client, ModelConfig(provider="ollama", name="gpt-oss", enable_thinking=False)
     )
     llm_judge = LLMJudge()
 
@@ -44,9 +44,7 @@ async def test_qa_ollama(qa_corpus: Dataset, temp_db_path):
 async def test_qa_openai(qa_corpus: Dataset, temp_db_path):
     """Test OpenAI QA with LLM judge."""
     client = HaikuRAG(temp_db_path)
-    qa = QuestionAnswerAgent(
-        client, ModelConfig(provider="openai", model="gpt-4o-mini")
-    )
+    qa = QuestionAnswerAgent(client, ModelConfig(provider="openai", name="gpt-4o-mini"))
     llm_judge = LLMJudge()
 
     doc = qa_corpus[1]
@@ -71,7 +69,7 @@ async def test_qa_anthropic(qa_corpus: Dataset, temp_db_path):
     """Test Anthropic QA with LLM judge."""
     client = HaikuRAG(temp_db_path)
     qa = QuestionAnswerAgent(
-        client, ModelConfig(provider="anthropic", model="claude-3-5-haiku-20241022")
+        client, ModelConfig(provider="anthropic", name="claude-3-5-haiku-20241022")
     )
     llm_judge = LLMJudge()
 
@@ -96,9 +94,7 @@ async def test_qa_anthropic(qa_corpus: Dataset, temp_db_path):
 async def test_qa_vllm(qa_corpus: Dataset, temp_db_path):
     """Test vLLM QA with LLM judge."""
     client = HaikuRAG(temp_db_path)
-    qa = QuestionAnswerAgent(
-        client, ModelConfig(provider="vllm", model="Qwen/Qwen3-4B")
-    )
+    qa = QuestionAnswerAgent(client, ModelConfig(provider="vllm", name="Qwen/Qwen3-4B"))
     llm_judge = LLMJudge()
 
     doc = qa_corpus[1]
diff --git a/tests/test_reranker.py b/tests/test_reranker.py
index 8dc0105e..e7a4e8fe 100644
--- a/tests/test_reranker.py
+++ b/tests/test_reranker.py
@@ -44,7 +44,7 @@ async def test_mxbai_reranker():
         from haiku.rag.reranking.mxbai import MxBAIReranker
 
         Config.reranking.model = ModelConfig(
-            provider="mxbai", model="mixedbread-ai/mxbai-rerank-base-v2"
+            provider="mxbai", name="mixedbread-ai/mxbai-rerank-base-v2"
         )
         reranker = MxBAIReranker()
         reranked = await reranker.rerank(
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 0a194582..d40f5d17 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -136,16 +136,14 @@ Emoji test: 🚀 ✅ 📝"""
 
 def test_get_model_ollama():
     """Test get_model returns OpenAIChatModel for Ollama."""
-    model_config = ModelConfig(provider="ollama", model="llama3")
+    model_config = ModelConfig(provider="ollama", name="llama3")
     result = get_model(model_config)
     assert isinstance(result, OpenAIChatModel)
 
 
-def test_get_model_ollama_with_thinking():
+def test_get_model_ollama_without_thinking():
     """Test get_model configures thinking for gpt-oss on Ollama."""
-    model_config = ModelConfig(
-        provider="ollama", model="gpt-oss", enable_thinking=False
-    )
+    model_config = ModelConfig(provider="ollama", name="gpt-oss", enable_thinking=False)
     result = get_model(model_config)
     assert isinstance(result, OpenAIChatModel)
 
@@ -153,7 +151,7 @@ def test_get_model_ollama_with_thinking():
 def test_get_model_ollama_with_settings():
     """Test get_model applies temperature and max_tokens for Ollama."""
     model_config = ModelConfig(
-        provider="ollama", model="llama3", temperature=0.5, max_tokens=100
+        provider="ollama", name="llama3", temperature=0.5, max_tokens=100
     )
     result = get_model(model_config)
     assert isinstance(result, OpenAIChatModel)
@@ -161,14 +159,14 @@ def test_get_model_ollama_with_settings():
 
 def test_get_model_openai():
     """Test get_model returns OpenAIChatModel for OpenAI."""
-    model_config = ModelConfig(provider="openai", model="gpt-4o")
+    model_config = ModelConfig(provider="openai", name="gpt-4o")
     result = get_model(model_config)
     assert isinstance(result, OpenAIChatModel)
 
 
 def test_get_model_openai_with_thinking():
     """Test get_model configures thinking for OpenAI reasoning models."""
-    model_config = ModelConfig(provider="openai", model="o1", enable_thinking=True)
+    model_config = ModelConfig(provider="openai", name="o1", enable_thinking=True)
     result = get_model(model_config)
     assert isinstance(result, OpenAIChatModel)
 
@@ -178,7 +176,7 @@ def test_get_model_anthropic():
     """Test get_model returns AnthropicModel for Anthropic."""
     from pydantic_ai.models.anthropic import AnthropicModel
 
-    model_config = ModelConfig(provider="anthropic", model="claude-3-5-sonnet-20241022")
+    model_config = ModelConfig(provider="anthropic", name="claude-3-5-sonnet-20241022")
     result = get_model(model_config)
     assert isinstance(result, AnthropicModel)
 
@@ -190,7 +188,7 @@ def test_get_model_anthropic_with_thinking():
 
     model_config = ModelConfig(
         provider="anthropic",
-        model="claude-3-5-sonnet-20241022",
+        name="claude-3-5-sonnet-20241022",
         enable_thinking=True,
     )
     result = get_model(model_config)
@@ -202,7 +200,7 @@ def test_get_model_gemini():
     """Test get_model returns GoogleModel for Gemini."""
     from pydantic_ai.models.google import GoogleModel
 
-    model_config = ModelConfig(provider="gemini", model="gemini-2.0-flash-exp")
+    model_config = ModelConfig(provider="gemini", name="gemini-2.0-flash-exp")
     result = get_model(model_config)
     assert isinstance(result, GoogleModel)
 
@@ -213,7 +211,7 @@ def test_get_model_gemini_with_thinking():
     from pydantic_ai.models.google import GoogleModel
 
     model_config = ModelConfig(
-        provider="gemini", model="gemini-2.0-flash-thinking-exp", enable_thinking=True
+        provider="gemini", name="gemini-2.0-flash-thinking-exp", enable_thinking=True
     )
     result = get_model(model_config)
     assert isinstance(result, GoogleModel)
@@ -224,7 +222,7 @@ def test_get_model_groq():
     """Test get_model returns GroqModel for Groq."""
     from pydantic_ai.models.groq import GroqModel
 
-    model_config = ModelConfig(provider="groq", model="llama-3.3-70b-versatile")
+    model_config = ModelConfig(provider="groq", name="llama-3.3-70b-versatile")
     result = get_model(model_config)
     assert isinstance(result, GroqModel)
 
@@ -235,7 +233,7 @@ def test_get_model_groq_with_thinking():
     from pydantic_ai.models.groq import GroqModel
 
     model_config = ModelConfig(
-        provider="groq", model="llama-3.3-70b-versatile", enable_thinking=False
+        provider="groq", name="llama-3.3-70b-versatile", enable_thinking=False
     )
     result = get_model(model_config)
     assert isinstance(result, GroqModel)
@@ -247,7 +245,7 @@ def test_get_model_bedrock():
     from pydantic_ai.models.bedrock import BedrockConverseModel
 
     model_config = ModelConfig(
-        provider="bedrock", model="anthropic.claude-3-5-sonnet-20241022-v2:0"
+        provider="bedrock", name="anthropic.claude-3-5-sonnet-20241022-v2:0"
     )
     result = get_model(model_config)
     assert isinstance(result, BedrockConverseModel)
@@ -260,7 +258,7 @@ def test_get_model_bedrock_with_thinking():
 
     model_config = ModelConfig(
         provider="bedrock",
-        model="anthropic.claude-3-5-sonnet-20241022-v2:0",
+        name="anthropic.claude-3-5-sonnet-20241022-v2:0",
         enable_thinking=True,
     )
     result = get_model(model_config)
@@ -269,21 +267,21 @@ def test_get_model_bedrock_with_thinking():
 
 def test_get_model_vllm():
     """Test get_model returns OpenAIChatModel for vLLM."""
-    model_config = ModelConfig(provider="vllm", model="Qwen/Qwen3-4B")
+    model_config = ModelConfig(provider="vllm", name="Qwen/Qwen3-4B")
     result = get_model(model_config)
     assert isinstance(result, OpenAIChatModel)
 
 
 def test_get_model_vllm_with_thinking():
     """Test get_model configures thinking for gpt-oss on vLLM."""
-    model_config = ModelConfig(provider="vllm", model="gpt-oss", enable_thinking=False)
+    model_config = ModelConfig(provider="vllm", name="gpt-oss", enable_thinking=False)
     result = get_model(model_config)
     assert isinstance(result, OpenAIChatModel)
 
 
 def test_get_model_unknown_provider():
     """Test get_model returns string format for unknown providers."""
-    model_config = ModelConfig(provider="mistral", model="mistral-large-latest")
+    model_config = ModelConfig(provider="mistral", name="mistral-large-latest")
     result = get_model(model_config)
     assert isinstance(result, str)
     assert result == "mistral:mistral-large-latest"
@@ -293,7 +291,7 @@ def test_get_model_with_all_settings():
     """Test get_model applies all settings together."""
     model_config = ModelConfig(
         provider="openai",
-        model="gpt-4o",
+        name="gpt-4o",
         enable_thinking=False,
         temperature=0.7,
         max_tokens=500,